{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29f7a85e",
   "metadata": {},
   "source": [
    "# Создание многоклассового классификатора изображений по типу эмоций:\n",
    "**Имеются 7 типов эмоций** <br>\n",
    "\n",
    "## Дано:\n",
    "В архиве \"all.zip\" лежит папка all, в которой массив фотографий людей, показывающих различные виды эмоций (для самостоятельного запуска кода архив необходимо скачать по ссылке и после разархивации папку all положить в репозитоий проекта)<br>\n",
    "В репозитории уже имеется файл _label_dataset.csv_, в котором находится код соответсвующей эмоции для каждого номера фотографии из папки \"all\", которая появляется в текущей директории проекта при запуске кода: <br>\n",
    "0 - neutral,<br>\n",
    "1 - anger,<br>\n",
    "2 - contempt,<br>\n",
    "3 - disgust,<br>\n",
    "4 - fear,<br>\n",
    "5 - happiness,<br>\n",
    "6- surprise<br>\n",
    "\n",
    "\n",
    "Загрузить архив _all.zip_ с фотографиями можно по данной ссылке. После загрузки и разархивации папку _all_ надо положить в репозитоий проекта, где уже лежат файлы _emotion_class_binar.ipynb_, _emotion_class_multy.ipynb_  и _label_dataset.csv_\n",
    "<a href=\"https://drive.google.com/file/d/1fdDs9fHt79Lkoo-HRzIsMdwugeQ96Nwg/view?usp=share_link\" target=\"blank\"><img align=\"center\" src=\"http://cdn.onlinewebfonts.com/svg/img_317646.png\" alt=\"kolesnokov__dima\" height=\"50\" width=\"50\" /> </a> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d81d9a",
   "metadata": {},
   "source": [
    "Работу выполнил **Колесников Дмитрий**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af205347",
   "metadata": {},
   "source": [
    "Загрузим необходимые библиотеки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd06e64d-1e8c-4e9a-80d5-695f98dbf082",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "from  matplotlib import pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "from tqdm import tqdm\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "import PIL.Image\n",
    "import PIL.ImageDraw\n",
    "\n",
    "import collections\n",
    "from matplotlib import pyplot\n",
    "import pylab as pl\n",
    "import glob\n",
    "import zipfile\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import precision_score, \\\n",
    "    recall_score, confusion_matrix, classification_report, \\\n",
    "    accuracy_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4f6fa7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install face_recognition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f0a5405",
   "metadata": {},
   "outputs": [],
   "source": [
    "import face_recognition as fr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bf3882b",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d564ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_im = os.listdir('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440e92e0",
   "metadata": {},
   "source": [
    "Загрузим датафрейм и посмотрим его содержимое:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43fc0b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('label_dataset.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "097fbf37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(920, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68141ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>file name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>S010_004_00000019.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>S011_004_00000021.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>S014_003_00000030.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>S022_005_00000032.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>S026_003_00000015.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label              file name\n",
       "0      1  S010_004_00000019.png\n",
       "1      1  S011_004_00000021.png\n",
       "2      1  S014_003_00000030.png\n",
       "3      1  S022_005_00000032.png\n",
       "4      1  S026_003_00000015.png"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ce43828",
   "metadata": {},
   "outputs": [],
   "source": [
    "dirName = 'all'\n",
    "allFiles = list()\n",
    "for entry in list_im:\n",
    "    fullPath = os.path.join(dirName, entry)\n",
    "    if os.path.isdir(fullPath):\n",
    "        allFiles = allFiles + getListOfFiles(fullPath)\n",
    "    else:\n",
    "        allFiles.append(fullPath)             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14952c02",
   "metadata": {},
   "source": [
    "Получим список фотографий для каждого номера эмоции:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd736a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[S005_001_00000001.png, S010_001_00000001.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[S010_004_00000019.png, S011_004_00000021.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[S138_008_00000009.png, S139_002_00000013.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[S005_001_00000011.png, S011_005_00000020.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>[S011_003_00000014.png, S032_004_00000014.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>[S010_006_00000015.png, S011_006_00000013.png,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>[S010_002_00000014.png, S011_001_00000016.png,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category                                               file\n",
       "0         0  [S005_001_00000001.png, S010_001_00000001.png,...\n",
       "1         1  [S010_004_00000019.png, S011_004_00000021.png,...\n",
       "2         2  [S138_008_00000009.png, S139_002_00000013.png,...\n",
       "3         3  [S005_001_00000011.png, S011_005_00000020.png,...\n",
       "4         4  [S011_003_00000014.png, S032_004_00000014.png,...\n",
       "5         5  [S010_006_00000015.png, S011_006_00000013.png,...\n",
       "6         6  [S010_002_00000014.png, S011_001_00000016.png,..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.groupby('label', as_index = False)['file name'].\\\n",
    "    apply(np.asarray).to_frame().reset_index().\\\n",
    "    rename(columns={\"index\": \"category\", 'file name': 'file'})\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c8837f",
   "metadata": {},
   "source": [
    "Продемонстрируем как пример виды эмоций по имеющимся фото из папки all:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc0aa4f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S005_001_00000001.png\n",
      "S010_004_00000019.png\n",
      "S138_008_00000009.png\n",
      "S005_001_00000011.png\n",
      "S011_003_00000014.png\n",
      "S010_006_00000015.png\n",
      "S010_002_00000014.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x360 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['0- neutral', '1- anger', '2- contempt', '3- disgust',\n",
    "         '4- fear', '5- happiness', '6- surprise']\n",
    "fig, axes = plt.subplots(1, 7, figsize=(19, 5))\n",
    "for i in range(df_new.shape[0]):\n",
    "    print(df_new.file[i][0])\n",
    "    image = plt.imread(os.path.join(\"all\", str(df_new.file[i][1])))\n",
    "    axes[i].imshow(image, cmap='gray')\n",
    "    axes[i].set_title(str(names[i]) + \" class\")\n",
    "    axes[i].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c57c0e6",
   "metadata": {},
   "source": [
    "## Предобработка изображений"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0f250e",
   "metadata": {},
   "source": [
    "Визуализируем применение фильтра Гаусса к одному изображению (до/после)\n",
    "Визуализируем применение эквализации гистограммы к одному изображению (до/после)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6d6811f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_files_list = allFiles\n",
    "image_1 = cv2.imread(img_files_list[10])\n",
    "image_1 = cv2.cvtColor(image_1, cv2.COLOR_BGR2GRAY); \n",
    "image_GF_1 = gaussian_filter(image_1, 7)\n",
    "# эквализация гистограммы\n",
    "clahe_1 = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) \n",
    "image_CLAHE_1 = clahe_1.apply(image_1)\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.imshow(image_1, cmap='gray')\n",
    "plt.title('Original')\n",
    "plt.axis('off')   \n",
    "plt.subplot(2, 3, 2)\n",
    "plt.imshow(image_GF_1, cmap='gray')\n",
    "plt.title('Gaused')\n",
    "plt.axis('off')   \n",
    "plt.subplot(2, 3, 3)\n",
    "plt.imshow(image_CLAHE_1, cmap='gray')\n",
    "plt.title('Equalazed')\n",
    "plt.axis('off')\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.title(\"Histogram Original\")\n",
    "hist_1 = cv2.calcHist([image_1], [0],\n",
    "                      None, [256], [0, 256])\n",
    "plt.plot(hist_1, color='black')\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.title(\"Histogram Gaussed\")\n",
    "hist_1 = cv2.calcHist([image_GF_1], [0],\n",
    "                      None, [256], [0, 256])\n",
    "plt.plot(hist_1, color='black')\n",
    "plt.subplot(2, 3, 6)\n",
    "plt.title(\"Histogram Equalazed\")\n",
    "hist_1 = cv2.calcHist([image_CLAHE_1], [0],\n",
    "                      None, [256], [0, 256])\n",
    "plt.plot(hist_1, color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36171931",
   "metadata": {},
   "source": [
    "Сделаем функцию предпроцессинга, которая с помощью модуля face_recognition, реализующий детектирование лица на изображениях по гистограмме градиентов, позволит сделать вырезание из исходных фоток лишь область лиц с заданным размером на выходе. В процессе преодобработки применеим фильтрацию гаусса и эквализацию гитограммы исодной фотки для убирания артефактов возможной засветки"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13993399",
   "metadata": {},
   "source": [
    "То есть на вход подается массив необработанных фоток некого размера а на выходе получается массив фоток размером 64 на 64, состоящий исключительно из вырезанных областей лиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f0ecb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ImagePreProcessing(data, sigma_GF, IMG_HEIGHT, IMG_WIDTH):\n",
    "    X = []\n",
    "    # При помощи флагов выведу лишь определенные фотки в качестве пояснения к работе кода\n",
    "    flag = 0\n",
    "    for image in data:\n",
    "        image = cv2.imread(image) \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY); \n",
    "\n",
    "        if flag in [0, 10, 20]:\n",
    "            plt.figure(figsize=(8, 8))\n",
    "            plt.subplot(2, 2, 1)\n",
    "            plt.imshow(image, cmap='gray')\n",
    "            plt.title('Original')\n",
    "            plt.subplot(2,2,3)\n",
    "            plt.title(\"Histogram Original\")\n",
    "            hist_1 = cv2.calcHist([image], [0],\n",
    "                                  None, [256], [0, 256])\n",
    "            plt.plot(hist_1, color='black')\n",
    "\n",
    "        image_GF = gaussian_filter(image, sigma_GF)\n",
    "        \n",
    "        # Face detection + crop\n",
    "        face_loc = fr.face_locations(img=image_GF, model='hog')\n",
    "        if len(face_loc)>1:\n",
    "            face_loc=[(face_loc[0])]\n",
    "\n",
    "        a = collections.deque(*face_loc)\n",
    "        a.rotate(1)\n",
    "        shifted = list(a)\n",
    "        img = PIL.Image.fromarray(image_GF)\n",
    "        img2 = img.crop((shifted))\n",
    "        image_CROPPED = np.array(img2)\n",
    "\n",
    "        # выполните эквализацию гистограммы с cv2.createCLAHE\n",
    "        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
    "        image_CLAHE = clahe.apply(image_CROPPED)\n",
    "\n",
    "        # Scale\n",
    "        image_SCALED = PIL.Image.fromarray(image_CLAHE).\\\n",
    "            resize((IMG_HEIGHT, IMG_WIDTH))\n",
    "\n",
    "        X.append(np.array(image_SCALED))\n",
    "        if flag in [0, 10, 20]:\n",
    "            plt.subplot(2, 2, 2)\n",
    "            plt.imshow(image_SCALED, cmap='gray')\n",
    "\n",
    "            plt.title('Cropped')\n",
    "            plt.subplot(2, 2, 4)\n",
    "            plt.title(\"Histogram Cropped\")\n",
    "            hist_1 = cv2.calcHist([ np.array(image_SCALED)], [0],\n",
    "                                  None, [256], [0, 256])\n",
    "            plt.plot(hist_1, color='black')\n",
    "            plt.show()\n",
    "\n",
    "        flag = flag + 1  \n",
    "    X = np.array(X)  \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e71c2bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jgVartemgikajgYWFBayurl5Q+Lfbbayurm7q3PV6HSsrK+v2X11dxfLycsSqaLVam1I8rLWo1WpoNptYWVlZF9jCutIXIaTceavVKhqNxrrzfivgPanVahfdp82ct1qtuvPW6/XIHHIMfK7j5pvCfRM4cck6HxAQcFXiahHmXwVwkzHmegBnALwRnSIiiRgaGsLLXhaY+DhYa7G6uoqFhQWcP38ei4uLWF1dhbUW7XYbfX19KBQK6OvrQ7vdhrXW/RljYIxBKpVaFwHMP36mAtHf3x85tq+vz/33z8PrKNgHpcRUmKmApnLBfbVPwPooWoU/BmCNhqPC4q9pTYG5f/9+3H333bj11lsTKT1fSdK52iokCXNe01qLX/iFX9iy628FtO/+to3a/vwnUbtKXe/YscO1b731Vte+4YYbXFuj1pV2TaLZ/epx+p0qbBodnUTbJlVwU7ZR6W1f6UxavzuJEtfqbLqPjkn3134kuQSA6L0pl8uurXOgfVVXg7a1T0r3J7k59Jz+9bSPekxS9LyOVedZ791mjJqtwlUhzK21K8aYfwvg79BJTfvjboWxRNx000342Mc+dln612uoVCp47LHH8OCDD+Jzn/scnn32WTSbTfeD6u/vRyqVckKUQovfUxirIGZ7dXU10ibT4KeepFIpZDIZdy0KRb0mBSmZCf6gVHiTxQA6LwQKclUUfL8YsPaD1DGoMOdx2jf2j9f399u1axe+7/u+DwcOHEA6nUZfXx8ymYybSzI3q6ur6OvrQ39/v+sn+3SpBDvngvOp0DH3ojAPCAi4eFwVwhwArLUfBfDRze5vjNl0oMm1hHK5jCeeeAIf+9jH8OCDD+LMmTNOWAKdFz0FDoW3b0GqhU2rG4ATdtQ+qQCokKbAotVPxcC3/inkWq2W8w2rUPQVBj2/KiLtdtsdq+cHOkqJumEoVHlu9ddzXwpzFZapVArVahWf/vSnsW/fPtx7773Ol07lhq6DdDqNTCazbtwAHDui7MVzQbvdRrPZRLPZXHdOVVACAgKuHfSsNPRf3AEd//mhQ4fwiU98Ap/5zGdw4sQJV9yA80RBTAFDgcV91OqlYFVq2l8/Os6K1e8oDH1LkkpCo9FAs9l0ihkFKvsGIGKB6/l9gatWN89HocfzKDgXDAzkNu0nlZdWq4VvfvObuP/++7Fjxw687GUvw+rqqot76O/vd1QkYw3iXAErKytIpVIRxoLjiXNv6Fyzv7wvFObKLuh8+ferV6D3mEiiPuPiDDY6JxAtHHLTTTe59oEDB1xb6XddREXbujiHGhb+YifqltE+Ji20oiyXUu66KImeR6PRNzsHOm9KRSvNnhTZnrS4ivbDdzXoMRpVr+fdt88twoj9+9dCqHSeN/NMKI2vhWGAaDS79kkpd72e3kt1q2hWg7o/lMa/3OhZYQ5cWtqy19FqtXD48GF85jOfwWc/+1kcPXrUUet+Gov6iWnZ0XK11jqrWoWQ+qkJzj9/VOl0Gul0Gqurq2g0GpFzU8DxR0clgAKJdLrvu1YLVhkB37++srIScQ+oEFbr1R8T/1Np8YU5r8dzPf744/jrv/5r7Ny5EwcOHHDCmf5Cn/aOcwNoEKSyIWQH/GdamRQqGK1Wy42NypcqaHECcSthjPljAK8FMGOtfUF32xg6i+YcRKfM7xustVfubRcQsI3Rs8I8LpDqWkWz2cTZs2fx5S9/GQ899BAOHz6MWq0WEUxq9SktTvA7CiVGS1NIqPUeR8vzfz6fd+emsKHAVoGkAnej++j3M86CpSWjDACvqda9KgiqNDSbTWf1qztAnzH+r1QqeOihh3DgwAG8/vWvx9jYmBO2zWZzncKgglwD6lQx0f38QETOgcYlqILmMxL+uS6jsvsnAH4XwJ/KtncCeNBae58x5p3dz++4XB0KCLiW0LPCPFjkHaysrODUqVN49NFH8bnPfQ5PPfWUy99WYakCV33NcfQZBa9axbQaFXHH9/f3OwqZ+fOqVFAI+XR/ElSRUMRZ8Cr4fUaC49Vr+5HQSULcf9ZKpRI+/vGPY3h4GPfeey/y+Tz6+vocJadzoMxFXP91jn0GRfurSpmvhPkCXRmFy/U7sdZ+1hhz0Nv8OgCv7LbfB+AhXECYW2sdtazUqX8vLxZKnSqFq9Su0t5a01vXJ0/KZNBIZ7+ISFLdcKWDlf7V7Ro1neRSUGrcf9Y0wlz7q24B7btSxnpsUpS2Xi8pwh6I0tU6H+rOUGpe6epnn33WtZm+CkQp9HPnzrm2zpn//Ou90X5ooZikQji6ZrqeR90GOgd+JP1WG589K8yB9T7Qaw0rKys4cuQIHn74YTz88MN47LHHMDs7u84KBtaoaAAR4cGXP3+4KsxUwPE432rUY/iSHRwcxMDAADKZTOSB9q3czTzcSfv42/18+DjFI+m7uM9xvjmdtyNHjuCBBx7Azp078ZKXvMSxAsoS8FhVpHz6O+na/pz7Y1OlhdfTc2p8wRXETmvtVLdPU8aYHXE7mWgxqMvYvYCA7YOeFubXOqampvCZz3wGn/nMZ/D1r3/dBbz5L3oKBH3hExToKvh9f7Qv4HmMpl7xGqSDM5mMixJX+jtOyF4qjTXpPGq1+pH1hJ+GF+f7VsHaaDRw9OhRfOlLX8Lznve8iGbPeaBA1fnUVDuyHT4V77d9ZSJprD713ituKCvFoPr6+nqj0wEBVxl6VphfTgrxakSj0cDx48fxzDPP4PDhw5iamoIxBplMZh0lG2cR+wFh+p1PwSuFTb+6f6wGXdF/zFxznpdBZNqXrYYvEH1Fh4hTNBRxMRrLy8v46le/igMHDuCee+7B3r17Y33kPF59/fxLKn6TFNHO7/Qecm55Dr1/V/g3Mm2M2d21yncDmLnQAYx3AC7+GfH31whlpc2V2lXqVKOVta33VKldLRaiVKvS5AASSxMnUdd6PY1yT1rCVBGnrMd9p3OTVMM+yXWg/dbtuiyoXzRG3RNK32v/NFpf21q/Xd0RyvppPzZaIldpc3+pWkLnQN0z+gzpfdm9e3fstfX5AKLPwVb8Lq84Dxfw3DA/P49nnnkGx44dw8zMDOr1ugs484PK4gQ6LUYVzPo9c8w1UI0R6Ur7qpAH1gR5tVpFrVZzliitdP86wNYqZnGsQpzPNU5wXKhP7XYbS0tLmJqawsLCAur1eiRGwLf8Oaf9/f2RedCAOJ9i94veaJAe74tmDdDS7+vrixSsuUL4CIA3d9tvBvDAFexLQMC2Rs9a5tcaVGBWq1UcPXoUx48fx5kzZ1wAjNLEuj8FMhFHOwPraXkVMCpIeE4eQ5+7MWslXrkQi+7Daml+zXc/0OtyIk65IC5kGVprMT09jePHj6NUKqFcLrtAn0wm48aTzWYjFrl/Dm4jNe/fxzi/uiolVA7ImlwJAW6M+XN0gt0mjDGnAfw6gPsAfMgY81YAJwG8/rJ3LCDgGkEQ5j2IUqmE48eP4+TJk5ifn3c52xqB7tPlvmBQAe0LECBKpV3I1+wXb+H5SfnrAjX0E6sVqddgoZnLhedC96sC0Gw2cfjwYZw8eRL79+9396BQKKBQKESUGr2eT5knsQC+IhYXu8B51yBGLaRzOWCtfVPCV/delg5gPZOiVKgWikmKTtf99Vxnz66t+aTLYB46dMi1Z2dnXduPZlf6OWnhpySqW2neJJeARl/71HFSnIXup8crPa776zOodHFSFL6OGYhS4uqS0Kj1+fn52GskLX+r0O0aOa9FbYDofGrNd91P+5oUPa/PU9Jz5s+B3vuLXQBsMwjCvEfAH2Wj0cDy8jJKpRJmZmawvLwc+cGyeEpcVLRfCEUFvlZZ02v6vlkFj/evo5Y909VodQJRBoDX7O/vdwLIp44vNzay1rldx3nq1Ck8/vjjeNGLXoTx8fGIwOaPdmBgwMUe0L/tV4Hzy7H6L2KdY73HGmR3lfjKAwICLjOCMO8xcDU0Uuxad11f4hSSKqyVfveFMAWvRkTHnROIj3DX8zYaDfT19TnFgmyB9iWTyUQo+/7+/oivP06QaW75Vgn5JJ/+Rj7+lZUVPPnkk3jiiSewc+dOJ7j5HZFOp925fEbE7wO/Y9Cgn0Pv98NnYr6V2u8BAQG9hyDMewiNRgPnzp3D008/jcOHD2Nubi62/KgvbIH4QhsqsJMKj8QJTr2e0vU8FwV4f3+/S1FLpVKuKhuv7Vv8tFQZbOf71LUfFPyX2s/uKxM6Nn+OfSr20KFDuPXWW7F3714MDQ0hl8tFLPE4twahzEjc4jR6PR03lSRa9ao89aIwT+p3kvKmTJJfD1xpaY1EVipZ6VUtNvKVr3zFtZ96am0BR6WFlYJVStXvq36XRLnrM6xjSopGV5pcx6mR+kB0yVY9Rrdrn5QyTlr2VPdXyrxUKrm2UuZANDpd51Cpeb1GEg2tfUpyO2iaqN53/7O6F/R6SVHySUV+9L5on/xsBb2GUvaXyjAJwryHsLq6imq1ivn5eczPz6Ner68T2nE+bX05qtCO81urz9un5X0BowLPp3fjjuWL0/d7UTD7dLFChSEAl8/OYDpdNvVSwBfWuhCMLi6j0f5HjhzB6dOnXYUxLpHKRVFarZZb1EXdEHG12/0Yh7hgOI1z8O9rLwrygICA544gzHsE7XYblUoFy8vLmJ2dxfLy8joLli9xDYQifGGo0dAaQOXnKPtUul7Lp+L90pGk3JvNZqS4TJw/mJZmKpVydLR+z3PSel9ZWUGz2XRFctj3S22l+8FmvvDlWFZXV/Hss8/im9/8Jl70ohdhcXERAwMDLgCOWvrq6ioqlQrS6TSy2ey6fNy4WAfOpX4mgwEgcs904ZerpApcQEDAZUAQ5j2CZrOJSqWCubk5nDx5EnNzc85C1MjlOJrSt5jVUvaFhR/UpufyhSV937raF/OoCd+3rufSQDKlhulDj1NMlJ5XQcUce577UsGfTz9gkAIU6NTIfuihh3D77bfj277t27C0tBRhP8hMWGtdQQ7N59f5UuXJTzWkAqcuCzIEfpBhL2MzdLPu4ytGGtWsFLM+n0r/KrWuhUr0/isNrdHQSqEqXQ8k1+tWClfbenxSW8+jkd9+wRql3ZV+TlLydBxJS7cqzZ40Nr8fSsf70f6EGgPaTopUV8p87969rr1nzx7X1nvkI6m4jF5P3TBK5aubImndAP951PuXRN9/KwjCvAdAK3RwcBD9/f1oNBqROusa2aw0q1rWhPpdlZr2g9vY9v3n3Jf+cOaOq9WqlqHCF+qqfPgFZbicapxw0uVDGTjH89FivVRWetz8aRAbhTGveebMGXz1q1/FLbfcgkKhgEqlgkKhgL6+PqdwcN6MMWi1Wu4ccYyKf01/O0Elg8f7TExAQMD2RhDmVzmYp10ul1Eul3Hs2DGcPXsWrVbLWWx+oBiwvmypWuBx1cnirGdfqKvVTOFDC59WPoV4f3//OrrYPxevzWOpXJCeVmtVA944dgDuOnFj3gr4yo6fRtZsNnH+/HmUy2Xs3LkTrVYL1WoV6XQarVYLuVwOmUwm4oPnWuo+LU4FQVP19B5yXn2fuR/7EBAQsP0RhPlVDvqHW60WlpaWcPr0aUexx/m2/cA0wo+MJuIsvqQgOqXR6fMdGBiILPmplLHSWBqhDSBS1lRpegaNqWDTnPOVlRVUKpWIEOM64tr/pLF8q/Cvo7Xm+Tc3N4fFxUX09fVFItoZac7PymT4wW68lu8y8ffh3PpKhdLzvYokal3nZKNodq1lrnSp0poaWa3Rx1r8I4lqTapv7i//6StohEbDax1vjQRX6lppWv1tbVRURWlwjcDWviulrX33Y0PioNuT6qP719P+JrWTrqH9U7pf50z77VPYeryOW5FkDCTtn3R+jZYHgF27drl2Uk17vV8XG8QahPlVjna7jVqthmKxiFKp5AQZg6r8qHFgfdUwYD3FrYgT6PodhSzpYFricSlofPHpqmEDAwPrfPb5fN5Z9356HQU9hT2tcQr8bDa7LqhOx8g58AvQfKvwrwWsVbTjy6vdbuPkyZM4dOgQbrvtNtdX7l+tVrGysuKWiNX596vu6Zi0//SZ+zENqsQFej0g4NpCEOZXOVqtFgYGBlCv1/HII4/gG9/4Bubm5lwhESC6zjaFQlwkukKFRZIm6tPm/CNN3N/fj4GBAeRyOQwMDETodV3ek98BHUHE4+gTV+HU19eHbDbrNFwKeY201yh+ZQwGBgbcOH0t/1IIdA0wA+CUGZ1voGPtPfnkk3jZy17mAqaUTqdrQu+PKkyk0vXeaZCiKhX8T8vfd2P0smUeEBCweQRhfhWj3W67iOhnn30WX/va13Dq1Ck0Go11Alyhn/1Kb3H7APHWnNLm/E9LnLXHc7lcZKEPWuFaq90vhAJ0qEsGgWkQGy1/KgXsP+u8V6vVdWl1FPQUcnFLtAKXTqBfSFC2Wi089thjeOqpp3DjjTeiUCigVquty/nnuXSccQyKCvQkt0Ec1b5RH69mqCIXBx2fUuka6QxEC3goVa5zolHJet6koiV6PaVd9fw+HZsUCe5XByT8bBBCafYk6tmn+HUOlcLVCHilg/V4v655XD+SqHU/Yl2v7Qezxu2TFGmu/eYCU0DUTaH18/350Pun7c3Uulc3TtKzqfto5gMQjazX6H7tu1/P/WIQhPlVjGaz6Wqvz87O4ty5c+4hSLK643zoG1HofpsCXauJqU88m80il8u5HGmmptEy5nHsG634drvt8sIp9NViB6LBenGUNoU4A+VUyaACQX87LVW+FGg9xwUAXix8ZYl91u1zc3N45JFH8PKXvxw33nijU1jUbcA23Q2k4zmHm4HOlZ8ZEOdzDwgI2J7YEmFujPljAK8FMGOtfUF32xiAvwBwEMBxAG+w1i50v3sXgLcCWAXwc9bav9uKfvUayuUyWq0WGo0Gjh8/jrNnz6JWq7kAKj8ffKOAqSRooJkKUFLgfX19zheu6WgU0gzyIm2uVhWFip5fc6bVSo+LwFZfMqu9tdttd91Wq4VWq+X89xTu9Xrd7c8x6Dk0P/9bEeoMQoxDq9XC448/jq9//eu48cYbkc1m3f6a0qfjZOYCGQtu1xxypd/1/vN7ZUkCAgKuHWxVlMyfAHiNt+2dAB601t4E4MHuZxhjbgPwRgC3d4/5fWPM5sySbY5MJoORkRFYa3H27Nl1VjkQTU3id3zRbyZFizS3H9DGc6pvXC3sdruNdDqNXC6HfD7vjle/Ov9U0Ov+wJrfWfdVfzqPofKQyWSQz+fdebjMqFZUo2Anlc9gMwbpadtfuexisZEyMDs7iyeeeAJHjx5FvV6PWOCk0zg2jTzX4D/eX1rqFPi61jzvM+eS+22HwjEBAQGbw5ZY5tbazxpjDnqbXwfgld32+wA8BOAd3e0ftNY2ADxrjDkC4G4AX9yKvl3N0OhkBkytrKzg+PHjOHz4MJaWliJrhANr1jSP1UIwF7I6/TQmFR48t/rMKYDz+bxbSIQCXiu/6XE8b6vVcvuyrxRSmnftCyaOTdPYyEpQaK+srLi+kYb3rXqen1S7Ut5qGV+sANwo2Kxer+PEiROYmprCrl27nCD3U+9orXPueC72X3PNyTZwf9+NwvnV56LXcKF74AchEn4qkH72/ZdEUrpX0vWS1qTWhTN0wREgec3upEpoSX5k7WtS5Ts/PS8pBU39xYqkhUw0oFRT3JLmz6+Cp2NNSrfbjDswqSKeYqNnPs61CETjA3Ru1M+dtGiNPlu6Xro/x+q/17nVWA/1pV8sLqfPfKe1dgoArLVTxhjWGdwL4Euy3+nutmsO+nCtrq6iVqvhyJEj+Pu//3s8+eSTqFQq7kflB/To6mEXeokrJU/hpsJVK7oxwpx+cqDzUGazWRQKBfeAKu2ugti3OIG1lzUFGpUT9f/yP/3LLBTj138HOj8aMgbsf6PRcNavKghEUurec0HScdZa1Go11Go1zM7OIp/Po6+vD6OjoxgYGEC73aldn81m141L6XMyIYyCp3IUJ/TU5eIXoQkICNi+uBoC4OL4zdi3ozHmbQDeBgDXXXfdVvbpimNpaQmHDh3CZz7zGXzhC1/A9PR0xOr2o9TVd+7nH/N7hVqjACJCl5/pL89kMq5AzOrqamQbBQepbj8Ii8KEjIKfG68WpFrz7DPPQQtDfeEAnMVOq0DZAC3zSmpahZwqNDzfxfrRdRzaZ7ZPnjyJZ599Fnv37sXg4CCstVhYWHCuBLWE/GI7FORUqHyrXZU23suNIqkDAgK2Ly6nMJ82xuzuWuW7AXAlg9MA9st++wCcjTuBtfa9AN4LAHfddde2fktVq1WcOHECzzzzDM6fP+9e6nEvZz8YCohfMc2nY/1t1lpHSfuCXCPEBwcHMTg4GKmdzuuor5oKAoUv6Xi/OIoGcKnQ8hUOjfT2A78oALUC3erqqrPWebyyGL5/+bkIvrjAQ53XUqmEM2fOoFaroVKpYGhoyFnoVEKWl5cjypO6BKissP9a1pXzofdcWY04pa4XoPebSEoDU1rTX1RDq7gp5a6V15IWO0mi07WtlLku2KIpU/55N2JxLrSP3svNpJwBUdpcz6s0bxKtrPOnv3Nt6/U2ooiTqpwp1CWgfVJaP8kFo2NTOvzAgQOR/XQBHb1nel+T1qnX+6jGpNLsSWltPpLWXNf+Xez76HIK848AeDOA+7r/H5Dtf2aMeQ+APQBuAvCV2DNcI5ibm8OxY8ecwNGXtuY5M7rbz1EG1pcCjHsw/H2Vah8YGEA+n3dFYfgDHhgYcAu+qCDLZDLI5XKRyHL2TX1F/NEoFU9/MP3nKrw0n5zCSXPQyQKw0Iz6x5n2pUItTokh4+FH0V8M4rIKVldX0Wg0MDMzg1qt5iLVh4eH182fX/2OwksZBf8++oqEukh8yz0gIGB7Y6tS0/4cnWC3CWPMaQC/jo4Q/5Ax5q0ATgJ4PQBYa58yxnwIwDcArAD4GWttfJHeawCtVsu9/Ofn551VB0SDm4CohqyWqi9YNguNXmc6GoUzo9dpVTLQjEI8n89HcsqBaCEaLTGr//m9XkstUB7La/qV4dT1QOFprUU2m3X0tJ5La6lzrlZWVtal+m0WOtf8zLGzz6dPn0apVMLNN9+MXC7nIts5X6qY+HETqqT5Uffa9uMdghAPCLi2sFXR7G9K+OrehP3fDeDdW9GXXkO1WsXy8jLOnTuHo0ePYnZ2Fo1GI1JRjYizvn26zhc0fi43ofnkXLVM/dHpdBpDQ0OOtqOFnM1mMTw87FLX4ny2jMqngNMVw3yBpH3z/dr6ncYONJtN96dCmv2kZasWOBkCFZbKUFysUPfHo66No0eP4uGHH8btt9+OsbExF9Cmiola0uoK4H91PSjrofOkSpT2o9egYyWSoo2VotSFLIDoutZ6vNK8Sg1rlLVS5UkLs2hbI6v9dax3797t2krN67V9xZxQmvzGG290baW3T58+7dp+RH5SxTmF0sE6n9pvpY937tzp2sq6aSUz/7lLWp9cobS+XkPHp/OXhP3717y2/jOhVLk+RzoOvX/a1jnQudE145Vy92l2nRO9x+ra0OslZTsk4WoIgAvootVqYXZ2FrOzs1hcXES9XnfCkFBhpfnkccLHD5AC1vucGCFNXy2tOwo8UthDQ0POT04hzj+NYNd1yfW6vLb/Q/b95VQS2DcNWFP2QS1ztW5V2eDcUYnJZDLOCtc0OUbKJ63atBnEzTP722g08NWvfhXf+Z3fiRtuuAGrq6tYXl5GtVpFoVCIlK+lIOdc6P3gGDkGjkvvKbf5wYRbCWPMfgB/CmAXgDaA91prf8dsUCgqICDg0iII86sItVoNZ8+exezsLFKpTulUrcPOFz0R5z9VS9P/Pk7Ya9CVWnMU2ENDQxgeHsbg4KCz1lmEReur6/V4LvV562elxWklURHQCnHaF402bzabqNVqqNfrEQWClLpa9Ol02m2j+4CWsQbr0Td9KQqtxClWpVIJJ0+eRKlUwsrKChYXFzEyMoLBwUHk8/l1UfZMyeP91qA+/cx54pz7dPtlssxXAPyitfZRY8wQgEeMMZ8E8BZ0CkXdZ4x5JzqFot5xOToUEHCtIQjzqwTWdtbBnpmZQS6XQ6VSwdzcHMrlsrMafTpYI5njXtq+tRj3vV+8hIFvhUIBIyMjGBkZcWln/KMg1/QzvT6pdLUmlTamNUzBNDAwEFtP3LfMtZwp9/FT3VThYR/42bdyyXzQf+1HtF9Kv/PS0hIOHz6M48eP44YbbkCxWHQV6+gvV785x6gxBzo3ysj4TIjGS1yOKnC2Uz+CNSSWjTGH0KkVkVQo6kLni/wHkhdX0ajsgwcPRs6jNLvS0kmFTpQ214hm3a7Up9KoGlWvi3YAwC233OLaShkfPnw4th86Vo3GvuOOO1z71KlTrq1j08hvABgfH4/tl0Z8K2Wsc6jzp/SvjlVdBUppa1Q2sL6YDaFzqMVT9Hq33nqra+s8Ja1foHS4z7YpJa6/DaW6dR91O2hb+7rZNeCTitzoPdL58DMTLoQgzK8SVCoVHDp0CM1mE7t378bc3JyrJJXJZFCv1yMvd2D94ihA8ippSUFRLNiSSqVcYZhcLuesceZDaw45EPU10/Llj4s+al7TL0dK3zXpZC02QwGk1DtT4HjM6uoqcrkcjDGo1WpoNptOSdA2x81r6/moMC0tLaFWqyGdTkeEalx61LeCRqPhagUMDg6iWq06AZ1UUlZLtuq8q5tB7yOfD87TZbTMHUyn8uOLAXwZyYWi/GNc/YiAgIDnhiDMrwJY26m9Xi6XMTk5if7+fpTLZVSr1YhlGUehx6Us+eBL3c+3ph85n8+7v8HBQQwNDWFoaCjiD1doMRYKF/qm/Yh1AJGcalLrFLi0ltXy5GcyALyOFn2hL7parboAQZ5TqX1eP5VKOeuAi7OwRjvnutFooFarodFoOPr/UgnzdruN2dlZnDx5EjfccANyuZxzGXD1uLj7qj5xtqnQqPXOgLqkaPfLAWPMIIC/AvDz1tqlzV7fSv2IVCoVwvADAp4DgjC/CsCSnzt27HDC5ezZsyiVSpGXu1rXfsU2wo/IVupahQWLv3ChEhaCyefzjlb3y6f6QpsUN33utNBpcRP0/1KI0+pmn5gvr1Xd1E+u1igrwDUaDVQqFZTL5Ujuvc6Br4hQ6DWbzYjVyutVKpVIsJ2ff/6twFqL6elpnD59GlNTU9i/f7+r1V6tVl01PXV7cI6VWldfehLNyPnymZythDEmjY4g/4C19sPdzUmFohKhyphCaVqlc5///Oe7tl8gRKlQnaukAjQ6V0pDa5S7z4YQSv/6c640rNLYWjhEKVXtt1K+ShkrlXznnXciCUlR/Do+7YdS6LqPRmnrfGik/wte8ALXVtcE0Fl0iNCodZ0bzSBIcjskRZrrPnpffIVSqXKl1nXcOlaNjE+K4t9sffqkOvs6/zrnOoebeQcFYX4F0Ww2USqVMD8/7x7k0dFRJ6SYM02r1LfM/QdVBb/6HX2BxHxxUum5XC5S1Y2547QYKezUB72ysoJms4mVlRVHeTMATa+vudyaE07w4efxpLmTXupMc2NOOYPZ1IrnuNXi11Kp/KxR/D5tDXReWs+1MlwcWNVvenoaY2NjKBQK7h5zDIwfUFDRoUWuc6VshipynIPL4TM3nQv+EYBD1tr3yFdJhaICAgIuMYIwv4JQOrfVaqFYLGJkZASHDx/GwsKCs2JpuQLRHGJgfRU33ea3AbjqbhrcVigUUCgUnABn1TdNQ1NBSUFeq9UigVa02n3h4gtKCn0qBRpZ7lunGuXOa9frdSwvL7t1yzVSXa1SVpQjw0BBqP1RqttXgFQBuRQCvV6vY2lpyfWPS8Gq4kN2g/3yXQYq2BU6ZlVgLhPVfg+AnwDwhDHm8e62X0FCoaiAgIBLjyDMryAGBgZQLBZhrXXW8MrKCmZmZnD+/Hk0Go1ItHac5aVWsFq1cQKIfmPmjHNdcOaL53I5DA0NucpkFJAU5qxoxoA5Ch315bJIBfdnP7WPWrCF/u5UKuUihVWQa9Q8sFblrV6vO8HH/usyqLTMSYeRmq/X646m1j6p4NNYAms7K58lFdy4GKyuruLo0aM4duwYrr/+eqdMMQhQx8j+c544tqQIdV+AKyux1bDWfh7xCyYBCYWiNgOl1pVu1uIpt912m2srDQpEaWmdBz1vUiEQvd9KgWvREqWFNYpcI+GBaOEWpeCVTk+qoX7u3DnXTqrxrtH9ShcDyUt1qqtCo9yT2klLpiotrPfIXwhL51/vi1LPSVS5zp/S29on7auO2S/gkxRtrtuVfteIfs2c0PnfTH1//xraR41m1/lUhm4z758gzK8QKCT4ouaPkAtyUOj4PvM4+NR0nBVJy5cpZxTkpHYpzJmmpYuo8AfnMwW+pa5Baj6l7gtLno8Pv08tq09dfeyq0OTzeSd4NSaAfaTwpoXNKnH8YbDaHXPRuea6lq/VHPRLYZ3PzMzg0UcfxR133IGJiQnHCmhFOL68lKHQ9EENAgSiK6bpM8AAw4CAgO2PIMyvEIwxKBQKWF1dRblcdhRzuVzG4uIilpeXEwW0Wt6bSaEi/TowMOAC3mgRqkCj8CAFTqRSnfXBSVnz+0ajEckXV8GrAXLsgx+Qp2lqSiPrOPUcFLQ+nUx3BQMJGZlerVZRLpfdWumaOkdBl8vlnBJDgUoriQKddd7pq/9WUK/XceLECZw8eRK33HJLZGU3XoPCXCvy+WlqvnD3fehq5QcEBGx/BGF+BdHX14dWq4VKpeIEFl/u6if3Bbcv8C4EY4yjjbPZrPvPAjG6xGm1WnW+bA2+YwlU9lEZA80Vv5ACAkStdLW+ff+072KgwsFrWmudEGfOeKVScfPXarUixVV0PniexcVFd24yEup/17k3xlwS4Xj+/Hk8/fTTuPvuuzE8POziADi/Gs1O5oYCOil+QvP01XrvRcTFgSgVqXS4UqI+FZy01KaeV6O949aWB6IR2352R9x2vx9KE+vzo/S9UvZJdeG1T0qZK5Ws0dr+ftpHdUmoq0H38Zm1uLbuo8cqXQxEo7d1DrStVLIWo9F5Uhpb91HXRlIdf79fGkWubgfNitDIez2v9kOfp6SIdSD5eVHXiLb1eoFm7wGQUm+32xgbG3MCSlOLUqlU7DKdPpJS1RgIRgGuFdUorPkDZcpXvV5Hs9l0KWVK+aulDKwF1WkEvAoWXxmJK0ADIJI/7VeM41hUwWg0GlhaWnKWuMYYbBSFTpcDlRDS/bVazfnZWbqWxWT4pwrLc4G1FpVKBY8//jhe8pKXuNSXVqu1znfO54A/at8qJzQ2geNTyz0gIGD7IwjzKwgGu506dQq7d+9GsVjE0aNHce7cOeeDVn9tUpT6RoKFPmUKcwp0taZpcdbrdZTLZSwsLKBcLjsLV3ObqUmrQOM16HtnRDwD6VTYUBhpLrn60NUnT1+3bqfQZeU3Uv0aoa5KggbC8Vq6xCutcl6fvnVWmWP/6VfXZVafK1ZXV3HmzBk89thjuOWWWzA+Po5CoeAUHg3y0eh7pdPjFDvOL+fBX/QmICBg+yII8ysI+pdzuRwWFxfx6KOP4qGHHsITTzyBxcXFdT7mjVKkNKqdn5UC1yVN/VSwer3uct3VytWyq74PXOnvVCrlqDAKSy6JymVT1SdPKlt9wmQJKLioQFC4kjFgUBsD27TqmwpvX3CzP7y+Rr9TiANwCkSr1Yqk2vkWOpWti4HGBSwuLuLw4cM4e/YsisXiOoVGx8Jr6hz5kfhqicdV7esl+MwPEKVHk6KKWf6YUGoyiU7XdhLFqVHnej2lTbUoik+JKt2q1LBS6EoT6/FJFKzS5+qCUOoYSI7sToqa3kwUufYvadlY3x2lVH5S4Redp6SlYnWfJOpe76neOyA6Vxp9nxTdr+fVuUnqh86NPwdKwet8an/1udN9NoMgzK8g+vv7cf3112N4eBhf+cpX8Mwzz+BrX/sazp07F1nFy39563+NaFbQJ6zR6b7FR5p6cXERlUrFCQpdmcsvWOILGwUfdrW8q9WqCzKjUNfjaYn6/nmNPG82m441oCXO+VElR5UfXQ2OlDrZAgby6VKrzEOn8pPJZNaVrdVxMkXvYqCBfkCnwtPS0pJTqjj3XD9eFSAer24RdWX419Fsg4CAgO2PIMyvIIzpLBQyPz+P+fl5nD9/HvPz86jVahErME6Y6zk0iEz30UAo/QM62i79zX7eMulypbvT6XREwVDKXQUz2zyWwolWvlr2FPgUor51rhY6I9Y1OFCFbFx0N9diZwoe2Qn2WefWD/ahBa9xAlqhTsd3MWAUvbUWs7OzOHPmDBYXF131PQpwzhkAp5AksS9+v/U+BwQEXBsIwvwKot1uo1wuo7+/H8PDw6jX61hcXFwnJIHoi9sX3HFWu9LMuipXu91GtVp1Ud9aHtS33lXYqRAnDa0Lpvh/vmDXinH+giA6ThXkwFqxl5WVFSeglX72hRuwtgSrrtOuEeL+cXHz7QeQ8T9dCBw3i95c7H3XnHvS+SxS4ytKGqDoR/prqp7/nFzGCnCXFByr0o9KKyt1quPbqBa23u8kKlMjn3W77q80ttLTSZHw/rmSxqRR6BoprWPQce/du9e1tX64RvcDyZHW/toJF7q2jkmp9aQiKXFGB6FzoNHi6hLQGvZKuWsRHXVN6L3XefKj6pXuT8oI0L7qb1vHp9dOcgP4xYN0Pv1lcuOu5xe8uRCCML+CaLVaLu/bGIP5+fmIH00fKn1J+wFePn3r+471ZU/rVouiABe26lR4tlotF4hGge4H6akwZB8omJkqpw+un06lQksj+bmPRpbHgX5t0uHNZtMVlwGiwWHqTvCtXd91oEqBlqPdDHyWxFqLUqnkavH7Ues+88CFWTReAUDEXZB0fEBAwPZGEOZXENZaDA0NYWFhAdPT05iZmXHrXPN73Vdf0sD6ldRU2NGKVeuUQpGWqwomYH30s1p26q+l4BsYGIAxJlL9zVcutG9Km/P87Gfc4iIqKH1/vc6Lb837eezKUvjrp+ufRov7c07lgZH7dD2wVsDFguObnZ11wYPNZtPNK8/baDQirIrvu/cFNvf1721AQMD2RhDmVxAs1uLT4HGpaBsJcP1erUuNZGfQlw9SR3GFIDRSHIATLowy9wuzcH+f6qWg5zn9AjRqYdNK9suv8k8FNz/78+RboxpUp33zI/01z1vnUdkN+rNZ5lVTxS4E3wXB82t9elW0WLmOChmvTaWGx3MOfYWvF61y32VAXCzl6CMpklgpY6XTlQZVWngztLz/O5uennZtVa6S7o9uVxpa6XQtbKJ99eltXUZT68frWHVu9Xq6XceaVARHx+0ruHrtpMh4vZ5mLFx//fWurbS+Uu5zc3OxffKfG3VDJC3xqnOjY1W6X+dVnyG9th7r930j9xBxsYp4EOZXEIwSP336NE6cOIH5+Xn3AqcQifvBxwl5/3v1l6uA4gPiKwv+g0NfN/3VTA+r1WrrorxVSOnYaPFSUAGIrHKmFLJfKlbPp4wAhVlSrfS4KHsfnF+er9FouMI6tIq1//S/+1R7s9l02zcTCOezHspWaCpgrVZzee0ci7/SHIBIqp1a4iGaPSDg2kMQ5lcYzWYTR44cwbFjx1AqlZwgpvDdqGCML9T5Qtdo7EwmE7E4/bxsXwhp0BrrnMct+pLkAtDjNWiNwosBcIQKUfXLs7ypsha8li/IVTgngRYur0FGgd+p9a/9AbCu+I3m0mezWVQqlU0Jc7WaqSy0Wi0sLCwglUpF1jfX+85tSvPzPLynPhui9yUgIGD7Y0uEuTFmP4A/BbALQBvAe621v2OMGQPwFwAOAjgO4A3W2oXuMe8C8FYAqwB+zlr7d1vRt6sNtVoNpVIJ1Wo1kr7lR4QD0eUv44LFuI2CRldDY8AZLVstCKOWNS3VarW6rvqcTxP7UAHjuwyUZSAlrO4A35fNfdvttqOn4hY6YTBdf3+/Kzsbh0wmg4mJCaTTaVQqlUjWAM/Da7CPnEOmjKliQcaBxWg26zfX+env78fKygoWFxexsrLilCGlmskU6Hry/v3mNqXe/WDJgICA7Y2tssxXAPyitfZRY8wQgEeMMZ8E8BYAD1pr7zPGvBPAOwG8wxhzG4A3ArgdwB4AnzLG3Gyt3dZLPjWbTZRKJZdbrtW+4qjkpDawRmtTSLDqGYW5+qJVkGvJVH/ZUL0Gz520eAWZAKaXsLa7BqgBcPQ6BamumT40NIR0Oo2VlRXnxyYt7wsoCr10Ou2Wc63ValheXo4Iae7HMrP0szN4zz+vKkP5fB75fN6xG8oq0JrnPNfr9U37zXm8KiKNRgOlUsnNo86ZCmQdW1yFKD/SvReFucY0+NuAqC9S/ZX+WNVHqfOmz7C29bx6rPpZ1X+ufmStJuYHc+p9Uh+u+pH12dH0KV0QRf3I2g8dg/pygeT115PGkZRul+Q/Vz+5MmP+e0J92rpwStL7RNPUNCYgyeet51TF2mfrkn4/Sb5xrSqomUZJY9C2b3ioL17vt157o3f8hbAlwtxaOwVgqtteNsYcArAXwOsAvLK72/sAPATgHd3tH7TWNgA8a4w5AuBuAF/civ5dLejr68P09DTOnDnjXt4M+tooOEYtMmB92hqDuvL5PIaGhtwSnvQN+6liDGjTJU39fMexsTEY00mf0xeo9iuXy6FYLK7zdasPX4O8uCTryMgIisUihoeHkUql3Iu0Xq9HFobRgirMveY2a60bN2lpzgnXcecc9/X1ubXQAbiKb6T1aYXTj68+dJ1zKgXq798MGEPAZVfr9TpmZmYwPDyM0dFRt53Wurop/DnX50GFWfCZBwRcW9hyn7kx5iCAFwP4MoCdXUEPa+2UMYbq7F4AX5LDTne3+ed6G4C3AcB11123hb3eerTbbczOzmJubg7FYhH5fH5dARL1QxM+1eoLdwrMTCaDQqGAfD7vrF09B4U585Y1qt23hEk1UwhqUROeL51Oo1gsIpvNol6vO0HLseZyOeTzeadskDUoFAoYHx/H+Pg48vk8+vr6nNJRr9dRqVRQKBTQarWckFPfOwUd+0ktPkmQqRBdXV2NWPZkCGiJxy1UQgVFhTnXhr8YvznnuVqtolqturXV6c9n9D0VDI325/jiAhjJwGj8QkBAwPbHlgpzY8wggL8C8PPW2qUNLIW4L9a9hay17wXwXgC46667evotZa3F0tKSewnX6/V1/ua4l3GcMOf/TCbjhObY2BiGh4ed1ajWm0Y/A3B0dlyeNq9JepxlRxl9DcBZ2BT0wNqiELTKKXRpleZyOQwPD6NQKKBQKKBYLGJsbAyZTAbNZtNVWdM/lp4tFApOUaBLQN0TpOYJ9Str3flisehWRmOfuDAMj9Hcdv7XyHyyAQMDA4mUoX/ftQyslm3ViH2m5JHSpGXO++2nBFF4U9HYDlAmJGlRDWWJ/JQwvR9KiybRpVqxS2lhpV2V5lUqWOdctwPR9deTFjhJqnCn1KzS8jqGqakp1z558mTk2jo/Olal0JW+Vzpd+5e0PS7dFYi6Kfw+qmGh8+EfQySlA+rCM7pdnxX/N5m00I0ek7S2vM5lkntmI5pd77FCj9/MuuVJ2DJhboxJoyPIP2Ct/XB387QxZnfXKt8NYKa7/TSA/XL4PgBnt6pvVwoUOK1WC3Nzczh27BiefPJJHDp0CHNzc44CVgEdVyRFKXWNYGZkeLFYdPQ6I595bv4oc7mcs3g1wp1BZJoqRRhjnODmdl5Xc8x5TV0RDYATuv39/SgUChgcHMTY2BhGR0cxPj6OYrHo8rdZXIbR2nzgST2r4GbfCT+nnhY22QLS6FoaNpfLYXBwEENDQxHFQ5UJjTcg+9FqtZzfvFKpbCo3lP2lGwToCI9KpeLYC1atM8ZgZGQkolz40BgCrVfA7wICArY/tiqa3QD4IwCHrLXvka8+AuDNAO7r/n9Atv+ZMeY96ATA3QTgK1vRtysFplstLy+jVCphdXUVY2NjzjqkD9ePXtdiICrIdT9ah7RwR0dHMTg46OhfYK04CwUbC9Zo1Hir1YqsFc7Vyur1uhMOagVTi9SAJaXBtQodKWz6oqlwDA4OYnR0FKOjoygUCo4FGB4edsF4IyMjzirx89w5NroN9Pq6prpGomvdc1Zcy+fzGBwcdFQ/9/OL42hBk3a7vU45uJhCD9lsFpOTkxgdHV1XCEYXhGm1Wk4Ji7PI/SIr+pwkWU4BAQHbC1tlmd8D4CcAPGGMeby77VfQEeIfMsa8FcBJAK8HAGvtU8aYDwH4BjqR8D+z3SLZtZQpLebl5WWsrKw4yosCwU9zUj8osL7qG9Ch4wYHB50QZ1oV91eqXYvJqCVXr9edAG40GshmsxgcHIz41m03h5xChdY8o9cZmMaxMBBP1y+nG2B0dBTDw8Mu2p79o9CkkB0aGsLIyIjzcVPwqVJBWppzvLq6inK57JgBDWwjQ0EFY3Bw0NH9jHLnvHOMCp1H5vLTz77ZFLV0Oo2xsTG3rno2m3UKFe/B0NAQrLUuj51pcBTeft66+slVSdtqGGOyAD4LYACdd8r91tpfNxukom5wrogiQyj9qFSmbt8sza7QayRViVN6W6PFlU5XytdfREOpea3iplHrSl3rmJQKVhpa+6QR17quuv9Zz7tv3z7XVqo7ibreDM2+keKo19b+qmsjqZqcXk+fZ50/dUckXde/hp5L99uIpo87djNrrPvHJFXL03usz5fOWRK2Kpr984j3gwPAvQnHvBvAu7eiP1cLWEGNkc+MuqYlrb5UhQbC+S9rbieFzBc+BbQKSa1mxv0o0NvtthNIpIxrtZorFsMgLAp0UvGMgFe6mkVXqGDQL00rMp/Po1AoODpc/fV8ieoa3wwOo+BXi5nKBJdIZbqY9pXzpOlgdDOQXqcg9yupJQXB+efW1MDNBJ0Vi0VMTk46qr7VamFxcdExK1rOtlaroVKpoNVquUA9LRDkp9ixT0n1ALYADQCvstaWu+61zxtjPgbghxCTino5OhQQcK0hVIC7TFhdXUWpVMLc3BxGR0fdS3x8fBwjIyM4fvx4JIdZLSufWveD31KpFLLZrLPyNBiKx9OioyVJgU4Bb611QoICgsVQSLP75V1phVMI0kKlkGZAGQPWGHxHtwIpcB7PwDUNOKPQVtpcoa4AVqrzC+JQiaDyQkqdDIbOG+dfLV6/Ih/B86pVsBlhnkqlMDw87OaGCgoD/FRx0uVmqVyQ3u/r63NjpbIUp7xsNWznojQh090/i+RU1ICAgEuMIMy3GNZaLC8vY2lpyUVS0opcXFx06VflcjkSiaw+cl94+y9pCiotO6pBYSog+ZmBbP5ayxT2uVzO9Y2UMn3YGm2uVC/pYR5PvzgtfgpGWv/qB/bLy/rV4zTNTfPOgY5Ar1arWF5edtHtPr1GKluVGf5pcJnGGChlTyQVc+E1mFK2UVpYKpXC0NCQYxo05515/ByjumY457VaDZlMxvn36fpQ94AqIJcDxpg+AI8AuBHA71lrv2yMSUpF3eg8scVwlG7WtkYI+/dDac2kxT2Sin+ogqaR3wrdRyPQ/Wj2uPH4/dO+a/800lmpdY221wj206dPR66h4xsfH3dtpdZ1jfakIjBJBWH8rBhC5wOIrteux+giNM8++6xrK8Wvbgul3LUfquT76b2KJAo9qWCQzl/SGvdJWRf+tfX4zVD8Sc9dEoIw32JQGB8/fhzLy8sYHx93S2hSiA0MDLhAuKWlpXUUOhANePNfWnwBagqYvtjVSlN/MZUAnl+pWy1fynQxXcc8KR1sYGAAo6OjGBkZcfQ1LX8eo8IGWFuznCVntWAKLX1S+fwR6EpwLDwzPj6OcrmMpaUlV45Wg+EozNVKV8ZCBTmwFhRI65dWMlPS4uj3OAveRzqdxq5duzAyMhKpeMfCOVRKeF85Vvat3W67IEV1aZDm1+fmcgnzbozLi4wxRQB/bYx5wWaPNVI/IkTfBwQ8NwRhfhmwuLiIWq2GPXv2YHBwEMZ0KqmRhj537lykrGoS4iwtvrR1MRUV3D5lzJclBSaFO7drQBcFeq1WczQ2FQb6cxkYx7Su4eFhjI2NORpdI8hVSKrlTZqYVLe6BSisSO9TGeDYVYFhuluxWES5XHYsgvrjfVeDBsXRz0yhzWvys/ZXa6n7PnSfCfExNjaG2267DUNDQy79jr7ySqWCM2fOIJ1OY/fu3a4iHMfZbrcjuee6Kp6m4Om8XE5Ya0vGmIcAvAbJqaj+Ma5+RH9/f0/XjwgIuFIIwvwyIJ1O4/rrr3eW2NLSEo4fP47h4WGMjIxgamrKWZF+CppaKn4gXFxxFwpNXeGLwoVV1DRimlazT5X7Viup8kajEREWtHYLhUKkCIymammuNn3COh4GoxUKBaRSKSdIOSY/T53HAliXLsbYgUKhgHq9vi5fngoCBaRGga+srDiansF0jUYjUidflSFa4Mpq+EpTHFiYhrUAGAz52GOPIZVK4frrr8dNN92ETCbjsgoKhUIk9Y/X0rHrM6OMzFbDGDMJoNUV5DkArwbwn5CcirrRuRxNrVRmUr1t//ehSIqITirMoedKquuedH7tn7oB/Ovpfkrh6rm0OIlGMev63SdOnHDts2fXSnLMzET1pRe+8IWu/Y//8T92bY0EV0o8qYjOZqK3df6UVvf7ofs99thjrv3EE0+49vnz511bI/K133q/kwqy+Pdaj0l6djYz1qQo/iR3zkbnTYqY12I+m0EQ5pcBxWLRRatTmOzZswfWWszOzmJ6ehqVSiVSzlXzpQmlzvXh03xjDXyjEAcQiUhnoRMKLwDrLFSfqucfFyuhf5+++nw+7/LaVUCqMFcrlteh8sC+86XL86r1DMCdh0KaL3zS0HHuBJ1TzgX31aIzpNDVhUCBrRH0qmBojIP+T7LOee+4UhrHubi4iL6+Pnzbt30bbr/9dgwMDKDVaqFcLrvI/6sYuwG8r+s3TwH4kLX2b4wxX0RMKmpAQMClRxDmlwG5XC5Cz5bL5YhAoW8ZWBPiKhzirHRSwhRepIc1UEwtdFrtzWYTqVTKKQ60zPV6tLoZqEbLPJvNRgLC6Fvn97wGr09qXuMDVMD79DoFW6vVcr56pcQ1jY0ClcVv4pQPzoXOofq+fd+40v+ay640uh+3wDmNUxTiIttHR0exf/9+1Go1TE1NIZVKYWRkBDt37nQR9jyu0WhE4g02Y/VfCVhrv47O+gv+9jkkpKIGBARcWgRhfpnAF3Cz2cTy8jKazabLv15aWsLU1JR7efslSn1By/OpkFdhxZQxtcwJpjPpAh4UZLTY1deqgkOtd81R12hw/qcw1JXYfKuc5/Qjy6lIUHBpkB1pbyoCalXncjmnfHCsPF6Fsgpmn2pTRYFjoQKhjERS7IIfuawUmjEGBw8exB133IFsNovx8XG37CvnE+jUjyZrwGIylzMy/UoinU67ZT+V6k6i2ZMi0DcLpdA30/Yj1Ql9jvwo5KRo5yQqX2l2pZg1gl1paI0I12sBwKte9SrXvuOOO2Kvp/OpUd0aGZ+0XKiOQedAi88AUdpdv9P+Hj9+PPa8WqhHo9yTCqzo/PvPhL/yIaGuEc0a0HlKouJ1H6XW/Xuhx6g7Q++3HuNnBFwIQZhfZpCOLpfLKJVKqNVqKJVKThipsCEl66daxAlxFTpKcRMaRU6BrnS9pmL5wlSLzfBPC6T4fSZdrRHw7B8AV51OlQKmyqVSa0ugarqaUt+MpqdlrlY0o+KpdGjsgObsx1nOGrPA8/lz76ec8ZocO+dXF6PhOfL5PEZHR52/3Gc/6vU6FhcX3VzkcjlXHQ9ARAlS+EpJQEDAtYcgzC8TWKWMfuVcLodqtYp6vY7Z2Vk0m80ILQwgInhUiFAwazAWBSg1f1qzPNa33JkLrQJOj2NwG6PJVTjGCRUex+A15s6Tauf1lDZn8Jy11lU6Yz47i9KoggKsKS0MUNNV03w2gkqHMg3sJ9tamMbPJvCpeQ3g0+toXjsVE5aFXV5edkVeRkZG0NfXWcOePnCW0KXSxCVZWZGuv7/fBchpTAP7pX/KpmiwZEBAwPZHEOaXCalUCrVaDdPT01hYWHAW4+zsLEqlUuRFrVajH1jFl74fPU0fNQWNWtzAGn2jNLxSxmpZqmDRIDEKeW7XFDEKcv5pMRxa5hTeasVrTjlTrDQIzV+tjEoAAFfExqdEeR32jYqEzq1Pt6tQ53GqPKmg53h5PQp1poqxop0K2Xw+70q4TkxMYHJyEmNjY6jValhcXESlUnHV8RiVrwVhgKjS4IN94NiIXhPm6XQae/bsARBdNjNp+Ukdn09rJhUF8V1PcefyWa04JBVYUZoWSI7K1+U4k2hXXYJTl2LVKHd9JrQADABcd911rq30s0bDK2XvM1Vx8JV/QqPfdWz+tXU+tG699l2p9aQiLjr/Scu7+q4pnask90LS0qhJkftJz40P3U/HpO2k4jebQRDmlwn0T586dcpVUAPWbnChUHAPsApJ9e36PnRNP1JqWD/76Uq6H7Bm1QNwwoOCnsFyPJZUu6aDcT+yAqwjztx0trPZLCYmJjA4OOii4ldXV7G8vIy+vj4MDg66vHQKSg2gA+CE5ODgoKtrf/78eVSr1YgbQGMOdDlUtco5fzoPGpin+/gpXlRqdB65j7+IDZW08fFxTE5OolAouPzy5eXlSHpZo9FAqVRyx/Fe+9kLvhslLhYgWOYBAdcWgjC/DKD1lk6nMTw8jJmZGZRKJTSbTWextVotDA0NuQU1tDynD7/QiwbXqZ8ZiAau6EveF/jso/qZ+R192xS0KvwpzLU6XKVSWVfCdu/evdixY4eL1lbfe7PZxPnz51Gv1111vMXFRWfVs7CKrk62srKCWq2G2dlZp5A0Gg0nZNnnZrO5rioeEBXkDNDTwD3/vzIlKsg1Kp4uBE2jY1nb2267zcUE0CWgQW9kG0it89pJOai8P7yvynBcjRHvAQEBW4sgzLcIfPGzljYjsVkghYKD5Tr50p+dnY34fuNylelzVwpcaWHS53EWu6aPKdW9urrqBJFeU2l7FR4cG2lwKiDq06ZlPTg4iMnJSWeVA2upclx5rVKpYGFhwV2fVj2j4RnwptHuIyMjyOfzrq49FyShskABp1H3FOwcH4/RQDUgWhuergP2W33ofoQ7GRSNExgbG8MNN9zgLPdCoYCxsTGMjIwgk8nAWutq1TMQLp1Ou7gB392hz5j2SV0qPuXcK2CpWyBKiSrtqjS03jPfBZFEBydFwGuktFKqOpdK4eq9UGp9o2U3ta1UuVLoSfW9kxQ7LZjiL7+qhWY0Ov3w4cOurRHzej2lfNUo0LbOjc6BXzhHj9GofKXH9doaya3zodfQ+6tzrrS1T7Nrf5Oiy/V43Ue36/X8jB/Cz2xJ6q/2UedA79FmEIT5JQZf/hRkagkzeCqXy2FlZQXVahUDAwM4cOAA2u2286VT6PjH8qVNHyytWioLFGi65rXSwD78/GwKTK3c5lPtFGQU+Cq4KRh1BTNrravKxoebwtEvJsOxUsFguVhGhbPPtF5Zk71arTqFQhdO0Rx6MgwqlDlmzptGsFNwsq9K0auy5W/jNbgG+eDgIPbv3499+/Y5Ic11zFkBrlqtolqtwlrrMho0RsAPOKTg5n9lDfR++pH3AQEB2xdBmF8CqGWskdZqHavQo4Xebredz3h4eNhRz7S6AURoXg20ovWVTqedYNyotrtf0pQCj4LZt1j1OADrqF8NUPMtdApfChpS40DHL7y4uIhqtYpGo7EuX55jpDJDZYUBKlyRjYqMBgj6KWqks3luntNnGLg/AKdEaF583LzynnJeOb7l5WWUy2WsrKxgcHAQN9xwA3bv3o3FxUVkMhlMTEygUCjAGOPG32w2sbS0hEql4iwSXyir4qN9UKtcFaKAgIBrC0GYXyR8SlspZ774Kex0n1arhWq1iqWlJdRqNaRSKVSrVczNzTkBxAVKWJ1NrUiNjqaikM1mnQD1U6y4HCeAiJWuflVN26KPmQKPVjVBfzz/OEa1EJWaZgU3AG6s7XYblUoFpVLJpeWp1azMgwpz329OSmx1dRWVSsXR6MAau0ABq1Q0o/j9++nfKwpxvxKcH/jnpwjSNUA2olAoYHJyEkNDQ+5+su/qO2cBmWq1ikqlgkql4goK+XPrR7j6AY1aMIifewn1eh3f/OY3AUTpY6UslZ5WOncjJcYPaiSSaNck2lyLn+j+GnWuNDKQvKylRn/r8XpepW2T+qFFWHx6++Mf/7hrq3vi6NGjsf274YYbXFuXSVXo/CUt63no0KHIMVoQhtkKQHIRnomJCddWuj/JfaRzo22/eI2eS2nzJCQ9Bxr9nlTMSd02QNRNou0kN8zFoqeFOQXB5bgO/+ufRkBrHrL6j7lNrXJGaC8tLbmbmslkMDQ0hKGhIfeQUWACa5YlLeNKpeJSsAYGBtwPSa1mjWYHEHnJ8zMQjfLmddhnFSYUsmqdK03OOaFAZo50s9l0vjtS8JpS5lPsel/VUqalT+rZWhsRjhTYyoqoEsTx+RYv22qFa3qczgv7pAJU+2mtdYvGFItFx0qMjo46IT85ORlRnlg4hkrU+fPn3TY/oE2ZBb223k/er0CxBwRcO+hZYb5VLyqN8FZfpFqdaq3pi1MFAi1ZFkAB4IKimE/NoDNa0fw8PDzs1hCv1+vu/CqcZmdnUa1WMTo6Ggm8Uqrb93UDay9+Kh0+00ChwrH6LgPNDWefCc6HroNujHFpau1220Wgz83Nob+/H5OTkygWi4l59Zzn+fl5t7rc6OgoRkdH3frng4ODkVKzvhtB/ckqGPXecSx0Wej9JXiczoEqW6lUCrlczgnwkZERN/5qtYq+vs6qc9Vq1fVZqX9VwpRFUVeFKh++8sRnRCn3Xg2ECwgIuDj0rDDfCujL0be6fGGuVrjuq9SwFluhQAPg/KTtdttZXyweAnSooXw+H/EXq/LCF/3y8rLL4WZJUBUyStP76WcUnhRQWrBFBakGqxH+sYSWNVUhRVq90WigXC7j/PnzbrEZ+tdJ6fnCx1qL+fl5nD17Fo1GA5lMxuWnG9NZpIaR4ByXKjEcB4UfF3HRbXpP1VfuC3MNfPMjy1OptYVdtM1IfS6mQwueSgcFuM4dU/A011wVLF+55GeNDyB6jWavVqv4yle+AiC6BCQj3IEo/au0sl+HWyngpDlR6tl3K8WdR6+nv3s9jxa78b/bDKWq1HBSRL4WW9HIb41eB4Cnn37atXV5VF02VSO5lVbWWu7j4+OurfOsv1e91te+9rVIP/xYD0Jp9he84AWuvW/fvtj9dQ6UMk+irTdK7UwqIKPtpPuVtL9ez2fQkvql91ifNXUzaWGfJPSsMP9WAn34AvSPV0Hu/8W9UH0B4Fu/vpXEILKVlc7Kaa1WC4VCAYODg+jv78fp06fdS5zFT8rlMlKptVXOtC/AWo60+ohJEyfRy7TINWVLLXWmQgGILGziW83AWjCef2+4bXBwECsrK85XzhcHFxBptVqYnp5Gs9lEsViMXNtai7m5OZdLzih2KiqFQgH5fN5Z5RTi/n3gXKgLQZ8DvV/6HHB8cX5qHT/XiF9dXXU1APL5vLOW0+k0xsfH3Uu63W47F4u6PnQpWaX4tYiQr4T4f/pMh0C4gIBrBz0rzJOQ5EfXl52+7OP8vdSg+Nn3DftC3qfZ9fhareZoe+YU12o1LC0tYWVlBWNjYxgcHMT8/Lyz1lutFrLZLHbs2IHV1VVMTU2hVCqts9DZ1tQqFbhcNMV/4bM/mrpFfzT77fvWdW58IRPnl6d1yf7V63UsLS1hcXER9Xo9kgPPlLZGo4GhoSGn9FSrVSwsLDhFhswB8+yHh4eRz+cj9LpGxuu95H+tDkdo8JhPn8cxNLpdhXG73XblXDXLgGl5PIbxAkr9c4lZKiYa8+Arkv5zrf91/AEBAdcOtkSYG2OyAD4LYKB7jfuttb9ujBkD8BcADgI4DuAN1tqF7jHvAvBWAKsAfs5a+3fP8dqRz/qyU0Gm1pnSnL6ABtZ8wT4FqwKd5/QjyzVnm/vQOiU1XK/Xsby8jNXVVbeMZy6Xc6lq9Xo9EsEbBxUOKpyTfNEa1EZlIM7yjlNyOM9UdEjdcz1yxgHQx5vL5TAyMuKopVKpFBFoPH+pVMLi4qI7Tt0CtLwZGKZWOSuv+QF+PNYPjvPvXdzzEZem53/Wuejr63N578PDw5HMArowOO+8L4ynYO4599M5ifPvq1Lq55+zP/zca0KdzxQQLaSikcFcIhWIUthKuwJRmlLvndLKSrXq8UmUqBZYUYpY9/efE70Hm1k+VPuhlLbS71rHXKPD1R0BRCPmT548iThohP2ZM2di+3rjjTfG9kPvkdLsvgtBr6EKskatK7Wu23VuNPI+iSbXefVrxOt+Scfr/U4qRqP7+1U2CZ0/H3p8UkR/UpR8ErbKMm8AeJW1tmyMSQP4vDHmYwB+CMCD1tr7jDHvBPBOAO8wxtwG4I0AbgewB8CnjDE3W2ufs8PPF0w+DR5nSfv0ue6zkXUeJzD1xaqCkAIegAuGyufzGBkZwa5du1wBGFrVrJ42OzvrXs6+4sExqUWt44ir7c3z8Du1vnk+/zw+/azUPelmrvpFFoJ16Fn1jkKvXC47fzmj/znPGpingW1UEqjw5PP5iNJDBQJYW11Ny+Jq/3UufEXFp9GVhtd7q+DYs9lsxGdOCp7ZB1RG+CxwvoCOP9YYg6GhIRczobXp/efKF9o+8xQC4AICrh1siTC3nbcJI0XS3T8L4HUAXtnd/j4ADwF4R3f7B621DQDPGmOOALgbwBc3uMY6OjgOcTSlT52qUPetc41s9s+h59XzqfVHYcL0MVYwU19zKpXC8PAwhoaGnDZHf7nmKwNrL3xen4KPrAAFtNK67BehQVxxQV1+xTFVWJRmptWrFdeYlsUgP46TPmTS0LlczqWoVatVV99d7y0tZA0IGxgYcOVvKcg1Ql2VCwaYqQLC8+ucxLkukp65OJ80BWuhUMDo6KhTPlZWVlzEPYW25o9ba904dBlUnleL0sRd1xfu+jzzXL0WABcQEPDcsGU+c2NMH4BHANwI4PestV82xuy01k4BgLV2yhizo7v7XgBfksNPd7dtCkkvNX6nAsgXxBS6fr40hXScf9w/j1pFSuXSKtZCLPV6HeVyORLRzGP8sXDb2NhYxDpcWlpydD5pfKUnNR/aF2TKJsQFfqkCooLFjyNgm+lnrEanPnoKelqirENPoc50LfaP/VIhSSFIq5a+fl3MRBc+8WsmM9dd+63Klv8c+Ra6+ql9alsDDrPZLG666SZkMhmcP3/eLWk6ODiI0dFR7NmzJ1LcBsC6LAT2U3PwWemOiqBG62uf9FlU90qwzAMCrg1smTDvUuQvMsYUAfy1MeYFG+we59hbZyIZY94G4G0AsH///ljLKk5gK5VKoZfkL/WFdVweuf+f5+CL2KfkgY6wUvo8k8mgWq0inU6j0WhgdnYWKysrruY6/Xq03Hh+CgRatQyK07Go5aYuA9+/qmPwc7DpS5e5j5xfKVwKbmCtApsKGY3MplXK1dGy2WysK4N+cvrGNfVMa7xrf5Sp0f6m0+nI/eX+HKPe95hnLubRXPtOhf2ZM2dcnfVCoYDrrrsOo6OjGBgYQL1eR6FQcCu5sU67RuHzfGRn6CdmH6kc+lR6XF97zVeu0HtHqP9RFTb1CfsVt1SRUT+o+j6TUpeSFnPx09/8PsdBj9cxJaV4KTTt7ODBg7HtjSrAvfzlL3dt9cU/88wzrq3xOElz89RTT7m2zr/uo/Pvr6uufdS0Oq04pz5z3Uf97fpO0jQ6raCn90K3+/3Vsep59Vnz3ZNEXPEtf5+NfOZxsS5AdJ40NuTYsWOJ5yK2PJrdWlsyxjwE4DUApo0xu7tW+W4ATHw8DWC/HLYPwFl4sNa+F8B7AeAlL3mJ9YUYEK3OFSfsSV2qsPWFPc/jC3mvL5H0r3a77aq7AWurjemCIqpE8HzLy8uoVqtYXV11BWDm5+cjOcu04nO5HIaGhhzVToHKwi58qHyanJYw++2Px6effVDw+RahXkNruzOdjfOoFdw0UpzUOa119oUWfjabdTn3TFnTe07wGnwhqrDWMXBfXkfvnZ5L//NYKgF6/9kP5r2fPn3aBbTdcMMNGB8fhzHGsTGMvud85fP5iDtCg+PUYh8YGHC0vW95q/Kp91P3CwgI2P7Yqmj2SQCtriDPAXg1gP8E4CMA3gzgvu7/B7qHfATAnxlj3oNOANxNAL6y0TX0ZRoXCOYLc90Wt3CGnkfPpelKWmlNKWrft+5b9+ofX11dK8fKl7y11vlTadnlcjnk83lYa11t8mw2i+HhYdRqNWQyGRQKhUhpVAaHafS0r1n6LAb/q0WrVrwf5KaCkm0qLn4pWX++fZ8+8+vb7TaGh4fdIiVc/YyBbowHIDQozVrrBLkKQ+2zL6B9hcAPEIyz0HnvfIWBfSuXy6hUKk6p43KTQ0NDGB8fx549e7C8vIz9+/ejWCxG3Apc7rRWq7nKcUxnoyKkApz3xxfiel/U7RMQELD9sVWW+W4A7+v6zVMAPmSt/RtjzBcBfMgY81YAJwG8HgCstU8ZYz4E4BsAVgD8jN1EJLvSt+rvjPNtK92r/kXur7QzoZ/jlqHkOfnC5bm5DKlP1WuAF9ChxLhqGl/AXAqTS2SmUiksLi66yGcGfJVKJSdEmGvNQizsNy1h1gzXYDGdK996U0Gtio0udkJBREFIRoLX9wMFNZCLOdXNZhPz8/Ou1Gt/fz8mJibcErHz8/OYmZmJRHTTD82AOq20RyVBWQSdW3U5aFyDPgM+VOhqhTl9PriIDuenv78f5XIZx44dc/09deoUJicnceDAAdxyyy0YHh5GLpfD2NiYq2rHfjDnXl00cQI8jmonfMXtcqD7e38YwBlr7WvNBqmoG5zD0cFKUyZVKVMaWil3IEpdqzKYtOBFkkKnCpFeQ2napAVbgM1VGlMqWVPqdEGV/fvXyMvdu3fH9ttfQ/u2225zbaWur7/+etc+ceKEa2tlOF0wRlPydA7UVaBV4vwUOb1PmnZ24MAB19aKf7q/tnXOku6vzqUeC0Qp8aQMFv09xTGYPpIU5rhiWoTeJ+27Vjd88Ytf7NoPPPAALoStimb/OoAXx2yfA3BvwjHvBvDui7hGxOKKs6p9YeoHQukx/otRLW//5cntar2qEKHgZM31er3uAsT4siqXyzhx4oSzQqkQ0AKnZcrraqDX0tKSCx7LZrMYGRlxP/qZmRlXqMYvb0qfqz9/FFi+EPcfZAo0jpOMgwo3KhBKqbPNuvNA56UxNzeHWq3mfgyMXB8YGMDq6qqLcvcVjuXlZWQyGWfZqyLH83ABGr2/7K/uq595DbW+4wSm/6JnJH7SD79er+PcuXOYnZ3FyZMnMTMzg1tuuQV79+5169sDa8vMUtnwWSBS8XGKhz63mm54mfF2AIcADHc/vxMxqaiXu1MBAdcCer4CXJxvWy3COApc/eRxiBP2up3wK53xvH7xEmqKvGatVkO5XHarabGoSjqddjXM6U9m2lpfX5+j5ldXVzEyMoKBgQGsrKxgcnISw8PDWFxcjFDZ2gdrbcRKVavap83ZV/+/WqW6rypLrOWuf3QB5PN5VKtVTE9Po1QqRSwWotlsOtdDHHvAe8iCK0xPowCk0kOFQF0OWu9cU/o4HqXkCWVv4ixgZWI0s4DKiJZ3ZR3+qakplMtlnD17Fi996Utx8OBB5xPnXDKnnjEIjEPwo+vVncB+qvC/XD5zY8w+AN+PjkL+C93NSamoAQEBlxg9K8z5wvQpct/a9v3lerwKZxXYKsCSqFffb6vWrKakaZQ3I5UrlYqLeNbAL0aukxrnoiCM/Ga6U7FYdIuLMPWrUqm4ldQoDAj1iauA16CuOB+7z1pwTFQKaO0zdYosBFeHo/DhMYuLiyiVSlheXo7koGt//HujfmIqKqTVqfwwgE4tbApFBs/x/JxTbuO+OkbuG1ff3p+/VCqFyclJ7N+/3xXGWVpawvLyslMYSMNrECDniHEBu3btijzT+qyx8IzOM8G5pZvCV9YuY2rabwP4ZQBDsi0pFTURmsqo41QqMqmtEehANLpa51TPq+0kOlwVIj/1kfDpbUWS0aDH633SfpPJAqL0sfZDj/Wr4Cl27Fibfj1Gz6tthboUkihtjUzfuzeaWeynZBI6Dr1/el51qyh0XjUKXOl+f+793w7hywZCx6r3K4mp812xSUh6pnTcF6r66aNnhTmwPpfcf9HFWe1xiLO8fX9lknXu+99ZCYwPAV9OjDrXZVBZ9pPpaqlUygkm+mAbjQZKpRLK5TJqtRpSqRQKhQKGh4fdj4LCP51OY2hoCNVqdZ2lrbQ4sBa45ys9hB/s5s8L2/yjolIul1Gv1yP0OVOzyEgoda7FUlRJ0NxxrhzHGu081j+O7AMVIt4PjkfHTfgZDByXX8PdT3mjEsAUumw2i/HxcYyMjMDazhK1S0tLrv/Ly8uoVCqYn59HKpVyfsNTp06hWCyi3W67pWDpalDB7LNM+jwqA6FMBMe31TDGvBbAjLX2EWPMK5/D8S7l9HIxCQEB2w3bQpj7fnHfoozziSv0O/9FyT//Ja7H+edgShEpZgpEWq6kw7n2tSoO6tvmmua6xjawlvbGHO3l5WUsLy9HfPW+4kIBpdchu+ALchZD4XEUnEp1syytCnJG4lcqFUeD8zyknbUGvFrdFGIUvhq0yO927NjhlBj6uiuVioskr1Qqjuq31rpa8RrLoM+OL+h8tkUVBypCfjwBq/n19/ej0Wi4e2uMwdjYWCRdr1QqYWlpCZVKxd0zoBPMtLq6ip07d+L66693tQf84j+07P1nj/dB7y+ve5ks83sA/FNjzPcByAIYNsa8H8mpqBFYSTnt7++/vFF7AQHbBD0rzJWS1BezL5z0pad+4jgBb+1a0FCcxapFS7gtLiCJgtjPM2+3245WJXWqglKVANYtX11ddVQbC0LQT5/P5yO0bbvdxvj4OIaGhlAqldYVi+ELX5cZpXXLsagyoX3zBaEKF/rGKdCXlpawtLSEer3ulI58Po9du3a58rSs6lav1zE9PY1KpRJxLzCAb9euXZiYmMDExISzVrPZrBPky8vL7rrM2SflTN+65tlrEKDOS1LsBe+7j3a7U6J1//79bplX0u4sxUtqslAouOjhkydP4syZM1hcXHSrwk1PT6PdbjvandY9XSlxigXhrxZH14m6d7Ya1tp3AXgXAHQt81+y1v64Mea3EJ+Kmoj+/n4XCa00YxLdqXPhF4BRZU3p5yR6VY/X6ymSggp1u7+P0sraj6QIdqWMh4bWvBZ6L3WBk6QIbSA5I0DHrfS4RpcrVX748GHX1mj26667LvZYv7hOUqGZpLZeYzNzrlCXh+/+SIp0T+qfQp8JPW+Swuzfi6QMCb3eoUOHXPujH/1o7HmT0NPCnJMQJ7xVAKn1F0eZq2DzLVSfZtfj9MXpfx8XgJZKpZDP5zE5OYm+vj4sLCw4Hy4FImnS1dVVlMtldw3tMwWdtRaVSgXGGEfVMj2NwizO78/jVcDpmFSh4bzwPFqcBoDz5TJwrVaruSC9TCaD8fFx7Ny5E8ViEcViEYODgxgcHHT11ZvNJqanp12AF4VitVp1qWp0XbTbbVQqFXcNMgHs09DQUGTpUVLcq6urToHiH++5LnWqSopPX/sR+6urqxgaGsItt9yC8fFxzM3NuRcs7wOVGTIyN998s7PWs9ks5ufnsbi4iMOHD6NWq6FYLGJkZAQHDx50rAuta3UrxAUq+r8NMhNXmLa+DzGpqAEBAZcePSvMgfU+c58S94W8L9R96LF6jEY86zZ9wStFr/nsfJmzzXSyXC6H6elprKysoFgsIpPJuHW+AUQW6aDwpdBnABcrzo2NjUWKslAD1xzppLlTK9VPM+N/1Vo1sI+Cjdes1+uw1jpLPJfLOcFtjHGCn7nhpMW5Ahqt2mq16ih3pp6xoEyj0VgXLa/3mAqK3mP2k2PUldR8a9y3yDVnnfeF++3atctZkcPDw0in024eGL3faDSwtLSE6elp5PN5R6UPDw+jv78fS0tLmJmZcf2cm5tDpVLB+Pi4s9YpwMlMaL/9Z9X3sV9uWGsfQidqHXaDVNSAgIBLi54V5rSc/LZ+n3RcnMXpf5dExWtAGRC/8pZGGPO/Ch+ucU3fN1dL4/KntGoZxU7hTaFCAbm0tISxsTHk83mUSiWk02kX7U1BqeNm37ldKWeOJU4IqOKiY1arXHPFSQ9TqFNRKZfL7jvOEcdJ+rFcLjtGgmVdjVlb+5vX0TQuP+pcaXW9n+pT9scSFyCpzIXS2GRZyCCw6Aut6Gw268a4sLCAhYUFrKysYGpqyhWRIfMyPz/v5nB+fh5nzpzBo48+ikqlgv3797v0Q62Dr/0g1HLn/PD56yXkcjnceuutAKJUqxYa0XFrBLRfC1vXsla6OmlOktbKViRRuBvR7L4LjlDKNymCXft07ty52P4l0bf+8Uora9EZnSctGqPnfclLXuLaWshG+/Tss8/GnhOIjlsLymiRFHUdTE9Pu7bOs9L3ug647pNUcx2Izpt+l+S6SWK2NrrfSduTouHV/aHz79eVvxB6VpgDUYvc38a27/dVipL76H//5vnf+9cAohawbyHFUfj0++7cuRMLCwuoVCpOUHM8hULBLbhCfzqFHy1AoPNiokVMwUfhyB8yX+y+IhLndogLnPOZCGuto7LVOtYV0GhZK41trXWxAKSuKfTULaFLhDKYrVaruWtSedM0NULHGMdK+ExNkhWblMqn39NFwBrrjLhnIZjh4WG3oA4VujNnzmB0dBTGGIyOjuLgwYOYnZ11Fv25c+ecz7TdbuP2229HoVDA8vKys/g3enno83Y588wDAgKuLHpWmPv0N1/cGqWdFMDFY3zLW61XjWz2FQBeX4/l/kq58xgVakq553I5LC4uwhjjip/QCtSFVhiVTdp9ZWUFi4uLjmpvt9cW46jVahgYGEChUHB+6LiFV9h3tajj4AdcqfWudcOpGavVQyWCwpsWrWrHdC3465LzGjyH+tR9Vkb76le988ehwk7vua+N+7EQ/vPGAES6OorFoquLzz4y2HBsbMwpbbz/VOZYwvLUqVMuwLFYLOK6665Dq9XCiRMnsGPHDtcXP4BR76k/NrYDAgK2P3pWmOvL2Q9uUytPrUlfOMfRYSpMVDj4+/E8fn/U8iONrTW7/aC90dFRZ9mxTCkAR8dzPEz/Io3L4CzS0YVCwV07k8k4a1/Tm3zEpfHp+JLmhcepVUwBR6gPOg5q8Sur4N8rPVfSefQecJ7jaPa4OAH9Lk7piesPAJfrPzAwgMXFRUxPT2NiYsJZ3VTC+vv7MTY25qxvsg4DAwMYGhpypX55v+v1OiqVilsnfnp6GgsLC9i7dy9GRkbWKRr++HrdGk+n044C/o7v+A63XSOrP/vZz7q2Uqp+wZSkiOE4Zg5ILv6hFK7S4aq4xrFDxGai1pVu1msoTZ5Un16XPdV9gGi9c6Xvv/CFL7j2k08+mXg8oZS29k/pdD3Wf7eqAq/Lqeoyn+pK0XHrWPUeaxGcpGI3fm12vcd63iQqP8lFovv4tU2IJFYQSF7GV8+lfUqKsFf0rDBX+BaUCh9/P8K3XOL+x/knlQqPu6Zel7nQKmQBuLKsqVQKY2Nj6OvrQ6PRwMDAACYmJlxNcgpuvqAHBgZcUJkKVT7IpKTpg9V8cs0dT/L7cyx+HIEfF6AWsj8Xem5jTGT8PlS50uVh48q4bgQqbxqtrsJc87N5Xd/V4F/Pz0Tw54cpgCzJW6lUUCqVUK1WMTo6iuHhYVcdj7X06ZKgC6TZbGJ4eBjj4+M4deqU+8HOzs6iVCph9+7dMKazRCrTtFhHQNdB3yiNMCAg4NpATwtzn1qMg76g/Re3wt9HhZr/koxTHjTPmPtrsBQFFn2jQEdTLhaLsLazWhoXXCH1nM/nUSgUnMDWOuT0ofOajUYDy8vLzjfb39/vFhqhkPCjtf0Xf5yV7vuYtT64X05XLZU4zdw/hlDfu66Ep31k7XNVTvxIc27XYDFf6dJrxmnOyuooI6B9GhgYQLFYRDabde12u42jR49ienoa8/PzmJiYwOTkpIsTYAxBrVbD7t27XWR6LpfD+Pg4xsbGXDzByZMn8dRTT2FwcBAjIyPOSi+Xy46VGRgYcAVr4p7HpPsQEBCwPdHTwty33OLaSqVSsPrHqnDjtjhrMi4YitanWr7czgAvFRzGdKLR/Rx2vuzp/6aQYnlSvtDPnTuHgYGBCNWlaXAA3OpkmretlL26AHRudC58a12tcGDN908hrgV1tFIar0dXAY/VOuX63w9o1DlW65vzq4Kd19SiN0nnU8RFJcfR/GoJc+la+rMHBgYwMjLinoVMJhNRrph+yOh1zQhotVrYvXs3VlZWcP78edRqNczMzGBxcRE7duyIFP+hK4cKGpUC3cb7oW6fXkGhUMDLX/5yAFGKlOvDA53CO4TSsX6hkqQa7BrZnkS56/5JkctxLhm/7Z9XnzWl2XWs6jpQepUpjEA02lsj+pUOB6J0rhYk0SIw+ozoeyUpQjwJSdQxACwsLLi20vFHjx51ba2pruPQLAWdM52bpII1ce4xQseUVIQniU7XMei4dc586l+fF31udc7/0T/6R66tUf9/+Id/iAuhp4U54f8gVSjrZ194qw81LqDoQtdUBSHJx6v53wyGoiXPIDj61VXwMHKbwiuTyWBsbAwzMzOuuhyFH/flamFcT9sftwpUHb/PRih82tsXjmotq1Xrz6Xvp+b5OHf8Xmu1+xQyBTcDwLSAiu6nY+M1kwSb318/E4L76Bzt3r0bt9xyi1tzvlgsusj6crmMXbt24XnPex6MMThx4gRmZ2fRbDZdEZ3FxUV3LrpbRkZGXGoiC+JMT0/juuuuQyaTceub8z/jJABEXBS65n2v+88DAgI2j54W5kn+7rh9gOhLWauebeRj30gB8H3FbCvNy+NViLIwCl/cAwMDzt/NAiuk06kB8mXNdCdjjFuhjKVc+QLnKl2k2zVgze+LzpMKbkIFuVLthD8vOmZValSYUyj7ObtUUqjAaJlZCkuf9laL1VfY4nzHG8UJ+POh6YZEJpPBvn37sHv3blcYhuvT9/X1YXh4GLlczjEWo6OjWFpawpkzZ5DJZDAyMuIUsGaz6awOXQ73zJkzWF5eRqlUwvnz5zExMeH6QaWPefycIxXiqoQEmj0g4NpATwtzP1gLiKe+fAGgFHbcvn5UOl/sel29HgU7Ba5u4/6kuSmwSIuyrateab54Op3G4uKie+kPDg5GVmSr1Wpot9sYGRlxQpzLrGr/LhRbELddLWd+rxS6Dwp5PZemCKqSQ1aCvl9fELHf2pd2e23NdLoOqKDE+fD13sZZ6v5YVRHQIjAcB333g4ODSKVSEddFu93G4OAghoaGnCVNoT02NoZarYZ0Oo3JyUkMDAzg5MmTLt2OFQEBuPoCx48fdyutDQ0NuTr8dN2kUp08d31O9DntVas8m83illtuAQAcO3bMbVeKWelHLVriQ3/fqoAmUZwKjXROiiTW+U2KePe/S1oqVanapBrsSkmr20Gj2f3zJ53LV1CJpKh/pacVSa4Mf/+bb77ZtbXojN6Lqakp1z5z5oxrJxX/0d+wRu3rPhshzigBkt+RScu1KnQu41jOuGuPjo66tmY1TE5OJnU9Fj0vzPniVkpZLUNaJz4Nri/8OOvdt+Ljrh1nzamgY//6+vqQz+cjDy4j1Ql+x2A3Y9bKd3IJVAZL1et1zM/Puzrlq6ur2LVrF/r6+jAzMxMpsEKLX/3WjLL3x+MHfvmR6kqHc3+fNvcj331BzrHTz5zL5RwbAUSZAN1fz89lY7l0LMep+/gMgd77jaDPih7P52B4eBgTExMunWxxcdH5xvlCYS32ZrOJWq3mKPZsNusCE4E1n10qlXJBj2NjYxgfH3cxBefPn8fKyoqr+kdBTuVOlSBlT1T5CggI2P7oeWGuwiPOGvGjsVXg+v71uOC4uJd/nMD2j/eFWTqddpHIuvoZj+ELut1eK2PK7Sz/Sc2Qi6kcP37c0fVPPvkk2u22qwZHGpcFW3Qd8400Rl8w+GlivptBj4+zflWY04qkRc4UK6WPeawur6rX1KA3nWMAru660vFxKWgb+c7971ShsdZix44dLgOB1+L91XSx/v5+Z91ReWk2m5idnXU+8kql4laJY755f38/RkdHkclksLCw4JSziYkJjI2NuVXUeD+ZwqjsDhmMuOC/gICA7YmeFeYqMCnI9aULrBdcflCcH1zFNl+Gm7muDz0HEO9bp89bFzYB4ut+t9udZVNZ0a1UKjk/OoPn1AdPwcgUOJYXzefzTtjo9dgnHYsvJOMYDO7nCz8tvOLPr9ZsZxlXXpuV09QKVj8/z+e7P3xFTFed8xW2OHfJRkqb3mcqWc9//vMxMjKChYWFSKwCz0HfNbMYGOBGZY2BkKTjZ2ZmMDIy4uak3W47Ic++cY34kZER1Ot11Go1t647r0sKXp8psjC9hFQq5SK7lT7WyOObbrrJtZXW1AhvIJla10hrLaSiTJn+TpTm9d03hEY9+y4c/d3o86zn1bZS/HNzc66t1Lq6IDSyWovrAMCLXvQi177jjjtcW5c91SjyI0eOuPbTTz8d2yddmlZp/Ve84hWuTVcJofdJ74tmJmi0vW7Xts6H1i7XuvBKT/t0v/Y3yXWq9yhpCVndrpkISW4UIPrcab9e9apXubbOjV/f/kLoWWEORIW1+lTjAqJ8OjjOgveFWNI1/bYKO7USfT+uWumMcPdTWrg2t0Z5qxAuFovOfzo2NoZjx445ipY0uq7z3W53SoeyrQ+bWuA6h3Fsg1Lv3B5HqauiRMFKxUUj0fWHpAVoOC88rx+o6Pu2VCkiBU+hpha6umR0HDr3+qzo/uzHrl27kMvlcPbsWRdgSFcBAw1rtZpTUpaXl7G0tOQs7mKx6Erwtlot7NmzxylhHCe/Y0piLpdzNQTOnj3rlpHVdEetNMh517EGBARsf/S0MOdLLw6+9exbi/4+SQI8Lho4KQBMj1GhTiGlL1YWdAHWgsQYBa2UOgWTph5RQA8NDWF4eBjWWpTLZdTrdedLJu3LPpB29a21JAbCHz+tZK3l7itLGm/guyx0zikceS72mZSzUus+Xc4ocVLZChXcvtbtF7rxx6EWNo/xsW/fPvT19bnI9Vwu5xQJuj4YrEh2RBUbpg2yzn6lUsH8/DzOnj3rshp4f3nfqKBRUeB95Qp0rE3AzAWOifMeaPaAgGsDPSvM44Lf1D/qB6PxGL60faGjiAuk4/H8r8JfA7T8es1qPWqhFVLBFE5Mc6L/1Pe/+1GjLAu6d+9e5PN5PPvss46WnJycxMTERISWp4ChkqD98oWfKj5J9LMKHJ8J0XnTgjLsu45Dg9moLOg86f2jpc/r+hS89l9jEXQN8o2sVZ910XiB/v5+DA0Nob+/H5OTk64EL5ez5f6ZTAbFYtEpJnSVzM7OolKpYHBwEIVCwRUJyufzrjKg+uEZLAd0onM1d1zvJd0w/f39Lp2RCptWw+sVtFotF9WsdbuVllS68tSpUxd9jSRKVZVDpYX12rq/niepPjeQTLMnHT87O+vauiSmFl5Ravf666937Re/+MWRaz/vec9zbaW+9+zZ49o6Vt3+Xd/1Xa6tUe6aQaBzplHZ/vKdJ06ccG0dn0awa7S9ui00e0ELsagbRqn/pEhx/3g/XZhQl45eI+l9o78xvfd+xLvOyf79+1377rvvdm19ttWtshlsmTA3xvQBeBjAGWvta40xYwD+AsBBAMcBvMFau9Dd910A3gpgFcDPWWv/bjPXoKDjSy7OulYB4/vH+b2eT9u+kuBb+b5v3vfZ8zv2j/QnX9ikZmm1U/gNDAy4FzYFhhaIWV5eRq1Wi1DLuVwO+/btQ7FYxK5du5BOp3H8+HFMT09HxkgBqnnccfOqbSofKvi5Dwvb8Ht//jgH6h/neOr1uhNIWiY2ju7ntQC49b05Bp/y9+l0VRLi7q8fu+Bfl5ibm8P58+dx/fXXI5fLoV6vu0h2lnalQG00Gkin0xgdHcXq6iqWlpYwOzuLxcVF5HI5jI6Oor+/31nU7XYb8/PzaDQarhAN54VUvj5T9XodZ8+eRV9fH3bt2oVsNhtZ/z0uRiAgIGD7Yist87cDOASAESbvBPCgtfY+Y8w7u5/fYYy5DcAbAdwOYA+ATxljbrbWbsz/dqGWID8DiLzUFL41rt+rZa+f1aL3j9Hz+IJIA5N8XzIFCAUrBYAuEkLKeXW1s2IaU87a7bbTzBkwdODAAUfdLy0tYXp6GufOnUO5XEY6nXbBcn19fa7+O6PpfXZCc6uV1qbiRGUkSSniZ7XWKRxpiTPSXq3mOEWI8C1qWqC+UhJ3jLoHdD/O80YxEpxjXjOdTmNubs7dU5beXVlZiZTkHRwcdOvOk0U5fvw4stksbrjhBuRyORhjMDw8jOHhYSwtLeH8+fNYWlpyVPvw8LA7H58j5plTUGsUvB+7oJZEQEDA9saWCHNjzD4A3w/g3QB+obv5dQBe2W2/D8BDAN7R3f5Ba20DwLPGmCMA7gbwxQtdJ5VKrSs2wu0qkP3PSQLDt2jUulTaNs7nrt+pRaRCXK18Bn1RqCg1TWtd66dTCAKI+F1ZIMFa64T06uoqduzY4SzHxcXFyMIkqVTK5T+r8qBg/ylMaJ3rvjoGuhhUUdG5Uh84hbha5T7Vr/cpKYKYAot9UAtbr+cfr0penJvF7wsVl4GBAQwODrpIdVUiqtUq6vU68vm8i9pfXV3F/Py8Y1Ly+TwGBwddYBsteqapGWMiqW5jY2PuXmkVvP7+fuRyuYjiRkVNU/a4rdfA/isVqVHMSjFrkQ2/gIxGsKsyl6T8+XUg4o5NKjSyEc2ulHHSspYaLa50ulK+eh6ljzWC3a8HrpHgOj8axa/1zjWyXedW+6p0sY5HC734mQV6/5R+1ohtPa+OW+dTr6fPQVLRHaXJ/fPqfklLzep2fVb8Gvhx+/v17JOofM0gUJdHUmGaJGyVZf7bAH4ZgJbi2WmtnQIAa+2UMYaL0e4F8CXZ73R32wWhAU0qPDjpGk3t+9N9ilzhB3bFRVDz+knf+Var3w8A7sWtAo2WuL9oRjabdb479S/zGE2VYOrT8573PGSzWRw+fNj9UHj9dDrtrkOfsC94dVwKXV5U08l8xSXO4lXBqwoT72HcMRRQcSyAVswjqDDQ+veFuf75Y1VBzrllFgDpbVZuo9But9toNBpuzKurqyiVSpibm0O9XkexWMT4+DgGBgacAmWtdUIagFt8pV6vY3p6Gnv27EGxWHQvO84BFTkGwk1NTSGVSmF0dNT1h3OykSslICBge+GSC3NjzGsBzFhrHzHGvHIzh8Rsiw3BNca8DcDbAGD37t2xQVcqyH3rMM7S1rYKeLUMk4SS9Gud4PaP9YWNBnpRoGokO60zDZID1jQ/3wrly9t3Bezbtw/pdBqHDh3C7OxshA3IZrOoVqsuOpu56CqY+acWOudM08z4x4pzjAfQCGsdux+TkOSnBtasHo09IDTAjn1hECFdErqKWBxboP5//x5RyQA6ebCnTp1yvvCdO3diYmLCFXJJp9Oo1+tYWFhwyhPLs1IBm56extTUFPbu3Yvdu3c7JaFYLCKXy2Fubs65P8iycEwAnEVvjHHL4y4sLLjMDj/w7ULZCgEBAdsDW2GZ3wPgnxpjvg9AFsCwMeb9AKaNMbu7VvluACy2fBrAfjl+H4CziIG19r0A3gsAL3jBCywFgf4B6/3lag3623mcL8AARCxHFcZxUe2+UpFk+aswpIAkRe6fC1gLMGM0vAo/+p75wqaATqVSqFarMKaz3OquXbswMjKCz3zmM1hYWHCRz/Sl0yJsNpvIZrORBTzYV/73A8lUaGiAGn27tN6Vfo+zFvUe+NayVlZTf74KZlWMNGqfgk1ZGkVcdLsqW+zbysoKSqUSSqWSW96Uihcr+2kKGYVtX1+fc2k0m023JjnneHl5Gc1mE8Vi0SlW9JnPz887wc1raM39oaEhTE5OYmFhAXNzcygUCsjn8+uyNgICArY/Lrkwt9a+C8C7AKBrmf+StfbHjTG/BeDNAO7r/n+ge8hHAPyZMeY96ATA3QTgKxdxPXSvBSAqyDd6kcUJaV9gx9Hv+j8uap1WuU/PE76lREHFvOQ43zRT1+hPIT3Ofvh+Y1rGDNDq7+/H3r17cdddd+FrX/saZmdnUavVnIArFAro7+9HtVpFtVp1Qt1falTHHucXVJeBRs2rH4/CnPNDhYbwaXG9rrIpug8FOOMM4oIN/fQ4Uug+qBBQcTJdP7bei3a7U53v6NGjePbZZ10Q4sjIiNuXVaiYfVAqlVzk+q5du1yKmjEmsjxqLpdDqVSKrIaXyWSQz+ddOhwVn0aj4cbGuY6LAbgcMMYcB7CMTkbKirX2LrNBBksSOLdA1Ieq/ltVBjWVSv3DQNTPG/dM+Ujyd/pZGnHnSUpV8j/rM6f907SspIVg1E+r/nP1rfrpTOqD1XbS+t363tEFS7QfOu4kP7BfvUxTrtQnrX1XV+Fm/OQ6HzqvG733k1IDFTq32tb7qHOpSPLJA9Fx6Hmfeuop19Y519TMzeBy5pnfB+BDxpi3AjgJ4PUAYK19yhjzIQDfALAC4Gc2G8nuv9xpOWqUsgpVFdBaq9y/qUn+WRXWSfSlRq5rZDv7yD7pg8EiL3xQuWY1feUa2MRzqD9X63AbYxxdToFvrUWhUMAtt9yCwcFBPP7443jmmWfQbDaRz+ddShOLkXDlNS6ComNTAQnACT11BdBvDKwpGhRk+qdzxHlPug+6jX8MCGN1O/ZRGQB1R2iMhV+Zj2PhOTkfPE4teAp4rmqWTqdx3XXXOaWL/vHl5WVUq1XkcjnccsstWFhYQKlUQqVScZXhyuWyc3Ow1O3IyIhjVziHQOflxR87Uxfb7U4J2YGBAWSz2QgTkvSy2kJ8l7V2Vj7HZrBc7k4FBFwL2FJhbq19CJ2odVhr5wDcm7Dfu9GJfL/Y80eKlvDFHRcURajAITZDRfqUpX+MWptKF/t+ffXFq3CmQKVAoVJCoUdKncex4Ajpcka7Ly0tuVSofD7v/K0saPL85z8fBw8exNGjR3Ho0CGcPXvW5ayTpmdA19LSUoQW1jgEjc6ndU0/Oq1bYM1VoelsOieqrfqBczqfmsKmufl6rNLp+lxoP5Ki52npMkpdFUHOIcfI5441AUZGRrBjxw4X6T44OOjmivstLi66hVNWVlawsLCAXbt2uVrthUIBw8PD6Ovrc/M+PT2NnTt3uhQ1Pt/sGxUm5utrUCSASBXAK4SkDJaAgIBLjJ6tAAes90v7/m7fRx5HvWvAWJzPXK0x/1x+P9jmZz9VS6HChQKRdLEqKbovr6uWMRUA+olp4bfbbWfxaf9I095+++24+eabcfToURw/fhxzc3OYmZlxaU8cK/28lUoFAwMDkQVbfEVKLVjfB65shu935ziSlKo4X7qvVPBY/zMj2+OEOPtEZYdKFe8Ho939inSpVGct9t27d2PPnj0uzUdzzdkX1uGvVCoRt8LS0hKMMU5QVyoVVCoV5y4AOtTmrl27kMlkUK/X0d/f71wifF5nZmZQr9exuLjo0t80mj2JDtwCWACfMMZYAH/QjW9JymBJRCqVcqlSygolVWfzj1UolZmU3hgXz+IjicJNYj788ySlsymtnJSKpX1NoroVSmf7x8fFwfh9SmIck6qlJV3Lh15Dj086r/ZV6XRdt1yfCZ0/TfNTl4XfxyQ3ifY1Ka0wyXWSlO4GRCsJqntIXUjnz593bXVHbAY9K8yTfNr0aTKwyPf36otez+H73tV61ohm/4FVCp2faY2xtCcfCL/P2h+1ehnFToFI4ejnkJK+JrXOYCta6fl8PhIMx9XVarUaCoUCBgYGcP311+PAgQMol8uYmZlBqVTC1NQUjh07hoWFBWdlN5tNt+AHhTpdAOo24Hz4PxqltpnqBaz5r/2XatyLIekaSe4W/rB04Ra1zlV5YkyCMjvch1HlfX19GBkZwR133IG9e/e6OazVaq44DO8JAKcYjY2Nob+/HwsLC84q57yShh8cHES73XaW+9DQEPbv349CoeBeVMxzB+AqA/b19WFoaMixKVxsha6Py1g45h5r7dmuwP6kMebpCx7RhZEslR07LijvAwICYtCzwlxpa/5ne2ZmBh/+8IfxzW9+0wl3pXQpWNSyZGoPBQOFDAO7ADjByXMojc70I2DNbz48PIw3v/nNrl6yr8GxT7S0lAnwfcNK+WpeN5fO5LUHBgacJbqysuJo8lQqhXK57ARWX18fFhYWnCKQyWRw8803I5VK4cyZM7jppptw/vx5nD17FlNTU5HiJ/V63VUqy2azGB4edpXQdE5JBVMRies/fd5UmHxWgp8ZUc7jKdhpSav1z33or9ca6ysrKy5tDVgrwEM3B++/FrbJ5/OYnJzEgQMHXEoZ96Ffm+VcNSZDFUfOA90V1loX8Lhnzx4MDQ1haWkJqVQKO3bswOTkJEZHR924MpkMCoWCW0WNcQ3t9tqiO7TKATjXwOUS5tbas93/M8aYv0an8FNSBot/rMtSufnmm8PKMAEBzwE9K8yB9ZHr/KvX69i/fz/e9KY3IZ1O4+tf/7orolKtVvG3f/u3+MIXvoDv/u7vxnd/93fDGIOHH34YN998MyYmJnDq1Cncf//9OHXqFN7whjfgpS99KRYXF3Ho0CG85CUvwcDAAJ588kk88MADOHfuHG6++Wa84hWvwOc//3ncdNNNePWrXw1rLR555BEXscz+qj8TiAbVMZ2LAheIUj6kzrmetQoPCsdms4kzZ85geXkZ1lpXKWpxcRG33norduzYsU5JWF5exoMPPoj+/n689KUvdX2jgtFoNPDMM8/gT//0T/GNb3wDCwsLjvmw1uJf/at/he/5nu9xdDP7dPr0afzBH/wByuUyfvmXf9lVqlLW4/HHH8f73vc+zM7ORgQ2x5tKpfBjP/ZjePWrX43Pfe5z+MAHPuBotF27duHtb387brjhhnVK1he+8AX8yZ/8ibN0aTlTsJM58SsIKguya9cu7Nq1C/v378fY2FiE5qR/HUDkful5qNQ0m01UKhVUq1VHtdVqNSwvL6PVamF+fj6S5jYwMOAEMYvf5PN5dx5VfFhshvXcWepVgye3GsaYAoCUtXa52/4nAP4fdDJV4jJYEpFOp10Ur7I1Wr3Mr/RGKF0JRGnOzdC8SqPqtZOqu+k+es4kuh6I0sEaze5XCyOSaF6N6tZ++FXD9LNWQ9Nr6/h0zpSu1vMkVUXTZ81nEfWzukl0e1I7yb2g85xEdW+kzCqjp+3NRLMnRfEnzat/Lp1DreD3whe+0LX/9m//NrHvcehpYa7Wq9K3rVYLo6Ojjh7NZDIYHR3F0tIS2u02du/eje/8zu/E+fPn8clPfhJ33XWXywl+5JFH8PDDD2PPnj3Yt28fJicn3Ut3cnIS7XYbn/70p3Ho0CFks1lHkQ4NDSGVSuHw4cPo6+vDS17yEhdVzL76FD+wPt1Nfb5K2dLy5X4URPSX5vN5DA0NOYq9WCxicnIS1locPnzYvfi5SlGz2cTg4CAOHDjgrMa+vj4MDw+jUCjg5MmTOH78OM6ePYtsNotKpYKdO3di165deNnLXoaFhQU88MADOH78OO666y48//nPx8MPP4yPfvSjePnLX47XvOY1WFxcRKlUQrvdxq5du7Bjxw587GMfw9e+9jVXVGZ6ehozMzPIZDLYv38/Wq0WTp486V4efX19GB8fx8GDB/Hoo4+6SHBa3h/60IfcSzydTuMHfuAH8KIXvcgxEfPz884PTSGoAZK0fPv7+7Fz505cd911yOfzyOfzTmHKZDKO4VH2hZHk/v0ko8O8cFLiml7WaDRQLpdhjMHCwoJLM+NCLWRA6CfXCP1sNouVlRXMzs6iVCphbGzMFQdSVoTKwGXATgB/3X02+wH8mbX248aYryImgyUgIODSo2eFOS1LwhgTKVIyMDCAU6dOIZvNuqIdJ0+exOjoqHvJ6apdFLpUANTvePz4cUcp089KIUMfMoUoi68AcDnCcfADaTRQzHcHtNvtiKaqUdoUDPw+nU5jx44dTlitrq5idHQU4+PjrjiMBs8Ba+llXIyF161Wq04wZbNZNBoNDA4OOn/xrl27UK/X3QpgR44cwUc+8hEMDw/jn/yTf4JarYZSqYT+/n43r7VaDeVy2QnDxcVFLC0tOeWjUqmg2WyiVCo5pYbCsNVqudQ5WtA8P+9huVx2kenlctkpVGqx0udPv//g4CDGx8fd+vAahc5AMp4/n89HnhENcvOVN2YB8F6vrKw4y7pcLrvAuFKp5FLSisWiS+NrNptuIZbR0VFMTk46JoVV5lZWVlwkPRVG312w1bDWHgNwZ8z2xAyWgICAS4ueFeaEH/xEP7QKRwZEra6uIp1O4+TJk3jiiSfwwz/8w7j33nsxPz+PqakpTE5O4rWvfS1uu+02fOADH8DMzAwOHDjgioIsLy/juuuuww//8A/jiSeewN/8zd9gbm4uYlW94AUvwKtf/WoMDg7i+PHjkUj0OGjwl0/VknbX4+MWRtEIaio1tVrN+cTT6TSq1Sp27NiB8fFxJ1QBuIC5yclJp3ikUins3bsXo6Ojjn04cuQIHn30UTSbTXzlK19BqVTC4uJipMgBU9tIAQ4MDGB0dBStVguzs7MoFAq45557cM8996BYLKJYLOJTn/oU7rvvPpw9exb/8A//4GqTMyqXkdy6MEu5XHa+5n/+z/85XvjCFzrBOz4+jmw2i7GxMUxMTETy2rlEKa1tCtvBwcEIJU0/Pek9rVvPz/Rjk+qjRc3501RDshCck8HBQYyNjbmxMO1tdXUVc3NzrpobA+4Y08G0w8XFRczOzqLRaODAgQPYv3+/c1Ho72Kj6OKrFcYYp5gmLbyhQXIaIXz8+PHIuZKit7WdpOwk0ex6zqR9/HNuhg7W7aq4ayS3tpV6VvjUblKRlKTFSJKi6vV6GlGu231qXaFzpfsluTb8QlOEZgAkReE/l3TMJGpd+6QU+maUZP9e6MJBN998s2vrmvMbzeGF0PPCXK1qpVFbrVYkKIuf6/U6brvtNtx8883Yt28fvvKVr+Av//Iv3WpWqVQKjUYD3/7t347V1VVnLaZSKTz66KP40z/9U/zIj/wI0uk0Xv3qV2NiYsL1xVqLT33qU3j88cfx2te+1gU0+X7tuEhvtrX4C7C2ahm/55i5LZvNOjcAwSj+AwcOwBiDqakp1Ot1d/zs7CyeeuopV+WtWq1GKrhZa3Hs2DF84QtfwAtf+EK89KUvdefet28ffvRHfxTNZhO///u/j5mZGSd4CoWCqzFO5SabzWJ6ehr//t//e2dht9tt/PRP/zRe//rXY3FxEfPz87j77rvxS7/0S2g2m/it3/otfPGLX3QvTtLitKaLxaKz9u+77z43t4VCAW9/+9vxvd/7vajVak4Y0jKnssdKa7VaDel0GoODg45V4ZzofVtdXXXlVEmtU0GkEAbWfohkV/R4rR+QSqUwPDyMwcFBrK6uutXtKpWKU1xSqZRzobRaLUxPTzsrv9lsIp1O4+abb8Ytt9zi3Ct6PQYJ9qJADwgIuHj0rDAn1cwXll9Mo6+vD3feeSey2Swee+wxjI2N4c4778Ty8jI+8pGP4Itf/CJ++Id/GC9/+cuxf/9+HDlyBLfeeitGRkZw4sQJ/Pmf/zmmp6fx0z/907jjjjtw/vx53HHHHXjDG96AHTt24Otf/zo+/vGP4+mnn8Z3fMd3uDSm7/qu78K9996LwcFBfPazn12X4haXH+rnO/r76As5k8lEXtyZTAY7duyIWPUMAmRtby7NSct7bGwMd999t1OCKpUKTp065RQhYwwOHjyI8fFxZ2Uzhz2VSuETn/gE5ufnUa1WMTEx4azae++9Fy9+8YvdCl60iJ/3vOfhZ3/2Z50PH+iUO83lchgYGMDY2BhOnTqFX/3VX0W5XMb58+dd8RoWv6GSRfq6r68PN910E972trfhhhtuANCxmnbs2OEKtwwODmJ5eRmFQgFDQ0NOoDLOQZVAKn602LkK2sDAgHueaBnRf85ock2zo8KgbiBdvpbzQr93s9lEoVBwjAkVHtLu9LPrsz42Nobdu3e71Di1oJiu5hfNCQgI2N7oWWGu6WUa+c2X6fHjx/Hss88C6KytWywWXfrPoUOHsLy8jEcffRRnz55Fo9HA4uIiTp8+jf7+fpw/fx7Hjx+HtRYPPvggHnnkEZRKJTQaDUxPT8MYg9OnT+PEiROw1uLMmTOYn5/HwsICTp48iU984hOoVCool8u4/fbbXV/9l2tSrnzSePlH6p3R2RQaFDC1Ws19T0bCWutqhNMPzChvUsGVSgWPP/44hoeHkclkHP2WSqUwNTXl1tfWBUdGR0fxyCOP4DOf+Qzq9bq7VqlUwsmTJ7GwsIBcLoePfexjLuqbDEC9XsepU6dchP7p06edFUx63VqLv//7v8fhw4fxzDPPoNFoOCWhXC67+0OrmHNx/vz5iJLHPHIWwOE1GNVO1ob3gBS8pv2xohrvI5c0ZVxBq9XCwsICVldXndBmOiOFru5PxZOUPABX8EcL8aTTaYyMjGBwcBDDw8MoFosu5kCDJckQ+ClxvQTGtgDROuO6/napVHJtBnQCUQoWiNKlGruiLJYqQtrW/ZNo3iRFKek37H/nu27ioH1VOjyJbvZdekn0sVL2WgN8bGzMtbm+ABB1Z+j659rW+fALnuga7VrYRseUFDGfFPWv+yQVffHn1Y9Vijt+M/sn1f3Xtp9Jcueda2Eld911l2ur20jXgT9w4EBsn5LQs8IciK6mpTnODGSanp5GKpXCxMQE+vr60Gw2MTAwgDvvvDMS2MSgMQqZYrGIe+65JxIgxsAovjQZ2a351Pv373dWW19fH2644QYMDQ25oCSeH4gWp9HtcQ+VKgN88TMlSS1/vtwpmJaWlrC6uupqhWs6FKuQZbNZjI+PI5PJYG5uzlG4y8vLLpJ9aGgIrVYrMha6EKy1eOaZZ3D8+HE0m03kcjnU63VMTU2hUqk4peGTn/yki85mcZt6ve7K0ZKKpqAmS8D0tccffzzyA0qlUpifn8enP/3pSOlarmFOVqFQKLhzDQwMuHvKNlkOCnbGXGgaIdMLWXYViNbP5/G8pyy922g0kE6nMTk5if7+fiwuLq7zSXLcFOB8jtnH0dFRFItFjI2NubHU63VUq1WnOHGcfA61Vn9AQMC1gZ4V5ppXrpXSjDHYuXMn/tk/+2cuMCyOur4S/QWSNcCN6FCOURUBpXa5jb5a/0XebDaxvLzsUqEKhYIToBSsjCIn/Tw1NRVJp+JKYhQoABxdzQpzY2NjyOfzqFQqqNVqkfxr9onWgQYk0gqiINf8ad8CZR4+c+xJK5Pu5hxp6dl2u41cLhf5nj59LS3L86jPW1PZOM9UAsiUqI96YmIC1WoVpVLJBdyxABF9/syk0CBNAG5sVErIDgBwPnUyMUNDQ866VxeOrqxHH31AQMD2R88Kc6XZ9SV/NQnwOPj55RdznL60OUYKBgpOLQWby+XcS52rahljsLS0hOXlZbdACwUFACdMrLXYsWMHisWiKxDDqO9KpYJMJoPh4WHk83k0Gg2X5kW/OiPG+/v70Wg03HKe9E0DcP3iXGgte3VJqEClgFcXBZUCCrfBwcFIjIAGRvJ6TFGjm4Zt+srpO2e/eByfuUwmg0aj4eqpkzFhbXxVYjSQj/eN/aLSwuh5pczpw9dgOjIvGtzJOeBccaxXaOW0bwmNRgPf/OY3AUSLw9x2222u/cQTT7j2zMxM5FhFEg2rdLMi6TeZFHGdRAX7CpQfE0NoJPhG9Cyh9zKJ7vejoZMi4JVOV5qd1Q39/ZOUQu1r0jwBiAQKq5tE6XfNXpibm3NtZbP8exx3vY3erTrPOp96jO6jc5v0POn+2tfx8fHItW+88UbX1nFoFob245577kkcRxx6VpjTr0mhocU9rmZs9KCpgIqrT64V7yjYdWEUTcXT+uK0SCkABgYGUK1WHR3MKHTmQc/Pz6PRaKBYLKJarbpAOroaFhcXkc1mMTEx4XKmx8fHnU+Zwo33h7ngg4ODzndM3zbpbC2NS2GqVjMFE33QHAewFpzHoDKtokdmgUKRUfCtVsuxEMwOyOVyLqMhn89HStDyueKccd6z2Szq9bqLVWAuOs/NXH2eg8qOpo6ReaBiwfvLbTyex3I++LzrvPnPSkBAwLWBnhXm/f39KBaLEWuOlOx2iOD1x6B0MwU5sEb7AtH10rUeuVrzSkMzarvVaqFarTrBw5W5SqWSy2tmTXEqUUyRIhvACnj8vlgsOmu31WpheHjYUeHMGfdz7H0BT/89BbFS0rlczgk3XfKVAp8R6uwnhSm308etQlUVI84hz6cLqWQyGeRyuYhlPTAw4FwQWnCGfdTgP46R11arWi1qsiq0xvWZYES8ujL4DGj8xXb4LQQEBFwYPSvMfUtEa2H3Ki5kSfnBc1q9jsKDAjKbzTpBpOtcM1+bL31a77lcDu12G9VqFQsLC5FgrGKx6K7tW+pAh7bkWtxUDFTQ5PN5jI2NOcWCudS6uAmVCgBO4JP6p/Alnc4KabRM8/k8crmcE9BUAPQzBSvPTaGsND+wVkQHQMT/TKpdKwU2m00XWe7X2uf8kjGgxc+SsBppr88st1NxIavA+8nnXK1yfX7oimC/L+OqaZcEjUYDJ06cABClY48cOeLaGimtNK1Ss0CU9tWCHVrXPKmQikLZPm0nuTCUcgeiv2uNkk9iETfjHtCxbVRMRqPQlULXJTi1CEySGyGpHnvSPj42s9Spzo1GySfR3kkFbvQ8ut3vY9LStkkyRPuatKytju3222+PHH/w4EHXPnPmjGtrFL+uL6AFuTaDnhXmpGppxfjCfbuBQglYe0nrQ0sBwqh3jSVgdTIKdJZq1VKnwFrhlcHBQVculAKEdH06ncb4+Hhkuc+ZmRnUajWMj487AcUccgpvjRSnNcpzAnDlS6mcsK90G7CP6n9mCVN+pk9cg8HU36wpYIwIJ2U+NDTkVpcjKOyZ8kelaGBgwAWgMcIfgJsTrgTHfHhdeY8KBf9ba10qoS/YGQegfnayHXpOZV8YT6ABegEBAdsfPSvMAThhpC8x4OKDy3oBSqGrS4ECqa+vz/lpWXyE0eO08EgLA3ACRZUgfjcwMIA9e/Zg165dWFhYwKlTp9BoNDA/P49arYZ8Pu+CZlqtFnK5HCqVigu+A+D6YIxBq9XC8vKyqwznWyRqHVOYk2IHotH8qVTKReMDa8vNkkVQ9wID9ejH5nmZe890L85BvV53ikO73UalUnER/ePj425fVR7JfPj+azIGwFqUvGZgcO4opDUgj/3nPVJ/vY7ZV2CpePF8vA8BAQHbHz0rzNXiAxChbCnUe5ly9+GPhQJjaGjIsRRK26qFyhc8hSH9vTxOS5KSUqZlnMvlcPDgQbegC6O3y+Uyzp0753zJjJznWtvsG619llVl8Zq+vj6XDqfR4j5FqcKVUL+5Wu/cv7+/31VH08ItFLqkvFl3gMVfOIeM1FdqX3Pf1SfNjAC6OrgfU/mAaG139pU+ebpENE9d6XU/JU6P5/3TmAmNq1A2p1fAvHwgSkUmUeuk5IH1NK9SzOfPn3dtLdKhimVSRHPS8qZJ1K5vTCgdrMuK+u6VuPMmRYirq0DH6d9vpWp1P1XytHCLXlvnXAu9KC2cNB8+va2fNxN5rvOp/dZ50j5pdLhey49+177rfdV5Tqp7r1DGS587zSbQ+usAcPToUddeWlpybS3OoxHwus9m0LPCfHFxER//+Mfdy1hzgTcqwLLd4Aszbov7kZB6pbVOgc4/CiAVWBT0tVotIvwrlQrOnDnjVinjORntTT+6WrAAXNlbnoMCjddhwBcpawoxvnworBiVzm1Kx2uaGq/J62qUdyrVqZHOHy+PoZ9bCwDx/GoVk+7Wwj3MQddrxdXXZ8AfFdB0Ou3ug2YjxClxPH51ddWt4OfnrHPfqz27IyAg4NKgZ4X5yZMnyz/1Uz/1zJXuxxXEBIDZK92JK4Qw9ovDxdWFDAgI6Dn0rDAH8Iy19q4L77Y9YYx5+Fodfxj79h17f3+/o8H37dvntn/uc59zbaVOX/nKV7q20tlAtOiMfqdR76Ojo669GZfExRacAaLR7Uk1vfW8StVqO+naSpkr/Q5E3Qh6baWbk+qmazZBEs2u51RWyK+JrrS59lEp7YtlkZIK1uh4/GfCd+PFHa9sWFL2QlLmg7o1tDAPEM0g0EI9StPrGgQXG/u1/XnogICAgICAbY4gzAMCAgICAnocvUyzv/dKd+AK41oefxj7NoXm2mtk77333uvaSkUqxfzggw9GzqU1r3ft2hW5BqGUrBbsSFraU6G0t1L/fjqgRmMrbavUdVJhFKWnk5ZlVSQFTALR6HSN7p+dXQvBSIoQTyq2khSFvxH0GJ0bpah1npLqqSvVrRH5GgXu0+FKp2+G+tf581dtJPQ50O1+EaOkOdS+K/SZ3Qx61jK31m7rl9qFcC2PP4w9ICAgIIqeFeYBAQEBAQEBHfSkMDfGvMYY84wx5ogx5p1Xuj+XGsaY/caYvzfGHDLGPGWMeXt3+5gx5pPGmMPd/6NyzLu68/GMMeZ7rlzvLw2MMX3GmMeMMX/T/Xwtjb1ojLnfGPN09xn49mtp/AEBARePnvOZG2P6APwegO8GcBrAV40xH7HWfuPK9uySYgXAL1prHzXGDAF4xBjzSQBvAfCgtfa+rhLzTgDvMMbcBuCNAG4HsAfAp4wxN1tre7mW59sBHAJAB+k7ce2M/XcAfNxa+yPGmAyAPIBfwVU8fmNMEcAfAngBAAvg/wDwDIC/AHAQwHEAb7DWLsSfoYNMJuMWpNDFUdR/q2uYT09Pu7am/gBRv7D6QdU3npSKlbTohyKpgpufQpaUOqb7zc/Pu7b6UNVHrGl06l/WhVb89C7102r1Ob2e+nb12npsUtpZku/eXxNAfc9JVdx0njQlT+dMz6PPRNKc+dXc9LPOm96LzaTI+UW64vqna7gDyYvV6H5TU1OufbELrfSiZX43gCPW2mPW2iaADwJ43RXu0yWFtXbKWvtot72MjlDbi84439fd7X0AfrDbfh2AD1prG9baZwEcQWeeehLGmH0Avh8d4UBcK2MfBvCPAPwRAFhrm9baEq7+8VMBuQXAneg8s1TAbgLwYPdzQEDAFqAXhfleAKfk8+nutm0JY8xBAC8G8GUAO621U0BH4ANgkentNie/DeCXAWh47LUy9hsAnAfwP7puhj80xhRwFY//OSggAQEBlxg9R7MDiCuLsy3XeTTGDAL4KwA/b61d2qAi0LaZE2PMawHMWGsfMca8cjOHxGzrybF30Q/g2wD8rLX2y8aY38HGFu3VMH5VQO4E8Ag6bpKIAmKM2bHBOQB0KFwuPKGpQJpCpjSoUqV33nln5FxagevQoUOurfSsUtdK+SrdqfsoJZqUTuZTu5oepVSrpmXpubQfmk6WhCSa1/+s19Y+JbkLks6blIK2UWqazklS6pfOh+6j/VC3ytmzZ11bx6NuET2n/1mp9aT0t6TtWllOr63j+fKXvxy59tNPP+3aurb5gQNr1Zb1XJu594petMxPA9gvn/cBOJuwb8/CGJNGR5B/wFr74e7maWPM7u73uwHQebid5uQeAP/UGHMcHRfKq4wx78e1MXagM57T1lq+Ce5HR7hfzeOnAvLfrLUvBlDBRVDqxpi3GWMeNsY8rEI7ICBg8+hFYf5VADcZY67vBge9EcBHrnCfLilMR737IwCHrLXvka8+AuDN3fabATwg299ojBkwxlwP4CYAX7lc/b2UsNa+y1q7z1p7EJ17+2lr7Y/jGhg7AFhrzwE4ZYx5fnfTvQC+gat7/BergERgrX2vtfYua+1dagUHBARsHj1Hs1trV4wx/xbA3wHoA/DH1tqnrnC3LjXuAfATAJ4wxjze3fYrAO4D8CFjzFsBnATwegCw1j5ljPkQOi/9FQA/0+PR3HG4lsb+swA+0FVWjwH4F+go3lfl+K2154wxp4wxz7fWPoM1BeQb6Cge9yGqgCQilUq5SGal2TWyd+/etZAA3cdf3EMXYVHq9Utf+pJra4S3KhJ6LqV2NQpcK3SNjIy4th8NrZHx2l+l+5Xy1ehtpdw16lzbGp2vVfOAqKtBx6TuiaQFX5SWT2or9Pz+vVDaXKn8pCh3zSzQser8a7/1+dD50HH619NxbKZ6nUbMa1vHqtv/4R/+IXK8Vi48c+aMax87dsy1dZ70udkMek6YA4C19qMAPnql+7FVsNZ+HvG+UKDzoow75t0A3r1lnboCsNY+BOChbnsO18jYrbWPA4hbGe1qHv+mFZCAgIBLj54U5gEBAVcXLlYBCQgIuLQIwjwgIOCqQSqVitCRhEYPa1SxFpbx16rWwiN3372Weq8R6UqFLi4uujbXVAei9PuRI0dc+8knn3RtRuAD6xfIUKpX+6TUsNKrul540traSQVg/EU7lK7W66lbQF0QSVHrGmWt86+FXpLW/vbPpe4FXRQlaf107ZPOkz4n2lZ62ncJ6HOU5C7QcegzoX3Se5pEs/tQF8bnPvc519aFbl784he79k033ZR4rjj0YgBcQEBAQEBAgCAI84CAgICAgB5HoNkDAgKuGvT19TnKVGlJjWhWWlIpYp/aVTpXqdcXvehFrn3HHXe4tlLo2lZKe/fu3a6tNO2pU2tF+LSYCRCl9bWdVCRFx6S0vG7XPikt77salPZVyliL4iiNnbR+utLKScWrdP79gifaL+2vzqHS1eoSSJon7Z8eq9f2+5HkrlE3gs6t7q/zr229hh7rr6WeVGf/tttui21rVP5mECzzgICAgICAHkcQ5gEBAQEBAT0O41NTAQEBAVcKxpjzAE4AmAAwe4HdtyOuxXFfi2MGLm7cB6y1kxvtEIR5QEDAVQdjzMPW2ri89W2Na3Hc1+KYgUs/7kCzBwQEBAQE9DiCMA8ICAgICOhxBGEeEBBwNeK9V7oDVwjX4rivxTEDl3jcwWceEBAQEBDQ4wiWeUBAQEBAQI8jCPOAgICrBsaY1xhjnjHGHDHGvPNK92erYIzZb4z5e2PMIWPMU8aYt3e3jxljPmmMOdz9P3qhc/UajDF9xpjHjDF/0/18LYy5aIy53xjzdPeef/ulHncQ5gEBAVcFjDF9AH4PwPcCuA3Am4wxt218VM9iBcAvWmtvBfByAD/THes7ATxorb0JwIPdz9sNbwdwSD5fC2P+HQAft9beAuBOdMZ/SccdhHlAQMDVgrsBHLHWHrPWNgF8EMDrrnCftgTW2ilr7aPd9jI6L/e96Iz3fd3d3gfgB69IB7cIxph9AL4fwB/K5u0+5mEA/wjAHwGAtbZprS3hEo87CPOAgICrBXsBnJLPp7vbtjWMMQcBvBjAlwHstNZOAR2BD2DHBof2In4bwC8DaMu27T7mGwCcB/A/uu6FPzTGFHCJxx2EeUBAwNWCuOW4tnW6jTFmEMBfAfh5a+3Sle7PVsIY81oAM9baR650Xy4z+gF8G4D/Zq19MYAKtsCVEIR5QEDA1YLTAPbL530Azibs2/MwxqTREeQfsNZ+uLt52hizu/v9bgAzV6p/W4B7APxTY8xxdFworzLGvB/be8xA57k+ba39cvfz/egI90s67iDMAwICrhZ8FcBNxpjrjTEZAG8E8JEr3KctgeksCv5HAA5Za98jX30EwJu77TcDeOBy922rYK19l7V2n7X2IDr39tPW2h/HNh4zAFhrzwE4ZYx5fnfTvQC+gUs87lA0JiAg4KqBMeb70PGr9gH4Y2vtu69sj7YGxphXAPgcgCew5j/+FXT85h8CcB2AkwBeb62dvyKd3EIYY14J4Jesta81xoxjm4/ZGPMidIL+MgCOAfgX6BjTl2zcQZgHBAQEBAT0OALNHhAQEBAQ0OMIwjwgICAgIKDHEYR5QEBAQEBAjyMI84CAgICAgB5HEOYBAQEBAQE9jiDMAwICAgICehxBmAcEBAQEBPQ4gjC/CtFd3/iVV7ofVyuMMWVjzA2Xet8LnOc3uqUnAwIuiPAbvrphjDlojLHGmP4r3ZdLhSDMLzOMMceNMa/2tr3FGPN5frbW3m6tfegC5+n5h9EYs88Y8wFjzJwxpmKM+Up3MYYNYa0dtNYe28w1LmbfgIDNIPyG12CM2W2M+SNjzJQxZtkY87Qx5je7q4IFXEYEYR4Qi61+wRhjxgB8HkATwO0AJgD8/wD8mTHmR65EnwICthMu02/4iwByAL7dWjsE4LsBFAE873L351pHEOZXIVTzN8bcbYx52BizZIyZNsZwUYbPdv+XulTytxtjUsaY/9sYc8IYM2OM+VNjzIic9ye7380ZY37Vu85vGGPuN8a83xizBOAt3Wt/0RhT6mrev9tdAIPns8aYf2OMOdzVyv+9MeZ53WOWjDEf0v09/DsAZQBvtdaes9bWrLV/DuDdAP5rdyEKXuNnjDGHARyWbTd22+PGmP/dvd5XjTH/QS0kb98/Mcb8njHmb7v9/bIx5nmy7+8YY051z/WIMeY7n/NNDLimcY38hn8BwDKAH7fWHgcAa+0pa+3brbVfl/P7v9+fMsYcMcbMG2M+YozZ4/Xn54wxx4wxs8aY3zLGpLrfvcUY8wVjzP9njFk0HRbgXjl2xKyxBGe674K+7nd9xpj/0j3nMQDf/xxv7VWLIMyvfvwOgN+x1g6jo+1+qLv9H3X/F7tU8hcBvKX7910AbgAwCOB3AcAYcxuA3wfwYwB2AxgBsNe71uvQWZ6vCOADAFbREboTAL4dndV+/o13zGsAvATAywH8MoD3dq+xH8ALALwpYVzfDeCvrLVtbzsXHrhZtv0ggJcBuC3mPL+HzvrAu9BZeejNMfso3gTgNwGMAjiCjvJAfBXAiwCMAfgzAH9pjMle4HwBARfCdv0NvxrAh2N+wz5+EN3frzHmVQD+XwBv6I7hBDrLoSr+GYC70Fkm9HUA/g/57mXoLFQyAeDXAXzYdBgCAHgfgBUANwJ4MYB/AuBfdr/7KQCv7W6/C0As+9fTsNaGv8v4B+A4OhZpSf6qAD7v7fPqbvuz6AifCe88BwFYAP2y7UEA/0Y+Px9AC0A/gF8D8OfyXR4dipvX+Q0An71A338ewF/LZwvgHvn8CIB3yOf/CuC3E851BMC/itme1fN226/y9rHo/GD7uuN7vnz3H7y5tABu7Lb/BMAfynffB+DpDca7AOBOmZ/3X+nnJ/xd+b/wG3bfHY77DXv7RH6/6Cz7+p/l82B3fAdl/9fI9/8GwIPd9lvQWd/eyPdfAfATAHYCaADIyXdvAvD33fanta/oCPrI3Pf6X7DMrwx+0Fpb5B/Wa8qKt6JjpT7dpZE3ChDbg46mS5xA5yWws/vdKX5hra0CmPOOP6UfjDE3G2P+xhhzrkvb/Ud0NGLFtLRrMZ8HE/o6i45m7mO3fB/bL8EkOuM7tYl9iXPSrmr/jDG/aIw51KXwSuhYPv54AwKA8BtG99pxv2Ef2qfI+Ky15e559ibsf6J7DHHGdqWx9/0BAGkAU12XQgnAHwDYIdf1z7utEIT5VQ5r7WFr7ZvQeSj/E4D7TSdSNG7t2rPoPNTEdejQTtMApgDs4xfGmByAcf9y3uf/BuBpADfZDkX4KwDMcx9NBJ8C8MP0hwnegM6P7psb9Is4j8749sm2/c+lM13/+Du61x/tvqAXcenGG3CNYpv/hv9ZzG/Yh/YpMr7uPIwDOCP76G/4uu4xxF5jjIn5/hQ6lvmEKFnD1trbu/tNxZx3WyEI86scxpgfN8ZM2o5fqtTdvIqOIGuj41cj/hzAvzPGXG+MGURHC/8La+0KOn60HzDGfEc3oOU3ceEf9RCAJQBlY8wtAP71pRoXOpHrwwD+yBizyxiTNca8CcD/BeD/9LTvWFhrVwF8GMBvGGPy3T7+5HPszxA6L83zAPqNMb/W7V9AwLeEbfwbfg86v5H3GWMOAIAxZq8x5j3GmBcmHPNnAP6FMeZFxpgBdMb3ZdsNoOvi/zTGjBpj9gN4O4C/kO92APg5Y0zaGPN6ALcC+Ki1dgrAJ9AJnh02nUDC5xlj/nH3uA91j9tnjBkF8M5LMgNXEYIwv/rxGgBPGWPK6ATSvNFaW+9SbO8G8IUurfRyAH8M4H+i46N7FkAdwM8CgLX2qW77g+hoqcsAZtDRZpPwSwB+tLvvf0f0R/UtwVo7B+AV6PjIv4EO1fYLAH7CWnsx1/m36NDh59AZ+59j4zEl4e8AfAwdRuAEOnN3Ico+IGAz2K6/4XkA34GOz/vLxphldHz+i+jExMQd8yCAXwXwV+iM4XkA3ujt9gA6vvvHAfwtOn524ssAbkLHDfduAD/SfZcAHUU+g877ZAEd5YdugP+Ozm/8awAeRccI2FYwmzCAArYhulp/CR367dkr3J1LBmPMfwKwy1p7oaj2gICexnb8DRtjLDrjWacMGGPeAuBfWmtfcdk71gMIlvk1BGPMD3Tp6AKA/wLgCXSibnsWxphbjDEvNB3cjU6w0V9f6X4FBGwFtuNvOODSIAjzawuvQydY5Cw6VNUbN+ObvsoxhA5lVkHHL/Zf0aHpAgK2I7bjbzjgEiDQ7AEBAQEBAT2OYJkHBAQEBAT0OIIwDwgICAgI6HH07Co2ExMT9uDBg1e6GwEBVz0eeeSRWWvt5JXux0YIv+eAgAtjo99yzwrzgwcP4uGHH77S3QgIuOphjLnqS1eG33NAwIWx0W850OwBAQEBAQE9jiDMAwICAgICehxBmAcEBAQEBPQ4gjAPCAgICAjocQRhHhAQEBAQ0OMIwjwgIGBDGGP+2BgzY4x50tv+s8aYZ4wxTxlj/rNsf5cx5kj3u++5/D0OCLj2EIR5QECP4id/8idx//33X45L/Qk6y3g6GGO+C5064S+01t6OzqIfMMbchs6Slrd3j/l9Y0zfperIAw88gE996lOX6nQBAdsGQZgHBPQo3v/+9+NrX/vall/HWvtZAPPe5n8N4D5rbaO7z0x3++sAfNBa2+guy/n/b+/O46Oq7/2Pvz4JOyTIEhYJSxBwAZEAAoIiigpVCqggUAVcWnoptXLVXrFaW0VvXW4raB9wtSJSf264r1XBlV6FgCKLLBKIkLAF2ZIYEkjm8/tjzoxnkpksZJnt83w88sjM95wz851Jzrzn+z3nfL+ZwKDaqsv48eO59NJLa+vhjIkZFubGRClVRUTC9fS9gAtEZJWIfCYi5zrlnYBs13o5Tlk5IjJDRNaIyJoDBw7UcXWNiW0W5sZEsTCGeQOgFTAE+D2wVLyVCVahoFMzquqTqjpQVQempET0aLPGRDwLc2OikG/q4jCGeQ7wmnplAB6grVPe2bVeKt65t40xdcjC3JgoFAFh/gZwsVOHXkAj4AfgLWCyiDQWkTSgJ5ARrkoaEy+idqIVY+JZfYa5iLwAjADaikgO8CfgaeBp53K148B09VbqWxFZCmwCSoBZqlpa55U0Js5ZmBsThXxhnpBQ951rqjolxKLrQqz/APBA3dXIGFNWlT4JROR7EdkgIt+IyBqnrLWILBORbc7vVq71gw4aISIDnMfJFJHHnBNmcLrkXnLKV4lIt1p+ncbEFI/HA4S1m90YE0Gq87X+IlXtp6oDnftzgI9UtSfwkXO/skEjFgIz8B5H68lPA1HcBBxW1R7Ao8BDJ/+SjIl9EXDM3BgTQWrSRzcOWOLcXgKMd5WXGzRCRDoCyar6pXNs7Z9ltvE91ivASLFPKWNCsjA3xrhVNcwV+FBEvhKRGU5Ze1XdC+D8bueUhxo0opNzu2x5wDaqWgIcBdpU76UYEz8szI0xblU9AW6Yqu4RkXbAMhHZUsG6oQaNqGgwiSoNNOF8kZgB0KVLl4prbEwMszA3xrhVqWWuqnuc37nA63jHWt7vdJ3j/PaNzRxq0Igc53bZ8oBtRKQB0JLyY0HbiFHGOCzMjTFulYa5iDQXkSTfbeAyYCPewSGmO6tNB950bgcdNMLpis8XkSHO8fBpZbbxPdYE4GP1fVoZY8qxMDfGuFWlm7098LrzodEAeF5V3xeR1XjHY74J2AVMBFDVigaNmIl3OsWmwL+cH4BFwLMikom3RT65Fl6bMTHLwtwY41ZpmKvqDuCcIOUHgZEhtgk6aISqrgH6BCkvwvkyYIypnIW5McbNxmY3JgpZmBtj3CzMjYlCFubGGDcLc2OikIW5McbNwtyYKGRhboxxszA3JgrV8xSoT4tIrjPdadllt4uIikhbV1nQiZaMMXXHwtyYKFTPLfNn+GlSJD8R6QxcivfSVF9ZRRMtGWPqiIW5MVGoPsNcVT8nyIiMeGc4/C8Ch14OOtFSnVfSmDhnYW5MFAr3MXMRGQvsVtV1ZRaFmmjJGFOHqjrRijEmgoQzzEWkGXAX3qGdyy0OUhZ0aGabOMmY2mMtc2OiUJhb5qcBacA6Efke76RJX4tIB0JPtFSOTZxkTO2xMDcmCoUzzFV1g6q2U9VuqtoNb4D3V9V9hJhoqd4raUycsTA3Jgp5PB4AEhLqfhcWkReAL4HTRSTHmVwpKFX9FvBNtPQ+gRMtGWPqiB0zNyYK1fPZ7FMqWd6tzP2gEy0ZY+qOtcyNiULhPpvdGBNZLMyNiUIW5sYYNwtzY6KQhbkxxs3C3JgoZGFujHGzMDcmClmYG2PcLMyNiUIW5sYYNwtzY6KQhbkxxs3C3JgoZGFujHGzMDcmClmYG2PcLMyNiUIW5sYYNwtzY6KQhbkxxs3C3JgoZGFujHGrcpiLSKKIrBWRd5z7rUVkmYhsc363cq17p4hkishWERnlKh8gIhucZY+J80nkTJf4klO+SkS61eJrNCbmWJgbY9yq0zK/Bdjsuj8H+EhVewIfOfcRkbOAyUBvYDSwQEQSnW0WAjPwznHc01kOcBNwWFV7AI8CD53UqzEmTtRnmIvI0yKSKyIbXWWPiMgWEVkvIq+LyCmuZUG/zBtj6k6VwlxEUoErgKdcxeOAJc7tJcB4V/mLqlqsqllAJjBIRDoCyar6pXo/if5ZZhvfY70CjBRrchgTUj23zJ/hpy/ePsuAPqraF/gOuNOpT0Vf5o0xdaSqLfN5wH8BHldZe1XdC+D8bueUdwKyXevlOGWdnNtlywO2UdUS4CjQpqovwph4U8/zmX8OHCpT9qGzrwKsBFKd20G/zNd5JY2Jc5WGuYiMAXJV9asqPmawTxetoLyibcrWZYaIrBGRNQcOHKhidYyJPRF2zPxG4F/O7VBf5o0xdagqLfNhwFgR+R54EbhYRP4fsN/pOsf5neusnwN0dm2fCuxxylODlAdsIyINgJaUaQkAqOqTqjpQVQempKRU6QUaE4siJcxF5C6gBHjOVxRktXJfzJ1tq/Xl3PeajTHlVRrmqnqnqqaqaje8x8I+VtXrgLeA6c5q04E3ndtvAZOdM9TT8J7oluF0xeeLyBDnePi0Mtv4HmuC8xy25xoTgsfjPeKVkBC+q0tFZDowBrjWtb+G+jJfTnW/nNtHgjGhNajBtg8CS0XkJmAXMBFAVb8VkaXAJrzf2GepaqmzzUy8J9M0xdst5+uaWwQ8KyKZeFvkk2tQL2NiXrhb5iIyGrgDuFBVC12L3gKeF5G/AafifJmvjee0MDcmtGqFuap+Cnzq3D4IjAyx3gPAA0HK1wB9gpQX4XwZMMZUrp4vTXsBGAG0FZEc4E94z15vDCxz6rBSVf+jki/zNWJhbkxoNWmZG2PCpJ7PZp8SpHhRBesH/TJfC/Wo7Yc0JmbYcK7GRKFwd7OHg4W5MaFZmBsTheIxzH0n/RljyrMwNyYKxWOYu1vm1ko3JpCFuTFRyMLcwtwYNwtzY6JQvIe5dbkbE8jC3JgoFO9hbi1zYwJZmBsThSzMLcyNcbMwNyYKxWOYu7vWrZvdmEAW5sZEoXgMc2uZGxOahbkxUSjew9xa5sYEsjA3JgrFe5hby9yYQBbmxkQhC3MLc2PcLMyNiULxHubWzW5MIAtzY6JQPU+B+rSI5IrIRldZaxFZJiLbnN+tXMvuFJFMEdkqIqNqqx7WMjcmNAtzY6JQPbfMnwFGlymbA3ykqj2Bj5z7iMhZwGSgt7PNAhFJrI1K2KVpxoRmYW5MFKrn+cw/Bw6VKR4HLHFuLwHGu8pfVNViVc0CMoFBtVSPoLeNMRbmxkQlX8s0jMfM26vqXgDndzunvBOQ7VovxymrMQtzY0KzMDcmCvnCLCEh4nbhYN8ugiaviMwQkTUisubAgQOVPrCdAGdMaBH3SWCMqVwEnM2+X0Q6OnXoCOQ65TlAZ9d6qcCeYA+gqk+q6kBVHZiSklLpE1rL3JjQLMyNiUIREOZvAdOd29OBN13lk0WksYikAT2BjNp4QmuZGxNag3BXwBhTffV8adoLwAigrYjkAH8CHgSWishNwC5golOvb0VkKbAJKAFmqWppbdTDHeDWMjcmkIW5MVGons9mnxJi0cgQ6z8APFAH9Qh62xhj3ezGRKUI6Gavd9bNbkxoFubGRKF4D3NrmRsTqNIwF5EmIpIhIutE5FsRudcpr/ZwjiIyQEQ2OMseE+eTyDlZ5iWnfJWIdKuD12pMzIj3MLeWuTGBqtIyLwYuVtVzgH7AaBEZwskN57gQmIH3DNee/DRE5E3AYVXtATwKPFTzl2ZM7Ir3MLeWuTGBKg1z9Spw7jZ0fpRqDufoXIuarKpfqndP/GeZbXyP9QowUuLpU8qYaorHMLex2Y0JrUrHzEUkUUS+wTswxDJVXUX1h3Ps5NwuWx6wjaqWAEeBNifxeoyJC/EY5tYyNya0KoW5qpaqaj+8ozkNEpE+FaweajjHioZ5rNIQkNUd/tGYWGVhbmFujFu1zmZX1SPAp3iPdVd3OMcc53bZ8oBtRKQB0JLyszRVe/hHY2JVvIe5dbMbE6gqZ7OniMgpzu2mwCXAFqo5nKPTFZ8vIkOc4+HTymzje6wJwMdqX72NCSnew9w+HowJVJUR4DoCS5wz0hOApar6joh8SfWHc5wJPAM0Bf7l/AAsAp4VkUy8LfLJtfHijIlV8R7m1jI3JlClYa6q64H0IOUHqeZwjqq6Bih3vF1Vi3C+DBhjKhfvYW4tc2MC2QhwxkSheAxzm2jFmNAszI2JQvEY5tbNbkxoFubGRKF4D3NrmRsTyMLcmCgUCWEuIv/pzNewUURecOZxCDlnQ01Zy9yY0CzMjYlCvjBLSAjPLiwinYDfAQNVtQ+QiPcqlKBzNtQGa5kbE5qFuTFRKBJa5nivhmnqDPTUDO8gUKHmbKgxC3NjQrMwNyYKhTvMVXU38D94x5jYCxxV1Q8JPWdDjdlEK8aEZmFuTBQKd5g7x8LHAWnAqUBzEbmumo9RrbkWrGVuTGgW5sZEoXCHOd5hnbNU9YCqngBeA4YSes6Gcqo714KdAGdMaBbmxkShCAjzXcAQEWnmzLUwEthM6Dkbasxa5saEVpWx2Y0xESbcYa6qq0TkFeBrvHMwrAWeBFoQZM6GWnrOoLeNMRbmxkSlcIe5U4c/AX8qU1xMiDkbauH5/Letm92YQNbNbkwUioQwr282NrsxoVmYGxOF4jHMrWVuTGgW5sZEoXgPc2uZGxPIwtyYKGRhbmFujJuFuTFRKN7D3LrZjQlkYW5MFIr3MLeWuTGBLMyNiULxHubWMjcmkIW5MVEoHsPcLk0zJjQLc2OiUDyGuXWzGxOahbkxUSjew9y62Y0JZGFuTBSK9zC3lrkxgSzMjYlC8R7m1jI3JpCFuTFRyBdm8Rrm1jI3JlClYS4inUXkExHZLCLfisgtTnlrEVkmItuc361c29wpIpkislVERrnKB4jIBmfZY848yIhIYxF5ySlfJSLd6uC1GhMzfGGWkBDe7+MicoqIvCIiW5zPiPMq+myoCTub3ZjQqvJJUALcpqpnAkOAWSJyFjAH+EhVewIfOfdxlk0GegOjgQUikug81kJgBtDT+RntlN8EHFbVHsCjwEO18NqMiVkR1M0+H3hfVc8AzgE2E+Kzoaasm92Y0CoNc1Xdq6pfO7fz8e6snYBxwBJntSXAeOf2OOBFVS1W1SwgExgkIh2BZFX9Ur175T/LbON7rFeAkRIBn1LGRKpICHMRSQaGA4ucOh1X1SOE/myoEetmNya0avXROd3f6cAqoL2q7gVv4APtnNU6AdmuzXKcsk7O7bLlAduoaglwFGhTnboZE08iIcyB7sABYLGIrBWRp0SkOaE/G2rEWubGhFblMBeRFsCrwGxVzato1SBlWkF5RduUrcMMEVkjImsOHDhQWZWNiVkREuYNgP7AQlVNB36kGl3q1d2frWVuTGhVCnMRaYg3yJ9T1dec4v1O1znO71ynPAfo7No8FdjjlKcGKQ/YRkQaAC2BQ2XroapPqupAVR2YkpJSYZ3XrVvH//7v/3LixImqvERjokqEhHkOkKOqq5z7r+AN91CfDQGqsz876we9bYyp2tnsgveY2GZV/Ztr0VvAdOf2dOBNV/lk5wz1NLwnumU43W35IjLEecxpZbbxPdYE4GOt4d66fPlyZs6cSVFRUU0expiIFAlhrqr7gGwROd0pGglsIvRnQ424u9atm92YQA2qsM4wYCqwQUS+ccr+ADwILBWRm4BdwEQAVf1WRJbi3alLgFmqWupsNxN4BmgK/Mv5Ae+XhWdFJBNvi3xyzV4WJCZ6T6AvKSmp6UMZE3EiIcwdNwPPiUgjYAdwA95GQrnPhpqylrkxoVUa5qr6b4If0wbvN/Fg2zwAPBCkfA3QJ0h5EbW0w/s0aOB9aaWlpZWsaUz0iZQwV9VvgIFBFgX9bKjhc/lvW8vcmEAxOwKctcxNLIuUMK9P1jI3JrSYDXNrmZtYFu9hbi1zYwLFbJhby9zEsngPc2uZGxMoZsPcWuYmllmYW5gb4xbzYW4tcxOL4jHM7dI0Y0KL2TC3bnYTy+IxzK1lbkxoMRvm1s1uYlm8h7m1zI0JFLNhbi1zE8viPcytZW5MoJgNc2uZm1gWj2FmYW5MaDEf5tYyN7FIVeOqVQ7x181eVFREVlZWuKthokTMhrl1s5tY5vF44i7M3QEejS3zoqKiak389NRTT9GnTx+bLMpUScyGuXWzm1imqiQkxOzuG1S0t8zPPvtsOnbsWOX19+/fT2FhIfv27avDWplYEbOfBtYyN7Es3rvZo7FlnpmZyZEjR/jhhx+qtP6xY8cAb6gbU5mYDXNrmZtYFilhLiKJIrJWRN5x7rcWkWUiss353aq2nivaW+Y+7777bpXW84W5tcxNVcR8mFvL3MSiSAlz4BZgs+v+HOAjVe0JfOTcrxXuMPcFXTRp06YNANnZ2f6y+fPnM2dO8LfId6zcwtxURcyGua+b3VrmJhZFQpiLSCpwBfCUq3gcsMS5vQQYX1vP5w7z/Pz82nrYOrNz505uu+02/2fQ8ePHASgoKAC8DY3Zs2fz0EMP8d577zF//nxefvll//bWzW6qo0G4K1BXrGVuYlkkhDkwD/gvIMlV1l5V9wKo6l4RaVdbT+buWvcFYiS7/vrr+fTTT5k0aRLnnnsuP/74I/BT3T///HP/urfccguZmZmkpaUxYcIERMS62U21xHzL3MLcxKJwh7mIjAFyVfWrGjzGDBFZIyJrDhw4UOn6vpZ5YmJiRLfMc3Jy+Pjjjzly5AgAhYWFFBcX+7+M+ML8iy++AOBXv/oVmZmZNGzYkKysLNauXQtYN7upnpgNczsBzsSycIc5MAwYKyLfAy8CF4vI/wP2i0hHAOd3bqgHUNUnVXWgqg5MSUmp9Al9YZ6cnBzRYf6Xv/yFn//855w4cQLwdpP7WuWA/3ZWVhYdOnRg5syZtGzZkpdeeommTZvy5z//GVW1bnZTLTEf5tYyN7Eo3GGuqneqaqqqdgMmAx+r6nXAW8B0Z7XpwJu1+JxA5Id5ZmYmhYWF5OZ6v8fs27cvIMwLCgrIzc1l48aNpKWlkZ6ezuHDh7nyyiu58847efvtt9m1a5d1s5tqidkwtxPgTCwLd5hX4EHgUhHZBlzq3K8V7jCP5GPmO3bsAMB36CBYmLdv356MjAzS0tKAnybMOfvsswE4dOiQhbmpFjsBzpgoFElhrqqfAp86tw8CI+voeQBISkqK2JZ5aWkpO3fuDCgrG+buupcdES45ORmAvLw8/zHzwsJCCgoKaNGiRV1V28SAmG+ZW5ibWBRJYV5fIrmb/emnn0ZEyMrK8h8r93EfM2/fvj05OTn+ZWUD2h3mx44do2HDhoC1zk3lYjbM7QQ4E8viMcx9Z4O3bNky4sL8rrvuAmDlypUB5ampqaxbt44RI0YA3jA/ePAgAJdddhm33357wPq+MB87diw5OTl07doVsDA3lYv5MLeWuYlF8Rjmkdwy9/UErlmzJqD8iiuuYM+ePf777du399+eM2dOyJa5j++YuoW5qUylYS4iT4tIrohsdJWFHH9ZRO4UkUwR2Soio1zlA0Rkg7PsMXE+iUSksYi85JSvEpFutfHC7AQ4E8viOcyTkpIoLi4u150dTr7GQ0ZGBiLCI488wtKlS+nevXvAeu3a/TSGjjvYfSoK89LSUu655x67VM0EVZWW+TPA6DJlQcdfFpGz8F6m0tvZZoGIJDrbLARmAD2dH99j3gQcVtUewKPAQyf7YtysZW5iWTyHuS/wIumMdl/jISMjg9TUVG6//XYmTpzoD2Mfd4B36NCh3OM0bdo04H5qaiotWrRg69atfPLJJ8ydO5dZs2bVwSsw0a7SMFfVz4FDZYpDjb88DnhRVYtVNQvIBAY5g0ckq+qX6t0j/1lmG99jvQKMlFr4lLKWuYllFuaRNT67+/PGHeAVhXmrVuUnlCv7N23evDnnnnsur7zyCh988AHw08hwxrid7DHzgPGXAV/fUScg27VejlPWybldtjxgG1UtAY4CbU6yXn7WMjexLJ7DvGXLlgAcPXo0nNUJ4J4EpqIw99W9SZMmVfr7JSYm0r9/f/bt28f//M//AN4z3Xfv3k1hYSGbNm2qjeoD3sFu3JfQmehS2yfABfvv1ArKK9qm/INXYyxnuzTNxLJ4DHPf2ey+oV9/+OGHcFYngG8cdoCePXv6b7du3RqAcePG4fF46NatG+C9lK0qCgoKGDky8LL9FStWkJqaSteuXenduzeqSnFxcY16KjweD/3792f+/Pkn/RgmvE42zEONv5wDdHatlwrsccpTg5QHbCMiDYCWlO/WB6o3lrOIkJCQYN3sJiZ5PJ64C3Nf69fXVV2VyVnqg6py+PBh//3LL7/cf1tEOHToEEuXLkVEuOyyy9i3bx9Tpkyp0mMfPXqU0aNHM3fu3HLLfF9mCgsLGT58eLmT56ojLy+P/Pz8gDPvTXQ52TAPNf7yW8Bk5wz1NLwnumU4XfH5IjLEOR4+rcw2vseagHeM56At8+pq0KCBtcxNTIrHlrnvY8F3RvikSZO48cYbw1klwHvs3t1o6NevX8DyVq1a0ahRI8Ab7sHOYg+lXbt2iAjp6ekh18nLyyMjI6N6lS7j0CFv+8ndwxDMDTfcEHf/d9GiKpemvQB8CZwuIjkichMhxl9W1W+BpcAm4H1glqr6/stnAk/hPSluO/Avp3wR0EZEMoFbcc6Mrw2JiYnWMjcxSVVJSIjZYSKC8oV527Zt/WWLFy8OV3VYvXo1p5xyChdccAEAt99+O+vXr6+1sHvzzTeZPXs2EPzMdx9393ppaSl33HEH48eP5+GHH6Z///5Vei5fz0Jl5yE888wzVXo8U/8qHZtdVUP1BwUdf1lVHwAeCFK+BugTpLwImFhZPU6GtcxNrIrnlrlviNNw+/rrrzl69Cjr168HYOjQof6JUmrim2++Yd++fYwa5R+mo8Iwz8vLC7j98MMPA94vAwBjxoxh7ty5dOrUieuuu45FixbRuXPngMeoapibyBXTX+0tzE2siucwd7/u+uydePXVV1mwYIH/vq9r2ifYpWYn45xzzgkIcggcbKYsd5gfPny4XPC/++67TJkyhccff5xly5bxxBNPlHsM32upaphH0oA9xiumw9y62U2siscw953N7g7wyk6Era3nfeihh5g7dy533323/0uFb4x1H9+Z63Whot6Ie++913/70KFDQc/y37t3r//kNt9luwAvvvgiGRkZ1W6Z+6ZnDeb555/n66+/Drl88eLFbNiwoUrPY6oupsPcWuYmVkVCmItIZxH5REQ2i8i3InKLUx5yuOeaCNYyr8kZ3FW1atUq5syZw7p16zh8+DDbtm3js88+Y/ny5QHr1VbLPJRf/OIXQcs///xz/+3t27cH/czLy8vjm2++Abxh++WXXwIwZcoUBg8eXGnL/Isvvgg4yW7v3r0sXbo06LrXXnstAwYMCLpMVbnxxhvp27dv0OXm5MV0mFvL3MSqSAhzoAS4TVXPBIYAs5whnYMO91xT7jC/5ZZbAOplkJOyF9c8//zzjBgxgnXr1gWU12XLHOC5555j8ODBFa6zefNmAG677bZyy3xhvm3bNoYOHcrevXv9y3wt87y8vHKvNycnh2HDhnH++ef7y8aNG8ekSZPYtWtXwLqVdb/b6HV1J6bD3FrmJlZFQpir6l5V/dq5nQ9sxjuiY6jhnmv6fIA3zOfNm8fs2bPrZXz2ss/h7tZ2a9asWZ3XpbLGyZYtWwDvFKr//ve/A5b5DlP4/OMf//Df9gW7x+OhoKAAj8fD1q1bAe+XCAjs6vctK9vdXtlAPu7r8WvpCmTjiPkwt5a5iUWREOZuzmyH6cAqQg/3XHabKo/o6DyWbzsAWrRoQUFBQZ2HQkXHkd1nhdfH38PdOHEPTuPjC/P27dv7h451c7fs3T0Lq1at8t8+evQo99xzD2eeeSbZ2dn+a8+DHUYoO+qc++8Y7IuW+zr23bt3l1tuTl5Mh3liYqK1zE1MiqQwF5EWwKvAbFXNq2x9n+qM6OisH3A/KSkJj8dDYWFhdatcLe4AOu2004CfWuH1fZncueeeC8DKlSt59dVXyy33BXTHjh2Dhvl7771HdnY2ffr0YeNG/6zWbNu2zX/7k08+4cEHH0RV2bJli/9QRrDwLRvm7pZ5UlISx44dC/i7uVvmvi8epnbEdJhbN7uJVZES5iLSEG+QP6eqrznFoYZ7rpGyA+UkJSUBdT97mrtlfuGFFwJw2WWXAfV7aRzAY489RkZGBoMHD6ZJkyZB17nxxhtJTk7mlFNOKbesdevWpKam0qlTJ7777ruAZb4vKNOmTfPfzsrKqvC8BHfr+6OPPio3jvzjjz9OcnIy33//PRD4xch9zN7UXKWDxkQzOwHOxKpICHNnaOZFwGZV/ZtrkW+I5gcJHO65RkpLSwNesy/MZ82aRffu3XnkkUf8y/77v/+bjRs38vzzz9f4ed1h/p//+Z+MGDGC/v3788Ybb5CamkpmZmaNn6OqmjRp4m+dl3XHHXfQqVMnbrjhBsB7GMJn06ZNAa3i1NTUctuPHTuWoUOHUlBQwJgxYxgwYEClYe7+IvXUU08FrRN4R8vLzs5mzJgx/mX79u0L+bhl5ebmMmzYMF5//XX69Ck39pghxsPcWuYmVkVCmAPDgKnABhH5xin7A94QX+oM/byLWhrhccuWLf5Zx+CnsHrtNW+HwIUXXsjy5cuZN28ed911F0CVw3zr1q388Y9/pGPHjlx22WXs3LkTEWHmzJn+MJ83bx69e/emT58+qCqPP/44V155ZdBgrC//+te/OHHiBFu2bOG2224L6Clw/3+ceeaZAdsFq3OHDh24+eab/fe7du1arTB3f3mYOnUqzz77rP/+zp07+dWvfhWw7Z49e7jhhhuYNWsWAwcOrOhl8v7775OZmcncuXN56aWXKlw3bqlqVP4MGDBAK3Puuefq5ZdfXul6xkSbSZMmaa9evaq0LrBGI2CfreinKvvzqaeeqr/4xS/895cvX654p0tWQAcNGqQiosePH/eXHTlypErv0aBBgwIey/ejqjpt2jTt0qVLyG3nzZunH3/8cZWep765X4fbk08+6V/WoEEDBfS+++4LWOfSSy8NWB7s55FHHvGvP3r0aH95QUFByG0A7dy5s6anpyugZ555ZtC6L168WN966y1VVX322Wf9206cOFHXrVtXbv3Vq1fr/fffX5O3K+JVtC/HdMvcToAzsUojo2Veb3JyctizZw9Dhgzxl/m62X18g5r88Y9/9JdlZWWVm8Us1OOHcvTo0aAnk/n4rnmPRHfffTdpaWnlyt1d1a1btyY3N7fc2epTpkxh2bJllJSUkJCQUO7SNgg8Zr5jxw7A27XevHnzgMcvO/Rtamqqf+Aa97H9FStWsGvXLnbv3u3voldV9u/f71/n5Zdfpnv37uUGnvEdfrj11ltp2rRpwLIlS5Zw4YUXBvTsxBo7Ac6YKBRvYe47WcsdQqeddlrQD+eHHnrIfzsrKytgmary0ksvBYSQx+MhNzf0OXqVhXkkmzt3btBpYt2XqPmmZy076M0NN9zgnzimU6dOQR8/Pz+f/fv3M3/+fL7//nt+//vf8+CDDwas4wty90h17vHj3ePOX3LJJVx33XX+IAfvtexlj6+X/bu6u/vLzsl+6NAhrr/++oDj9bEopsPcToAzsSrewtw3mYi7FdemTRuysrLYvn07QEBr0Oedd94hIyODNWvWcOzYMT755BMmT57MhAkT/Ovk5ORU+KX/yJEjURvmoSQkJHDttdcGfBkKdh25L+BPPfXUcstSUlI4cuQI48aNY/bs2Rw/fpxBgwb5l48cOZL09HTuvfdeGjRowNChQxkzZgw9evQICPP9+/f7L40r26IG77kK7jAfNmyYvxdg9erVlJaW+lv54P17/vDDD2zbto3CwkJefPFFIPbPno/pMG/QoAHHjx8PdzWMqXXxGubBxmLv3LkzrVu3ZubMmeWWPf300wwePJhzzz2XCy64wH9t9gcffMDu3bs5ceIEa9asCfm8u3btYtu2bUHDLNo9++yzZGVl+f+Pgg1HW1GYt2zZkg8++IBVq1bxxBNPUFBQEPAlafny5Xz99dfcc889HD9+nMTERN5++222bdtGz549/eutXLmSXr16oaoUFxdz6623BjzPH/7wBz777DOGDBmCx+OhT58+bN26lS+//JJBgwbxu9/9zh/u4A3ztLQ0evXqxR133MGsWbOA+hnHP5xiOsx79uzJxo0bravdxBwL8580bNiQ7du3l+veffvttwOGLP3qq69YsGCBv8t41apVTJs2jauvvjrk806dOhVVZc6cWhlePqL4/n98v4Ndl96mTRvgp25293uVlJTkH0jmoosuCtozUva5fG655RY2bdrE2LFj/WV79+6lqKiILl26MGLECACWLVsGQHZ2Nu3bt0dESEtLIz8/n6FDhwKwYMGCgGvmt2zZ4j+M4ptrHry9EbHcUxvTYT5y5Ejy8/NZvXp1uKtiTK3yeDwW5i6nnHIKiYmJAWW9evXiqquuKrfuLbfcQqNGjfjDH/7g74IF74xjZU9m+/zzz5k7dy7du3ev6UuIWL6u8WCHEnxd723atGHbtm3+cdoh8FK0UMfUQ0lISODMM88MuETO9zndqVMn3nnnHbZv387FF1/sX+7rHXAPoevz6KOP0qtXL1q2bBnwpc49vOyOHTto3LhxzI4JH9NhftFFFyEifPjhh+GuijG1Kt5a5vn5+TRq1IjGjRtXuN769evZunUry5cvp1evXkG7jocPH056ejpbt24NOE6clpYWdGrOa665puYvIIItXryYTz/9NOA4to/7GHaPHj0C3n/fF6tWrVqd9CQz7isSxo8fD3jPdG/evDndu3cnISHBH+K+69QnTpwY0EPgu4ZeREhNTaWkpMQ/zO7mzZs57bTT/NuWlpb6A75Hjx48/PDDJ1XvSBTTYd6mTRvOO+88Hn300YAzKY2JdlpmaNNYl5eXV+5StGDOPvtsevXqFTCs6IoVK1i5cqX/fr9+/Xj00Uf54x//GDAnuYgEPQksWMjFkhYtWviHqS3LN2RscXGxv+yjjz5i48aNnH766UDg2ejVFWwylrKt/HfffZe3336b9PR0wHtY5de//rV/+VtvvQV4J6GZN28e9957b8Dfe8SIEQFXQWzevJn8/Hy2b9/OHXfcwdVXXx0TrfWY/zS48sorOXr0KBdeeCEZGRkx8UczJt5a5nl5eSd9AtP555/P4MGD+eKLL/jHP/5B48aNOe+887jvvvvo379/wLq+0OrSpYu/rEGDmB6Oo0K+lrh7HvKLL76Y3r17+6/5L3sNeXWUnd/87rvvLjc6Xb9+/cpdVuZep0ePHsybN49XX32VSy65hHvuuYd+/fr5v+ympqZy/fXXM3XqVMA7KqD7y8trr73GlClT/PO9+/zwww9MmzYtYDz5iBZqNJlI/6nKiFGqqgcPHtSrr77aP3rQwoULq7SdMZFszJgxmp6eXqV1iYER4MaOHavnnHNOlV5vdc2fP19feOEFVVUtKCjQadOm6fvvvx9y9LR48vjjjyugM2fOLLds586dNX6Pdu/erddff321H+fo0aOVbtO3b18FdP78+aqq6vF4tEWLFiFHpWvbtm3A9r///e8V0L/+9a8n9+LqQEX7csy3zFu3bs0rr7zCX//6VwDuv/9+m0fXRD21lnmt+d3vfsfkyZMB77XqS5YsoVevXnXyXNFm0qRJ9O3bt9zlYuA9EW3ChAm88cYbJ/34p556KosXL2bWrFk8+eSTVd6uKv8Lb7zxBsOHD/fPcCciTJ8+3X+Gflnu6Vvhpwl2ouUM+LjpP7r11lvp1q0bV199Nenp6axevZquXbvWyXOtXbuW3/72t+Tl5TFgwABOnDhBSkoKN998M127diUhISGujnfGs88++4zVq1eTnJxMRkYGEyZMYPTo0UHXPXbsGMXFxUEvESor3sI8Pz+/Xo9d+8Iint7jYFJSUvxzpJclIrz88su18jx///vfq73NgAEDuOCCC0IuT0tL47PPPiv3PH//+99D/l0//vhj/xn0vlEB3UP9qioffPABl156KarKsmXLGD16dET8n8RNmANcddVVrF692j8y0TXXXMP8+fMrPUO2OpYvX84NN9zg/wfYuHGjf9n8+fMBb29Bq1ataN68OZ07d2bq1KlcfPHFLFq0iA8++IBRo0bx9ttvk5iYyHXXXcfPfvazoJdjmMhy4sQJXn/9dVq1akX//v157LHHuO+++wLWWbRoEWlpaVx11VUcPHiQAwcOsHfvXo4fP87evXvJz8/n/vvv58orr6RHjx4hnyvewjwvL69eW8u+ML/77rvr7TlN9VQ02E9lLr74Yj7++ONy5VdccQWHDh0iLy/P3+PgHjr29ddf5+qrr+bOO++kuLiYv/3tb7zzzjt07dqVs846K7yNtFD975H+U9Vj5sGsXbtWhw0bpoAmJSXpz3/+c3344Yd1zZo11Xocj8eja9as0XvuuUfnz5+vN998s4qINm3aVBcvXqyZmZnq8Xi0pKREFy5cqFOnTtX09HQ988wzNSEhQRs3bqzt27evcHYhQJs0aaI333yzrlixQj0ejxYXFwfUwfejqpqdna0FBQUn/d6Yyvnea1XVH3/8UY8dO6bz5s3T1NRU/9+sYcOGCugVV1yhN954o44fP14LCwv13nvv1SFDhiigLVu21C5duiigycnJesUVV+ioUaP8j3H77bfrli1bgtbhggsu0MGDB1epvoThmDkwGtgKZAJzKlu/sv25ffv2+utf/7pKr7e2lJaWBvytTewoLS3V0tJSPXHihH9/e/755/3HyN37YXJyshYWFuqJEyf09ttvL/f5fM011yigc+fODXiO3Nxc3bVrV63Wu6J9WbzLw09ERgPzgUTgKVV9sKL1Bw4cqDX5ZgbemXTee+89vvzyS7KzsxERzj//fESExMREevXqRd++fcnPz/dfGtO1a1dWrlxJVlYWGRkZAbP5NGjQgGuvvZYFCxZUet1lYWEhRUVFtGjRgv/7v//jmWeeoW/fvowYMYLi4mJKSkpo1aoVR48e5Ze//CVbt24FvN1eBw4coHXr1ogICQkJHDt2jJKSEkpLSzlx4gRNmzalS5cuFBYWkpSURPv27dm1axfff/893bp1o1OnTpSWljJlyhSKi4sREXr27MmRI0f48ccf6d69OwcPHqSoqIgTJ06wYcMGevfuTYcOHWjUqBFbt27lu+++8x97GjhwIMePH2fjxo0MHTqUFi1aUFpaSmFhIZ07d/Zfn3rixAkSExOj4hCDOt1pa9eu5fjx4xQXF3Pw4EEOHTrEihUr2L9/PykpKRw8eNA/m9Tw4cO5+eabWbduHbt27eI3v/kNgwYNCtqCPnjwIElJSSQmJpKXl+e/JKqkpIQXX3yR5557jvfffx/wzgZ19tlnM3XqVAoKCrjuuus4evQo99xzD/fee2+lr0VEvlLViieMrkUikgh8B1wK5ACrgSmquinUNpXtz82aNWPWrFk88sgjtV1dE+d8++fx48dp166d/+x133gE8+fP56qrrqKgoKDCMUv69u3LunXrKCoqYv369QwdOhRVZdOmTZx++un+zwn3DHTV/SysaF+OiDCvi52/OkpLS9mzZw+LFi3ivffe848StHbtWgoLC8ut37RpU7p3707//v0ZOnQokyZNoqSkhKSkJP91mbXJ4/Fw5MgRXnnlFb744gvatm1Lfn4+DRo0oKioiKKiIpKTkznllFNo2bIlu3btIisriw4dOpCXl8fatWvp0qUL5513HhkZGUG7lyoSbPrD5s2b8+OPP1Zp+2bNmtG2bVtyc3MDDmkcP36ctm3bcvz4cY4dO8ZFF13E4MGDOXz4MO3ataNVq1Y0atSI/v37IyI0bdqUVq1acfjwYcB78kxhYSEej4dmzZqRn59PTk4OTZs2JTk5meTkZJo3b87BgwfZsWMHIsKOHTs4ePAgHTt2JD8/n8zMTPbv30/jxo05fPgwubm5/Pjjj2zatCngkpTExETatGlDQkKCf9KHSy65hPT0dBo1asSIESO45JJLqvW+VmbHjh289tprvPHGG2zYsME/ChpA27Zt2bRpEykpKZU+ThjC/Dzgz6o6yrl/J4Cq/iXUNpXtz6+//jrdu3fnnHPOqe3qmjiXnZ1NYmIip556KuvXr/f/jy1ZsoSxY8dy2WWXVWkU0cTERBYuXMjs2bMDcmPu3LkkJSVx2223oar+z9KmTZuycOFCpk+fXuW6RkOY1/rOXxvy8/M5ePAgR48eJSUlhZKSEvbt20fv3r0rHIc40nk8HvLz8/nhhx9ITEzk8OHDFBUV0bZtW8A7itbpp5+OqpKdnc0ll1zChg0b/P+gZ5xxBm3btuXw4cM0bdqUr776iuzsbHr06EF+fj67d+/G4/Fw2mmnsWvXLjIyMvwzTxUVFdGkSRP/cKRHjhzx3//www/Jzs4OOXdyXUhISCA5OZni4mLat29PSkoKzZo146yzzmLw4MFMnDiRJk2alPsGXd/TYu7YsYMFCxaQlJTEqFGjGDx4cJWPmYchzCcAo1X1l879qcBgVf1tqG3qY382pioGDBjA119/zebNmznjjDNYsWIFw4cP58ILL+Suu+7i2muv5cCBAzzxxBP079+fzz//nPT0dP+YJj169OA//uM/uPzyy/nlL39JRkZGhfODuE949TVUQomGMLed36Cq7N2719+j4Ov2/+abbygpKeHQoUOoKq1bt8bj8bB7926SkpLweDwcPnyYtm3b+g8h5OXl+X9at25N9+7dOXbsGD179qRDhw5kZWWRkpJC165dadiwofc6zSjo/j8ZYQjzicCoMvvzIFW9ucx6M4AZAF26dBmwc+fO+qqiMSHt3r2bN954g9/85jeICB6Ph3nz5nHNNdeQmprKzp07Wb58OTfddFPAdv/+97957bXXmDFjBmeccQbgnczn+eefJykpiX79+uHxeNi3bx9t2rShefPmrFy5kmPHjvkfw3eSdCjREOa28xtTR2Khm90YU/G+HClNkRzAfe1VKrCn7Eqq+qSqDlTVgVU5VmiMCYvVQE8RSRORRsBk4K0w18mYmBYpYW47vzExQlVLgN8CHwCbgaWq+m14a2VMbIuIQWNUtUREfDt/IvC07fzGRC9VfQ94L9z1MCZeRESYg+38xhhjzMmKlG52Y4wxxpwkC3NjjDEmylmYG2OMMVEuIq4zPxkicgCo7ELztsAPlaxTnyKtPhB5dbL6VOxk6tNVVSP6Wk7bn2uF1adikVYfqH6dQu7LURvmVSEia+pzsIzKRFp9IPLqZPWpWKTVpz5F2mu3+lTM6lO52qyTdbMbY4wxUc7C3BhjjIlysR7mT4a7AmVEWn0g8upk9alYpNWnPkXaa7f6VMzqU7laq1NMHzM3xhhj4kGst8yNMcaYmBezYS4io0Vkq4hkisicMNXhexHZICLfiMgap6y1iCwTkW3O71Z1+PxPi0iuiGx0lYV8fhG503m/torIqHqqz59FZLfzHn0jIpfXY306i8gnIrJZRL4VkVuc8rC8RxXUJ2zvUSSwfdlfB9ufK65PfO/PqhpzP3gna9kOdAcaAeuAs8JQj++BtmXKHgbmOLfnAA/V4fMPB/oDGyt7fuAs531qDKQ5719iPdTnz8DtQdatj/p0BPo7t5OA75znDct7VEF9wvYehfvH9uWA57P9ueL6xPX+HKst80FApqruUNXjwIvAuDDXyWccsMS5vQQYX1dPpKqfA4eq+PzjgBdVtVhVs4BMvO9jXdcnlPqoz15V/dq5nY93us5OhOk9qqA+odT5exQBbF922P5caX3ien+O1TDvBGS77udQ8ZtYVxT4UES+EpEZTll7Vd0L3j820K6e6xTq+cP5nv1WRNY73Xa+LrB6rY+IdAPSgVVEwHtUpj4QAe9RmETKa4zEfbmiOtj+HGf7c6yGuQQpC8dp+8NUtT/wM2CWiAwPQx2qKlzv2ULgNKAfsBf4a33XR0RaAK8Cs1U1r6JV66NOQeoT9vcojCLlNUbTvgy2P8fd/hyrYZ4DdHbdTwX21HclVHWP8zsXeB1vl8l+EekI4PzOredqhXr+sLxnqrpfVUtV1QP8g5+6leqlPiLSEO+O9pyqvuYUh+09ClafcL9HYRYRrzFC92UqqIPtz3G2P8dqmK8GeopImog0AiYDb9VnBUSkuYgk+W4DlwEbnXpMd1abDrxZn/Wq4PnfAiaLSGMRSQN6Ahl1XRnfTua4Eu97VC/1EREBFgGbVfVvrkVheY9C1Sec71EEsH25YrY///Tc8b0/19aZe5H2A1yO9+zB7cBdYXj+7njPTFwHfOurA9AG+AjY5vxuXYd1eAFvN84JvN/6bqro+YG7nPdrK/CzeqrPs8AGYL3zz9yxHutzPt5urPXAN87P5eF6jyqoT9jeo0j4sX3ZXw/bnyuuT1zvzzYCnDHGGBPlYrWb3RhjjIkbFubGGGNMlLMwN8YYY6KchbkxxhgT5SzMjTHGmChnYW6MMcZEOQtzY4wxJspZmBtjjDFR7v8DDYnbjllTawgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#применение функции для преобработки данных с размером 64 на 64 и выведем\n",
    "X_processed = ImagePreProcessing(img_files_list, 1, 64, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4807fe4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(920, 64, 64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_processed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0506bbf4",
   "metadata": {},
   "source": [
    "## Извлечение признаков с фильтрами Габбора"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d191e6",
   "metadata": {},
   "source": [
    "Напишем функцию (GaborFiltersFeatures) для создания 8 фильтров Габора (cv2.getGaborKernel) со следующими параметрами:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae268b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_range = np.arange(0, np.pi, np.pi/8)  # Фазы синусоиды\n",
    "lamda = 3*np.pi/4  # Фиксированная частота синусоиды\n",
    "gamma = 0.5  # Параметр Гаусса\n",
    "sigma = 0.75  # Параметр Гаусса\n",
    "ksize = (9,9)\n",
    "phi = 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77db5f07",
   "metadata": {},
   "source": [
    "На выходе для каждой фотки размером 64 на 64 будет получено 8 отфильтрованных изображений с разными фильтрами из банка фильтров Габара. После этого в начале будет добавлено исходное изображение и после операции flattern на выходе мы получим вектор признаков размером 9 (число фоток, полученных из одной) х 64 х 64 (размер исходной фотки фотки после предобработки)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacbe00d",
   "metadata": {},
   "source": [
    "Следовательно каждой из 920 фоток будет соответствовть вектор признаков размера 9 х 64 х 64 = 36864"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a23b7cd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 920/920 [00:02<00:00, 385.38it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x360 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def GaborFiltersFeatures(X_processed, ksize, sigma, theta_range, lambd, gamma, psi):\n",
    "    result = []\n",
    "    flag = 0\n",
    "    final = []\n",
    "    res = []\n",
    "    for im in tqdm(X_processed):\n",
    "        final = np.asarray(im.flatten())\n",
    "        for i in range(0, theta_range.size):\n",
    "            g_kernel = cv2.getGaborKernel(ksize, sigma, theta_range[i],\n",
    "                                          lambd, gamma, psi, ktype=cv2.CV_32F)\n",
    "            if flag == 0:\n",
    "                plt.subplot(2, theta_range.size, i + 1)\n",
    "                plt.imshow(g_kernel, cmap='gray')\n",
    "                plt.axis('off') \n",
    "            filtered_img = cv2.filter2D(im, cv2.CV_8UC3, g_kernel)\n",
    "            if flag == 15:\n",
    "                plt.subplot(2, theta_range.size, 8 + i +1)\n",
    "                plt.imshow(filtered_img, cmap='gray')\n",
    "                plt.axis('off') \n",
    "            features = filtered_img.flatten()\n",
    "            final = np.concatenate((final, features), axis=None)\n",
    "      \n",
    "        res.append(final)\n",
    "        flag = flag + 1 \n",
    "    return np.asarray(res)\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 7, figsize=(19, 5))\n",
    "X_features = GaborFiltersFeatures(X_processed, ksize, sigma, theta_range, lamda, gamma, phi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3279e44d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(920, 36864)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f3b659",
   "metadata": {},
   "source": [
    "Вот, как выглядит исзодный массив данных перед стадиями разделения выборки на тренировочную и тестовую"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00304d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>36854</th>\n",
       "      <th>36855</th>\n",
       "      <th>36856</th>\n",
       "      <th>36857</th>\n",
       "      <th>36858</th>\n",
       "      <th>36859</th>\n",
       "      <th>36860</th>\n",
       "      <th>36861</th>\n",
       "      <th>36862</th>\n",
       "      <th>36863</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
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       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>52</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>105</td>\n",
       "      <td>135</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>59</td>\n",
       "      <td>57</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>105</td>\n",
       "      <td>134</td>\n",
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       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>110</td>\n",
       "      <td>170</td>\n",
       "      <td>187</td>\n",
       "      <td>178</td>\n",
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       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>98</td>\n",
       "      <td>134</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>123</td>\n",
       "      <td>170</td>\n",
       "      <td>181</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>124</td>\n",
       "      <td>118</td>\n",
       "      <td>96</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>75</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "      <td>81</td>\n",
       "      <td>116</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>106</td>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "      <td>99</td>\n",
       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "      <td>54</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>125</td>\n",
       "      <td>139</td>\n",
       "      <td>107</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>102</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>146</td>\n",
       "      <td>114</td>\n",
       "      <td>111</td>\n",
       "      <td>101</td>\n",
       "      <td>98</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>155</td>\n",
       "      <td>142</td>\n",
       "      <td>120</td>\n",
       "      <td>82</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>72</td>\n",
       "      <td>95</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>116</td>\n",
       "      <td>136</td>\n",
       "      <td>140</td>\n",
       "      <td>106</td>\n",
       "      <td>92</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>147</td>\n",
       "      <td>135</td>\n",
       "      <td>107</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>52</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>125</td>\n",
       "      <td>89</td>\n",
       "      <td>48</td>\n",
       "      <td>93</td>\n",
       "      <td>84</td>\n",
       "      <td>99</td>\n",
       "      <td>79</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>47</td>\n",
       "      <td>74</td>\n",
       "      <td>120</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 36864 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      1      2      3      4      5      6      7      8      9      \\\n",
       "0       30     28     20      9      6      3      3      3      3      6   \n",
       "1       26     24     20     18     10      4      3      4      3      3   \n",
       "2       36     41     26     17     21     25     23     48     63     46   \n",
       "3       41     37     17      8     13     21     26     50     53     34   \n",
       "4       35     46     29     16     13     16     16     36     63     52   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "915    124    118     96     59     58     64     75     83     84     67   \n",
       "916    106     89     76     99    105    106     54     67     67     90   \n",
       "917    146    114    111    101     98    106     93     48     55     80   \n",
       "918    116    136    140    106     92     57     57     91     70     60   \n",
       "919     80     98    125     89     48     93     84     99     79     99   \n",
       "\n",
       "     ...  36854  36855  36856  36857  36858  36859  36860  36861  36862  36863  \n",
       "0    ...     35     56     52     27     26     58     46     32    105    135  \n",
       "1    ...     37     59     57     33     27     53     40     31    105    134  \n",
       "2    ...     56     63     72     72     69     81    110    170    187    178  \n",
       "3    ...     55     71     78     76     77     98    134    179    179    173  \n",
       "4    ...     30     44     57     74     81     94    123    170    181    174  \n",
       "..   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "915  ...     43     43     46     73     61     76    100     81    116     41  \n",
       "916  ...    109    125    139    107     25      4     30     31    102    139  \n",
       "917  ...    150    155    142    120     82     28     21     72     95     38  \n",
       "918  ...     95    147    135    107     30      6     18     34     52    153  \n",
       "919  ...    103     21     37     24     12     31     47     74    120    174  \n",
       "\n",
       "[920 rows x 36864 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features = pd.DataFrame(data=X_features,\n",
    "          index=pd.RangeIndex(range(0, 920)),\n",
    "          columns=pd.RangeIndex(range(0, 36864)))\n",
    "data_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d1b120",
   "metadata": {},
   "source": [
    "Очевидно, что размер вектора признаков состоит из 36863 признаков. Данное число признаков слишком большое для обучения моделей, поэтому воспользуемся снижением размерности с помощью PCA (МГК)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e711768f",
   "metadata": {},
   "source": [
    "Но перед процедурой снижения размерности необходимо сначала отделить тренировочную и тестовую выборки. Инач емы при поиске проекций с максимальными дисперсиями будем учитывать данные, которые машина знать не должна, ведь они отнесены к тестовой выборке"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93af31e",
   "metadata": {},
   "source": [
    "Разобьем данные на train и test:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c387c5",
   "metadata": {},
   "source": [
    "## Задание метки класса "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "919d3b51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   label              file name\n",
       "0      1  S010_004_00000019.png\n",
       "1      1  S011_004_00000021.png\n",
       "2      1  S014_003_00000030.png\n",
       "3      1  S022_005_00000032.png\n",
       "4      1  S026_003_00000015.png"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "627daae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_features['file name'] = list_im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "ec755765",
   "metadata": {},
   "outputs": [
    {
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       "      <td>S005_001_00000011.png</td>\n",
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       "      <td>36</td>\n",
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       "      <td>69</td>\n",
       "      <td>81</td>\n",
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       "      <td>178</td>\n",
       "      <td>S010_001_00000001.png</td>\n",
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       "      <td>41</td>\n",
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       "      <td>17</td>\n",
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       "      <td>13</td>\n",
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       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
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       "      <td>71</td>\n",
       "      <td>78</td>\n",
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       "      <td>77</td>\n",
       "      <td>98</td>\n",
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       "      <td>173</td>\n",
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       "      <td>36</td>\n",
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       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>123</td>\n",
       "      <td>170</td>\n",
       "      <td>181</td>\n",
       "      <td>174</td>\n",
       "      <td>S010_002_00000014.png</td>\n",
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       "<p>5 rows × 36865 columns</p>\n",
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      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9  ...  36855  36856  36857  36858  \\\n",
       "0  30  28  20   9   6   3   3   3   3   6  ...     56     52     27     26   \n",
       "1  26  24  20  18  10   4   3   4   3   3  ...     59     57     33     27   \n",
       "2  36  41  26  17  21  25  23  48  63  46  ...     63     72     72     69   \n",
       "3  41  37  17   8  13  21  26  50  53  34  ...     71     78     76     77   \n",
       "4  35  46  29  16  13  16  16  36  63  52  ...     44     57     74     81   \n",
       "\n",
       "   36859  36860  36861  36862  36863              file name  \n",
       "0     58     46     32    105    135  S005_001_00000001.png  \n",
       "1     53     40     31    105    134  S005_001_00000011.png  \n",
       "2     81    110    170    187    178  S010_001_00000001.png  \n",
       "3     98    134    179    179    173  S010_002_00000001.png  \n",
       "4     94    123    170    181    174  S010_002_00000014.png  \n",
       "\n",
       "[5 rows x 36865 columns]"
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     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee05b80",
   "metadata": {},
   "source": [
    "Объединим таблицы чтоб определить метку класса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "03d048f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_full = data_features.merge(df, how='left', on='file name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "b36d4368",
   "metadata": {},
   "outputs": [
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       "      <th>36862</th>\n",
       "      <th>36863</th>\n",
       "      <th>file name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>105</td>\n",
       "      <td>135</td>\n",
       "      <td>S005_001_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>105</td>\n",
       "      <td>134</td>\n",
       "      <td>S005_001_00000011.png</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>110</td>\n",
       "      <td>170</td>\n",
       "      <td>187</td>\n",
       "      <td>178</td>\n",
       "      <td>S010_001_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>98</td>\n",
       "      <td>134</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>173</td>\n",
       "      <td>S010_002_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>123</td>\n",
       "      <td>170</td>\n",
       "      <td>181</td>\n",
       "      <td>174</td>\n",
       "      <td>S010_002_00000014.png</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36866 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9  ...  36856  36857  36858  36859  \\\n",
       "0  30  28  20   9   6   3   3   3   3   6  ...     52     27     26     58   \n",
       "1  26  24  20  18  10   4   3   4   3   3  ...     57     33     27     53   \n",
       "2  36  41  26  17  21  25  23  48  63  46  ...     72     72     69     81   \n",
       "3  41  37  17   8  13  21  26  50  53  34  ...     78     76     77     98   \n",
       "4  35  46  29  16  13  16  16  36  63  52  ...     57     74     81     94   \n",
       "\n",
       "   36860  36861  36862  36863              file name  label  \n",
       "0     46     32    105    135  S005_001_00000001.png      0  \n",
       "1     40     31    105    134  S005_001_00000011.png      3  \n",
       "2    110    170    187    178  S010_001_00000001.png      0  \n",
       "3    134    179    179    173  S010_002_00000001.png      0  \n",
       "4    123    170    181    174  S010_002_00000014.png      6  \n",
       "\n",
       "[5 rows x 36866 columns]"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5360ba",
   "metadata": {},
   "source": [
    "Посмотрм число фоток кадого класса:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "325b6834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    621\n",
       "6     83\n",
       "5     69\n",
       "3     59\n",
       "1     45\n",
       "4     25\n",
       "2     18\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "a4d1206f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>36856</th>\n",
       "      <th>36857</th>\n",
       "      <th>36858</th>\n",
       "      <th>36859</th>\n",
       "      <th>36860</th>\n",
       "      <th>36861</th>\n",
       "      <th>36862</th>\n",
       "      <th>36863</th>\n",
       "      <th>file name</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>105</td>\n",
       "      <td>135</td>\n",
       "      <td>S005_001_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>105</td>\n",
       "      <td>134</td>\n",
       "      <td>S005_001_00000011.png</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>48</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>110</td>\n",
       "      <td>170</td>\n",
       "      <td>187</td>\n",
       "      <td>178</td>\n",
       "      <td>S010_001_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>98</td>\n",
       "      <td>134</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>173</td>\n",
       "      <td>S010_002_00000001.png</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>123</td>\n",
       "      <td>170</td>\n",
       "      <td>181</td>\n",
       "      <td>174</td>\n",
       "      <td>S010_002_00000014.png</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36866 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9  ...  36856  36857  36858  36859  \\\n",
       "0  30  28  20   9   6   3   3   3   3   6  ...     52     27     26     58   \n",
       "1  26  24  20  18  10   4   3   4   3   3  ...     57     33     27     53   \n",
       "2  36  41  26  17  21  25  23  48  63  46  ...     72     72     69     81   \n",
       "3  41  37  17   8  13  21  26  50  53  34  ...     78     76     77     98   \n",
       "4  35  46  29  16  13  16  16  36  63  52  ...     57     74     81     94   \n",
       "\n",
       "   36860  36861  36862  36863              file name  label  \n",
       "0     46     32    105    135  S005_001_00000001.png      0  \n",
       "1     40     31    105    134  S005_001_00000011.png      3  \n",
       "2    110    170    187    178  S010_001_00000001.png      0  \n",
       "3    134    179    179    173  S010_002_00000001.png      0  \n",
       "4    123    170    181    174  S010_002_00000014.png      6  \n",
       "\n",
       "[5 rows x 36866 columns]"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f943b0d",
   "metadata": {},
   "source": [
    "Выборки слишком неравномерные. Увеличим число элементов иных классов кроме нейтрального в 3 раза. То есть добавим те же элементы еще 2 раза, тем самым искусственно увеличим число данных. Но чтобы не было ошибки надо увеличивать уже после разделения на тест и трейн чтобы не было такого, чтобы случайно при разделении в трейне и тесте оказались одинаковые элементы."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c99913",
   "metadata": {},
   "source": [
    "## Разбивка данных на train и test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "6c6f930e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_full.label\n",
    "X = data_full.drop(['file name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "134cf1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "4cd3edf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    497\n",
       "6     67\n",
       "5     55\n",
       "3     47\n",
       "1     36\n",
       "4     20\n",
       "2     14\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b256fe",
   "metadata": {},
   "source": [
    "Хоть немного сделаем более равномерное число классов и применим upsampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "6ae013b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    497\n",
       "6    335\n",
       "4    300\n",
       "5    275\n",
       "3    235\n",
       "2    210\n",
       "1    180\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_add = X_train.query('label != 0')\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "d_add = X_train.query('label in [2,4]')\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "X_train = X_train.append(d_add, ignore_index=True)\n",
    "X_train.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "2566cb28",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = X_train.label\n",
    "X_train = X_train.drop(columns=['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "d5ec6e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    124\n",
       "6     16\n",
       "5     14\n",
       "3     12\n",
       "1      9\n",
       "4      5\n",
       "2      4\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64035733",
   "metadata": {},
   "source": [
    "Хоть немного сделаем более равномерное число классов и применим upsampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "a60f3035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    124\n",
       "1     90\n",
       "6     80\n",
       "4     75\n",
       "5     70\n",
       "3     60\n",
       "2     60\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_add = X_test.query('label != 0')\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "d_add = X_test.query('label in [2,4]')\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "d_add = X_test.query('label in [1]')\n",
    "X_test = X_test.append(d_add, ignore_index=True)\n",
    "X_test.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "7ed85e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test.label\n",
    "X_test = X_test.drop(columns=['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "58f41838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 36864)"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "e0535d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559, 36864)"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "ee683aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "020199dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.array(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab9b081",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0a9122",
   "metadata": {},
   "source": [
    "## Применим LDA классификатор без уменьшения размерности данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "53f8037f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.99\n",
      "Точность модели на тестовой выборке:  0.55\n",
      "Матрица несоответствий метода \"Линейный дискриминантный анализ без МГК\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LDA_model = LinearDiscriminantAnalysis()\n",
    "LDA_model.fit(X_train, y_train)\n",
    "LDA_prediction = LDA_model.predict(X_test)\n",
    "\n",
    "LDA_train_accuracy = LDA_model.score(X_train, y_train)\n",
    "LDA_test_accuracy = LDA_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (LDA_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (LDA_test_accuracy, 2))\n",
    "\n",
    "print('Матрица несоответствий метода \"Линейный дискриминантный анализ без МГК\":\\n')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(LDA_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "dbac258d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.42      0.86      0.56       124\n",
      "           1       0.43      0.22      0.29        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.62      0.58      0.60        60\n",
      "           4       0.42      0.20      0.27        75\n",
      "           5       0.79      0.86      0.82        70\n",
      "           6       0.80      0.88      0.84        80\n",
      "\n",
      "    accuracy                           0.55       559\n",
      "   macro avg       0.50      0.51      0.48       559\n",
      "weighted avg       0.50      0.55      0.50       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, LDA_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f48b37e",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 93%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53435d2a",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a63637e",
   "metadata": {},
   "source": [
    "# Применение снижения размерности (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "a4a02a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Временно сохраним старые данные в новые переменные \n",
    "X_train_no_PCA = X_train.copy()\n",
    "X_test_no_PCA = X_test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b56eff",
   "metadata": {},
   "source": [
    "Напишием код примененеия PCA к исходным данным для снижения размерности признаков (90% дисперсии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "e6e5bdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 36864)"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Шаг 1 - Стандартизация X_train\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "scaled_data = scaler.transform(X_train)\n",
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "5c91e8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>36854</th>\n",
       "      <th>36855</th>\n",
       "      <th>36856</th>\n",
       "      <th>36857</th>\n",
       "      <th>36858</th>\n",
       "      <th>36859</th>\n",
       "      <th>36860</th>\n",
       "      <th>36861</th>\n",
       "      <th>36862</th>\n",
       "      <th>36863</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.512623</td>\n",
       "      <td>-0.609478</td>\n",
       "      <td>-0.699608</td>\n",
       "      <td>-0.740134</td>\n",
       "      <td>-0.755057</td>\n",
       "      <td>-0.769175</td>\n",
       "      <td>-0.838583</td>\n",
       "      <td>-0.640556</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.864283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155967</td>\n",
       "      <td>0.353423</td>\n",
       "      <td>0.384437</td>\n",
       "      <td>0.070108</td>\n",
       "      <td>-0.039392</td>\n",
       "      <td>0.456842</td>\n",
       "      <td>1.373521</td>\n",
       "      <td>1.731026</td>\n",
       "      <td>-0.043461</td>\n",
       "      <td>-1.438548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.877818</td>\n",
       "      <td>-0.986517</td>\n",
       "      <td>-1.122309</td>\n",
       "      <td>-1.187578</td>\n",
       "      <td>-0.858062</td>\n",
       "      <td>-0.833145</td>\n",
       "      <td>-1.222056</td>\n",
       "      <td>-1.439525</td>\n",
       "      <td>-1.439688</td>\n",
       "      <td>-1.585296</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.677254</td>\n",
       "      <td>-1.337633</td>\n",
       "      <td>-0.860718</td>\n",
       "      <td>-0.890476</td>\n",
       "      <td>-0.527805</td>\n",
       "      <td>-0.996752</td>\n",
       "      <td>-0.517885</td>\n",
       "      <td>0.222214</td>\n",
       "      <td>0.339397</td>\n",
       "      <td>0.854318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.877818</td>\n",
       "      <td>-0.986517</td>\n",
       "      <td>-1.102180</td>\n",
       "      <td>-1.057074</td>\n",
       "      <td>-0.961066</td>\n",
       "      <td>-0.833145</td>\n",
       "      <td>-0.731211</td>\n",
       "      <td>-0.580257</td>\n",
       "      <td>-0.278579</td>\n",
       "      <td>-0.103067</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.768614</td>\n",
       "      <td>-0.546405</td>\n",
       "      <td>-0.310533</td>\n",
       "      <td>-0.055186</td>\n",
       "      <td>0.197838</td>\n",
       "      <td>0.530256</td>\n",
       "      <td>0.818108</td>\n",
       "      <td>1.124391</td>\n",
       "      <td>1.200829</td>\n",
       "      <td>0.487460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.828019</td>\n",
       "      <td>-0.741441</td>\n",
       "      <td>-0.256778</td>\n",
       "      <td>0.229328</td>\n",
       "      <td>0.618342</td>\n",
       "      <td>0.654172</td>\n",
       "      <td>0.449886</td>\n",
       "      <td>0.248862</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>-0.165476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171908</td>\n",
       "      <td>0.353423</td>\n",
       "      <td>0.254130</td>\n",
       "      <td>0.376381</td>\n",
       "      <td>0.323430</td>\n",
       "      <td>0.060407</td>\n",
       "      <td>-0.382785</td>\n",
       "      <td>-1.255488</td>\n",
       "      <td>-0.936797</td>\n",
       "      <td>2.291181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.745020</td>\n",
       "      <td>-0.816849</td>\n",
       "      <td>-0.860637</td>\n",
       "      <td>-1.094361</td>\n",
       "      <td>-1.167076</td>\n",
       "      <td>-1.264947</td>\n",
       "      <td>-1.191378</td>\n",
       "      <td>-1.017428</td>\n",
       "      <td>-0.828578</td>\n",
       "      <td>-0.711561</td>\n",
       "      <td>...</td>\n",
       "      <td>1.016783</td>\n",
       "      <td>1.579051</td>\n",
       "      <td>-0.513233</td>\n",
       "      <td>-1.516944</td>\n",
       "      <td>-1.169720</td>\n",
       "      <td>-1.143580</td>\n",
       "      <td>-1.418555</td>\n",
       "      <td>-1.504365</td>\n",
       "      <td>-1.495132</td>\n",
       "      <td>-1.392691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2.259535</td>\n",
       "      <td>1.596198</td>\n",
       "      <td>1.695697</td>\n",
       "      <td>2.839419</td>\n",
       "      <td>2.146248</td>\n",
       "      <td>0.670165</td>\n",
       "      <td>1.170816</td>\n",
       "      <td>2.253820</td>\n",
       "      <td>1.921419</td>\n",
       "      <td>0.911090</td>\n",
       "      <td>...</td>\n",
       "      <td>1.973246</td>\n",
       "      <td>0.694737</td>\n",
       "      <td>0.268608</td>\n",
       "      <td>0.432067</td>\n",
       "      <td>0.225747</td>\n",
       "      <td>1.308443</td>\n",
       "      <td>1.763812</td>\n",
       "      <td>0.299988</td>\n",
       "      <td>-1.080369</td>\n",
       "      <td>-1.438548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>-0.429625</td>\n",
       "      <td>-0.138179</td>\n",
       "      <td>0.427594</td>\n",
       "      <td>0.844564</td>\n",
       "      <td>1.219204</td>\n",
       "      <td>1.373842</td>\n",
       "      <td>1.140138</td>\n",
       "      <td>0.836782</td>\n",
       "      <td>0.745031</td>\n",
       "      <td>0.614644</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.310610</td>\n",
       "      <td>-1.073890</td>\n",
       "      <td>-1.092375</td>\n",
       "      <td>-1.154985</td>\n",
       "      <td>-0.081256</td>\n",
       "      <td>0.251283</td>\n",
       "      <td>0.352762</td>\n",
       "      <td>0.968843</td>\n",
       "      <td>0.100111</td>\n",
       "      <td>-0.353258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>1.313349</td>\n",
       "      <td>1.690458</td>\n",
       "      <td>2.118398</td>\n",
       "      <td>2.242827</td>\n",
       "      <td>2.232085</td>\n",
       "      <td>1.885608</td>\n",
       "      <td>1.400900</td>\n",
       "      <td>1.108130</td>\n",
       "      <td>1.218642</td>\n",
       "      <td>1.301151</td>\n",
       "      <td>...</td>\n",
       "      <td>1.224017</td>\n",
       "      <td>1.191194</td>\n",
       "      <td>1.354499</td>\n",
       "      <td>1.476180</td>\n",
       "      <td>1.649124</td>\n",
       "      <td>0.823911</td>\n",
       "      <td>-0.592941</td>\n",
       "      <td>-1.084386</td>\n",
       "      <td>-0.426319</td>\n",
       "      <td>0.059458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>0.400363</td>\n",
       "      <td>0.804417</td>\n",
       "      <td>1.333382</td>\n",
       "      <td>0.415763</td>\n",
       "      <td>-0.463209</td>\n",
       "      <td>0.142407</td>\n",
       "      <td>-0.179009</td>\n",
       "      <td>-0.082786</td>\n",
       "      <td>-0.477189</td>\n",
       "      <td>-0.274693</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146913</td>\n",
       "      <td>-1.399690</td>\n",
       "      <td>-1.106853</td>\n",
       "      <td>-1.182828</td>\n",
       "      <td>-1.267403</td>\n",
       "      <td>-1.084849</td>\n",
       "      <td>-0.818108</td>\n",
       "      <td>-0.353312</td>\n",
       "      <td>0.371302</td>\n",
       "      <td>1.221177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>-0.363226</td>\n",
       "      <td>-0.684886</td>\n",
       "      <td>-0.558708</td>\n",
       "      <td>-0.497769</td>\n",
       "      <td>-0.789392</td>\n",
       "      <td>-0.849138</td>\n",
       "      <td>-0.853922</td>\n",
       "      <td>-1.017428</td>\n",
       "      <td>-0.614689</td>\n",
       "      <td>-0.009452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.319663</td>\n",
       "      <td>0.555109</td>\n",
       "      <td>0.891186</td>\n",
       "      <td>0.209323</td>\n",
       "      <td>-1.030173</td>\n",
       "      <td>-0.600317</td>\n",
       "      <td>0.322740</td>\n",
       "      <td>1.777691</td>\n",
       "      <td>1.822974</td>\n",
       "      <td>2.459325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2032 rows × 36864 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6      \\\n",
       "0    -0.512623 -0.609478 -0.699608 -0.740134 -0.755057 -0.769175 -0.838583   \n",
       "1    -0.877818 -0.986517 -1.122309 -1.187578 -0.858062 -0.833145 -1.222056   \n",
       "2    -0.877818 -0.986517 -1.102180 -1.057074 -0.961066 -0.833145 -0.731211   \n",
       "3    -0.828019 -0.741441 -0.256778  0.229328  0.618342  0.654172  0.449886   \n",
       "4    -0.745020 -0.816849 -0.860637 -1.094361 -1.167076 -1.264947 -1.191378   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2027  2.259535  1.596198  1.695697  2.839419  2.146248  0.670165  1.170816   \n",
       "2028 -0.429625 -0.138179  0.427594  0.844564  1.219204  1.373842  1.140138   \n",
       "2029  1.313349  1.690458  2.118398  2.242827  2.232085  1.885608  1.400900   \n",
       "2030  0.400363  0.804417  1.333382  0.415763 -0.463209  0.142407 -0.179009   \n",
       "2031 -0.363226 -0.684886 -0.558708 -0.497769 -0.789392 -0.849138 -0.853922   \n",
       "\n",
       "         7         8         9      ...     36854     36855     36856  \\\n",
       "0    -0.640556 -0.034134  0.864283  ...  0.155967  0.353423  0.384437   \n",
       "1    -1.439525 -1.439688 -1.585296  ... -1.677254 -1.337633 -0.860718   \n",
       "2    -0.580257 -0.278579 -0.103067  ... -0.768614 -0.546405 -0.310533   \n",
       "3     0.248862  0.042254 -0.165476  ...  0.171908  0.353423  0.254130   \n",
       "4    -1.017428 -0.828578 -0.711561  ...  1.016783  1.579051 -0.513233   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2027  2.253820  1.921419  0.911090  ...  1.973246  0.694737  0.268608   \n",
       "2028  0.836782  0.745031  0.614644  ... -1.310610 -1.073890 -1.092375   \n",
       "2029  1.108130  1.218642  1.301151  ...  1.224017  1.191194  1.354499   \n",
       "2030 -0.082786 -0.477189 -0.274693  ... -0.146913 -1.399690 -1.106853   \n",
       "2031 -1.017428 -0.614689 -0.009452  ...  1.319663  0.555109  0.891186   \n",
       "\n",
       "         36857     36858     36859     36860     36861     36862     36863  \n",
       "0     0.070108 -0.039392  0.456842  1.373521  1.731026 -0.043461 -1.438548  \n",
       "1    -0.890476 -0.527805 -0.996752 -0.517885  0.222214  0.339397  0.854318  \n",
       "2    -0.055186  0.197838  0.530256  0.818108  1.124391  1.200829  0.487460  \n",
       "3     0.376381  0.323430  0.060407 -0.382785 -1.255488 -0.936797  2.291181  \n",
       "4    -1.516944 -1.169720 -1.143580 -1.418555 -1.504365 -1.495132 -1.392691  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2027  0.432067  0.225747  1.308443  1.763812  0.299988 -1.080369 -1.438548  \n",
       "2028 -1.154985 -0.081256  0.251283  0.352762  0.968843  0.100111 -0.353258  \n",
       "2029  1.476180  1.649124  0.823911 -0.592941 -1.084386 -0.426319  0.059458  \n",
       "2030 -1.182828 -1.267403 -1.084849 -0.818108 -0.353312  0.371302  1.221177  \n",
       "2031  0.209323 -1.030173 -0.600317  0.322740  1.777691  1.822974  2.459325  \n",
       "\n",
       "[2032 rows x 36864 columns]"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Стандартизованные данные\n",
    "pd.DataFrame(data=scaled_data,\n",
    "          index=pd.RangeIndex(range(0, scaled_data.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, 36864)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "a52c237c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=scaled_data.shape[0])\n",
    "pca.fit(scaled_data)\n",
    "ratio = pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "7c9448ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 36864)"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "e8adaa58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    "plt.xlabel(\"Номер гланвной компоненты\")\n",
    "plt.ylabel(\"Процент объясненной дисперсии\")\n",
    "plt.title(\"Вклад главных компонент\")\n",
    "plt.bar(range(1, 16),ratio[:15])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "72a1df8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число главных компонент, объясняющих более 90% дисперсии равно 116\n"
     ]
    }
   ],
   "source": [
    "ss = np.cumsum(ratio)\n",
    "col = -1\n",
    "for i in range(len(ss)):\n",
    "    if float(ss[i]) > 0.9:\n",
    "        col = i\n",
    "        break\n",
    "print('Число главных компонент, объясняющих более 90% дисперсии равно', col+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "1ef3b720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MbCdlScJaImJ17pFYh+YvX89Hf/YQADd/+jDeOH54iSMyMzOz3pAlCauVNJKkG/I1EbEmn5Cs3WOL1/KxGQ8zqL6W6z/5VvYZM6TUIZmZmVkvyZKEDQfmsG0SFoDHhOXoyaXrOPunDzFycD3Xf/KtTBg1qNQhmZmZWS/K8nTkpF0QhxVYtLKBc2Y8zLCB/bjx/LcxdsTAUodkZmZmvaymuwskXShpRMH+SEn/kGtUfdiytY189GcPA/CLT0x3AmZmZlaluk3CgE9FxNr2nYh4FfhUlsIlHSdpgaSFki7p4PxISb+V9LikhyUdmDnyKtTQ1MK518xifeMWrjtvuseAmZmZVbEsY8JqJCkiAkBSLVDf3U3pdT8CjgGWALMkzYyIpwou+wowNyI+KGn/9Pp397QSley2R5dy+d0LWLq2kQF1NTS3tnHdedM5cJyfgjQzM6tmWVrC7gZulvRuSUcDNwC/z3DfdGBhRCyKiGbgRuDkomumAvcARMTTwCRJe2SOvsLd9uhSvnzrEyxd2wjA5pY2aiVWNzSXODIzMzPLW5Yk7EvAn4DPABeSJE3/nOG+ccDigv0l6bFCjwGnAEiaDuwNjM9QdlW4/O4FNG5p3ebYlrbg8rsXlCgiMzMz21Wyrh35k/TVEx2tqRNF+98Gvi9pLskC4Y8CLdsVJJ0PnA8wceLEHoZRvpalLWBZj5uZmVn1yLJ25BNsnzwREQd1c+sSYELB/nhgWVEZ64Fz0/cR8Hz6Kn6vq4GrAaZNm7ZdLJVqr+EDWLZu83bH/USkmZlZ9cvSHfkY0Ap8FTip4NWdWcAUSZMl1QOnAzMLL5A0Ij0H8Engz2li1idMmzRqu2MD+9Vy8bH7lSAaMzMz25WydEeenU4d8U2Sxby/FhHbtVZ1cF+LpItIBvbXAjMiYp6kC9LzVwFvAH4uqRV4CvjEjlelsjy/aiO/n/cybxw3nNUNTSxft5mxIwZy8bH78YG3FA+dMzMzs2qTpTtyFEk34nnA4cCvJT0YERd1d29E3AncWXTsqoLtB4ApPQ260kUEX7n1CfrX1fCzc6ax+7ABpQ7JzMzMdrEs84TNYeuYsPbB9ifkE07f8OvZS3hg0Wou++AbnYCZmZn1UVm6IyfvikD6inWNW/j275/m0EkjOf3QCd3fYGZmZlUpa3dkscuBocAVaZeiZfSjexfy6qZmvn7SdGpqOprFw8zMzPqCLN2Ry4GlbDvv114R4X60Hnph1Uau+dvzfPjg8V6WyMzMrI/LkoQ9FRFvKTwg6dGc4qlq37prPv1qazwFhZmZmWWaJ2yIpMMlTZXU3nxTNROm7iqPL1nL3fNe4dPvfJ0H45uZmVmmlrCnSSZqHQJMlLQYGJtrVFXo+//7LCMG9eO8IyaVOhQzMzMrA1mejtxmdnxJbwPukjQD+ElEzMoruGrx+JK13PP0Ci4+dj+GDuhX6nDMzMysDGRpCdtGRDwoaSpQD6zs/ZCqT3sr2McO27vUoZiZmVmZ6HESBhARy3s7kGr1zCsbuOfpFfzTMfu6FczMzMxek2Vgvu2Ea/72PP3rajj7bW4FMzMzs62chOVozcZmbn1kKaccPJ5Rg+tLHY6ZmZmVkSwz5p/S0fGIuLX3w6kuv3roRZpa2jjv8EmlDsXMzMzKTKdJmKQjI+J+4CZgPjCbrbPmB+AkrAstrW38/IEXeceU0UzZY2ipwzEzM7My01V35LfSnweSzBU2BPh2RJwbEeflHlmFu2/BSlZsaPJYMDMzM+tQlnnCFgCnSjoY+J6kZcClEbE09+gq2M2zFzN6SH+O3n/3UodiZmZmZairJOwrAJJ+yNZlihYBRwLPAoPyDa1yrdzQxJ+eXsEnjphMv1o/+2BmZmbb6zQJi4j70s3ZRaeK963Ibx9dQktb8JFp40sdipmZmZWpLN2R10mqB/ZNDy2IiC35hlW5IoJfz17CwRNH8PrdPSDfzMzMOtZtX5mkd5F0P/4I+DHwjKR35htW5XrmlQaeXdHABw92K5iZmZl1LsuyRd8F3psO0EfSvsANwCF5Blap7nxiORIcd8CepQ7FzMzMyliWUeP92hMwgIh4BvAiiJ2484nlTJ80ijFD+5c6FDMzMytjWZKw2ZJ+Juld6eu/gTl5B1aJnn1lA8+uaOCEN+5V6lDMzMyszGVJwj4DzAM+B/wj8BRwQZbCJR0naYGkhZIu6eD8cEm/k/SYpHmSzu1J8OXmridfBuC4A90VaWZmZl3L8nRkE/C99JWZpFqSwfzHAEuAWZJmRsRTBZddCDwVESdJGgMskHR9RDT35L3KxR+feoWDJ45gj2EDSh2KmZmZlbksC3g/z9bJWl8TEft0c+t0YGFELErLuRE4maQl7bVigKGSRLIs0hqgJVvo5WXFhs08sXQdFx+7X6lDMTMzswqQ5enIaSQLd/8JOKoHZY8DFhfsLwHeWnTNlcBMYBkwFDgtItp68B5l48/PrALgyH3HlDgSMzMzqwTdjgmLiNURsQpoSbdXR8TqDGWro+KK9o8F5gJjgTcDV0oatl1B0vmSZkuavXLlygxvvevdu2AFuw/tzwFjtwvfzMzMbDtZJmsdJWkUUCtpZMF+d5YAEwr2x5O0eBU6F7g1EguB54H9iwuKiKsjYlpETBszpvxamlpa2/jLMys5ct8xJD2rZmZmZl3L0h05h6QFS8Aj6bEAuhsTNguYImkysBQ4HTiz6JqXgHcDf5G0B7AfySLhFeXRxWtZv7mFo/bfvdShmJmZWYXI8nTk5B0pOCJaJF0E3A3UAjMiYp6kC9LzVwH/Dlwr6QmSJO9LaddnRfnLs6uoERz++tGlDsXMzMwqRJanIz/W0fGI+Hl390bEncCdRceuKtheBry3+zDL28PPr+aAscMZPtALCZiZmVk2WSZrPTR9XV6wPS3PoCpJc0sbj760lkMnZRkmZ2ZmZpbI0h35WQBJR7Rv21ZPLF1LU0sb0yc7CTMzM7PssrSEtdtuwlaDh59/FYBDJ40scSRmZmZWSbKMCfshSQI2XtIP2o9HxOfyDKxSPPz8al6/+xB2G9K/1KGYmZlZBckyRcXs9OecPAOpRK1twewXX+XEg8aWOhQzMzOrMFnGhF0naSAwMSIW7IKYKsYzr2xgw+YWpk92V6SZmZn1TJYZ808iWVro9+n+myXNzDmuivDEknUAvGn8iNIGYmZmZhUny8D8S4HpwFqAiJgL7NAErtXmiaXrGNK/jkm7DS51KGZmZlZhsiRhLRGxruiYn5QEnly2jqljh1FT4/UizczMrGeyJGFPSjqTZAHvKenTkn/POa6y19Laxvzl63njuOGlDsXMzMwqUJYk7LPAAUATcAOwHvh8jjFVhOdWbmTzljYOHDes1KGYmZlZBcrydOQm4Kvpy1JPLE16aN0SZmZmZjsiy2St99LBGLCIODqXiCrEk0vXMai+lsmjh5Q6FDMzM6tAWSZr/SIg4JfAWfmGUzmeXLqOqXsNo9aD8s3MzGwHZOmOnAMgqbF9u6+LCOYvX8+HDxlf6lDMzMysQnkB7x2wbN1mNja3su+eQ0sdipmZmVWoLGPCNpAkYIMkrSfpmoyI6LOPBT77ygYApuzuJMzMzMx2TJbuSGcaRRauaADg9bt7UL6ZmZntmCxrR35Q0vCC/RGSPpBrVGVu4YoGdhtcz6jB9aUOxczMzCpUljFhXy9ctigi1gJfzy2iCvDsigZe51YwMzMz2wlZkrCOrskytUVViggWrmhgipMwMzMz2wlZkrDZkr4n6XWS9pF0BdBnp6pY2dDEusYtHg9mZmZmOyXr2pHNwE3Ar4HNwIV5BlXO2gfl+8lIMzMz2xlZno7cCFyyI4VLOg74PlAL/DQivl10/mK2zsJfB7wBGBMRa3bk/XaF5/xkpJmZmfWCLPOE/amj492tHSmpFvgRcAywBJglaWZEPFVQxuXA5en1JwFfKOcEDODF1ZsY0K+GPYb1L3UoZmZmVsGyDLAfS9Ja1dP1I6cDCyNiEYCkG4GTgac6uf4M4IaMZZfMS2s2MWHkICSvGWlmZmY7LsuYsM0RMSciZgPDgREZ15AcBywu2F+SHtuOpEHAccAtnZw/X9JsSbNXrlyZ4a3zs/jVRiaMGlTSGMzMzKzyZUnCXpX0A0nXALOACyVlmSeso6aiztafPAn4W2ddkRFxdURMi4hpY8aMyfDW+YgIlqzZxISRA0sWg5mZmVWHLEnYKcAzwMPAmcCHgE0Z7lsCTCjYHw8s6+Ta06mArsi1m7awoanFLWFmZma207I8HfkqcGXR4cszlD0LmCJpMrCUJNE6s/iidEmkI4GzM5RZUotfTXJPJ2FmZma2s7K0hAEg6QRJSyS9LKnbwfkR0QJcBNwNzAdujoh5ki6QdEHBpR8E/pBOhVHWFq9pBGDCSCdhZmZmtnN6svzQ14F3AQ3APcD13d0QEXcCdxYdu6po/1rg2h7EUTJbW8I8JszMzMx2Tk+SsH4RsRBAUlNO8ZS1l9ZsYuSgfgwd0K/UoZiZmVmFyzJZ6w/SzfHptuhkqolqt3jNJo8HMzMzs16RpSXsZOBrbLto9+x8wilvS15tZOrYYaUOw8zMzKpAliRsTURcl3skZS4iWPpqI++dukepQzEzM7MqkOXpyJNyj6ICrNnYTHNrG3sNH1DqUMzMzKwKdJqESfpEwfZvJa2U9IqkWySN3zXhlY/l6zYDsOdwPxlpZmZmO6+rlrDPpD+vAWYCe5EMyP8dMCPnuMrOy2kS5pYwMzMz6w1dJWFNkvoDe0TENRHRkr6uBXbfNeGVj+Xr21vCnISZmZnZzusqCbsNuARYIelsSbXp6yxgwy6Jroy8sm4ztTVi9JD+pQ7FzMzMqkBXSdh3gVpgMvBzoAlYBXwM+EQX91Wl5es2s8fQ/tTWqNShmJmZWRXodIqKiGgjmR/sa7sunPL18vpGd0WamZlZr+l2igpJwyV9T9Ls9PVdScN3RXDlZPm6zU7CzMzMrNdkmSdsBskYsFPT13qSJyb7jIjg5XWb2XOYp6cwMzOz3pFlxvzXRcSHCva/IWluTvGUpQ1NLWxqbvX0FGZmZtZrsrSENUo6on1H0uFAY34hlZ/2OcL2cBJmZmZmvSRLS9gFwM8LxoG9CpyTX0jlZ+WGJgB2H+rpKczMzKx3dJuERcRjwJskDUv31+ceVZlZ1ZAkYZ4jzMzMzHpLlpYwoG8mX+1WNTQDMHpIfYkjMTMzs2qRZUxYn7eqoYl+tWL4wH6lDsXMzMyqhJOwDFZtaGK3wf2RPFu+mZmZ9Y5uuyMlndLR8Yi4tffDKU+rGprYzV2RZmZm1ouyjAm7CZgPzAbam4IC6DNJ2OqNzR6Ub2ZmZr0qS3fkgcDTwBDg2xFxbkScl6VwScdJWiBpoaRLOrnmXZLmSpon6f7soe86qzY0OQkzMzOzXpVliooFwKmSDga+J2kZcGlELO3qPkm1wI+AY4AlwCxJMyPiqYJrRgA/Bo6LiJck7b7jVclHRLCqodlPRpqZmVmvyjIm7Ick3Y8Ai4AjgWeBQd3cOh1YGBGL0nJuBE4Gniq45kzg1oh4CSAiVvQo+l1gQ1MLza1tbgkzMzOzXpVlTNjsbvY7Mw5YXLC/BHhr0TX7Av0k3QcMBb4fET/PWP4usSqdLX/0ULeEmZmZWe/J0h153Q6W3dF8DlG0XwccArwbGAg8IOnBiHhmm4Kk84HzASZOnLiD4eyY9oladxvsljAzMzPrPVm6IzewbfIkICJiWDe3LgEmFOyPB5Z1cM2qiNgIbJT0Z+BNwDZJWERcDVwNMG3atOJELldessjMzMzykOXpyIURMazgNTRDAgYwC5giabKkeuB0YGbRNf8DvENSnaRBJN2V83tUg5ytfi0Jc3ekmZmZ9Z4sY8IGSHoT0AQsj4h1WQqOiBZJFwF3A7XAjIiYJ+mC9PxVETFf0u+Bx4E24KcR8eQO1SQnr27aAsDIwU7CzMzMrPdkScJeBn5IMmZrrKQ1wLkR0e0A/Yi4E7iz6NhVRfuXA5dnjngXW7tpC0P619Gv1is8mZmZWe/JMjD/qMJ9SUcAVwHT8gqqnKxtbPbC3WZmZtbrety8ExF/BS7IIZaytG7TFkYMchJmZmZmvavbJEzScElXSJojabak75JM1tonrG10EmZmZma9L0tL2AxgPfAR4NR0+5o8gyonaze5O9LMzMx6X5aB+a+LiA8V7H9D0tyc4ik76xpbGD7QT0aamZlZ78rSEtaYDsYHQNLhQGN+IZWPiGBdY7O7I83MzKzXZWkJ+wxwnaThJLPlrwE+nmdQ5WJTcytbWoMR7o40MzOzXpZlioq5wJskDUv31+cdVLlY25hM1OqWMDMzM+ttWdaOPLhoH5LJVVcDV0TEA/mEVnprNyWLd3tMmJmZmfW2LN2R95OsA6mCY4dkXD+yoq3b5JYwMzMzy0eWJGxhRBxdeEDSoznFU1bWuTvSzMzMcpIlCRsl6SzgVWBxRDwBRL5hlYfXxoS5O9LMzMx6WZYkbCZwODAEmCxpL2BUrlGVifVpEjZ0QJaPyczMzCy7LE9HfrZwX9LewCxJfwL+LSLuyym2ktvY1IIEg+prSx2KmZmZVZkeN/FExIvA7jnEUnYamloZXF/X/kSomZmZWa/JMmN+n7WpucWtYGZmZpYLJ2FdaGhqYUh/jwczMzOz3uckrAubmlsZ1N8tYWZmZtb7MjXzSHofcAAwoP1YRPxbXkGVi4amFgbXuyXMzMzMel+3LWGSrgJOAz5LMmv+R4C9c46rLGx0d6SZmZnlJEt35Nsj4mPAqxHxDeAwYEK+YZWHpDvSSZiZmZn1vixJWGP6c5OkscAWYHJ+IZWPZGC+x4SZmZlZ78vSzHO7pBHA5cAjJEsW/TTPoMrFpqYWBnlMmJmZmeWg25awiPj3iFgbEbeQjAXbPyL+NUvhko6TtEDSQkmXdHD+XZLWSZqbvr7W8yrko60t2NjcymB3R5qZmVkOus0wJH2sg2NExM+7ua8W+BFwDLCEZKmjmRHxVNGlf4mIE3sQ8y6xaUsrAIM9WauZmZnlIEszz7XAg8BskqcjIemS7DIJA6YDCyNiEYCkG4GTgeIkrCxtamoBcEuYmZmZ5SLLwPwDgL8C+wPPAd+IiM9luG8csLhgf0l6rNhhkh6TdJekAzKUu0s0vJaEuSXMzMzMel+WMWHzI+KfgZOAqcC9GcvuaNXrKNp/BNg7It4E/BC4rcOCpPMlzZY0e+XKlRnffudsak66Iz0w38zMzPKQZbLWfSV9G5gJzAeOzlj2EradT2w8sKzwgohYHxEN6fadQD9Jo4sLioirI2JaREwbM2ZMxrffOY1b2pMwt4SZmZlZ78vSHfk0cCTwDMn8YP8q6QcZ7psFTJE0WVI9cDpJIvcaSXtKUro9PY1ndQ/iz83mNAkb2M9JmJmZmfW+LH1t57F9N2K3IqJF0kXA3UAtMCMi5km6ID1/FfBh4DOSWkgmhT09Inr8XnloTLsjBzgJMzMzsxx0m4RFxLU7WnjaxXhn0bGrCravBK7c0fLz1N4d6STMzMzM8pBlnrBFxYeAiIh98gmpPLzWHekxYWZmZpaDLN2Rs4A9gF8BvwOac42oTLR3R3pMmJmZmeUhyxQVpwEfBPoB1wAXRURZDJ7PU+OWNsBJmJmZmeUjy9ORAG3swOD8StbeHdm/LutHZGZmZpZdljFh1wNjgRuAjwPNkkZFxJqcYyupzVtaGdCvhpqajuacNTMzM9s5WcaEHU7SCvZl4BLSgflAVQ/Mb9zS6icjzczMLDdZpqiYtAviKDuNza0eD2ZmZma5ybJs0WxJ/yBpxC6Ip2w0bnESZmZmZvnJMur8dGAcMFvSjZKObV9qqJptdnekmZmZ5SjLFBULI+KrwL4kc4XNAF6S9A1Jo/IOsFQat7R6olYzMzPLTab5FyQdBHwXuBy4hWTNx/XAn/ILrbQ2b2lzd6SZmZnlJssUFXOAtcDPgEsioik99ZCkw3OMraQam1sZOai+1GGYmZlZlcoyRcVHIqJ4/UgAIuKUXo6nbLTPE2ZmZmaWhyxZxicLn4yUNFLSN/MLqTz46UgzMzPLU5Yk7PiIWNu+ExGvAifkFlGZ8GStZmZmlqcsSVitpP7tO5IGAv27uL4qNG1pc3ekmZmZ5SbLmLBfAvdIuoZkuaLzgOtyjaoMbGlto1+tkzAzMzPLR5Zli/5D0uPAe0jWjfz3iLg798hKqK0taGkL6uuchJmZmVk+srSEERG/B36fcyxlo7m1DcAtYWZmZpYbZxkd2JImYf3dEmZmZmY5cZbRgeYWt4SZmZlZvpxldGBLawBOwszMzCw/WZYtep7kqcjXDgEREfvkFlWJtXdHemC+mZmZ5SVLlrEBOBSYDjQAh6T73ZJ0nKQFkhZKuqSL6w6V1Crpw1nKzVvTa92RKnEkZmZmVq0yNfVExGpgDTAOeH+63yVJtcCPgOOBqcAZkqZ2ct13gLKZ9sID883MzCxvWbKMhZJmAn8AbgUOTidu7c50YGFELIqIZuBG4OQOrvsscAuwImPMufPAfDMzM8tblnnCTgOOBVqBP0REq6SPZLhvHLC4YH8J8NbCCySNAz4IHE3GLs5dYYvnCTMzM7OcZZkxfwtwe9GxX2cou6MBVVG0/1/Al9LErvOCpPOB8wEmTpyY4a13TrMH5puZmVnOsjwdOQX4Fsm4rgHtxzM8HbkEmFCwPx5YVnTNNODGNAEbDZwgqSUibiu8KCKuBq4GmDZtWnEi1+vcHWlmZmZ5y9IdeQ3wdeAK4CjgXDpu5So2C5giaTKwFDgdOLPwgoiY3L4t6Vrg9uIErBTa5wnzwHwzMzPLS5YsY2BE3AMoIl6MiEtJxnB1KSJagItInnqcD9wcEfMkXSDpgp0JOm9uCTMzM7O8ZWkJ2yypBnhW0kUkrVq7Zyk8Iu4E7iw6dlUn1348S5m7wtaB+Z4nzMzMzPKRpann88Ag4HMkE7V+FDgnx5hKrr0lzAPzzczMLC9Zno6clW42kIwHq3qvPR3p7kgzMzPLSZanI+9l+6kliIhux4VVKs8TZmZmZnnLMibsiyRPQ/4SOCvfcMpDS/p0ZD93R5qZmVlOsnRHzgGQ1Ni+Xe1a2pIkrK7GA/PNzMwsHz1p6sl9ktRy0dqWdEfWOgkzMzOznGQZE7aBJAEbJGk9SddkRMSwvIMrlfaWsNoullIyMzMz2xlZuiOH7opAyklrW1AjqHFLmJmZmeWk2+5ISQMlHZhuny7pIklV2woGSUtYXY0H5ZuZmVl+sjwdeRuwh6SXgRXABuDXwLE5xlVSrW3h8WBmZmaWqyxJ2ATgQGBxRIwDkPRYrlGVWEtr+MlIMzMzy1WWJGwLMAJYLWkkycD8qtba1ubxYGZmZparLEnYcGA2SfL1SHqsqqerSMaEOQkzMzOz/GR5OnLSLoijrHhMmJmZmeUtyzxhp3R0PCJu7f1wyoNbwszMzCxvWbojbwLms7VLEpLuyKpNwtragtpaJ2FmZmaWnyxJ2IHAvwNDgH+NiAX5hlR6nifMzMzM8pZlTNgC4FRJBwPfk7QMuDQiluYeXYl4TJiZmZnlLcuYsB+y9WnIRcCRwLPAoBzjKqmWtjaPCTMzM7NcZemOnN3NftVxS5iZmZnlLUsS1j8irs49kjLipyPNzMwsb1lGn1+QexRlxi1hZmZmlrcsLWEjOporrKrnCWv105FmZmaWr6zLFp3ItmtGZponTNJxwPeBWuCnEfHtovMnk0x/0Qa0AJ+PiL9mCz0/bgkzMzOzvGVJwl6KiPN6WrCkWuBHwDHAEmCWpJkR8VTBZfcAMyMiJB0E3Azs39P36m0tbW3075flozEzMzPbMVn63ObtYNnTgYURsSgimoEbgZMLL4iIhohon/5iMGWyMLhbwszMzCxv3SZhEXH2DpY9DlhcsL8kPbYNSR+U9DRwB9Bhi5uk8yXNljR75cqVOxhOdn460szMzPLWbRImaYuk9ZI2pNsbJK3PUHZHWcx2LV0R8duI2B/4AMn4sO1virg6IqZFxLQxY8ZkeOud45YwMzMzy1uW7sgnI2JYRAxNt4dGxLAM9y0BJhTsjweWdXZxRPwZeJ2k0RnKzpXXjjQzM7O8Zck0+gNIqgf2kXSppCzNRLOAKZImp/eeDswsvEDS69vLStemrAdW96QCeXBLmJmZmeUtyyOAf5b0GNAP+C9gPXAncHxXN0VEi6SLgLtJpqiYERHzJF2Qnr8K+BDwMUlbgEbgtIKB+iXjtSPNzMwsb90mYRFxgaQDgdaImA8g6YEshUfEnSQJW+Gxqwq2vwN8p0cR7wKtrW4JMzMzs3x1m4RJGkU6livdBliQZ1Cl1tIW1NU6CTMzM7P8ZOmOXA4sZfsZ8/fJJaIy0NoW1GQa9mZmZma2Y7IkYU9FxFtyj6SMeJ4wMzMzy1umtSPTNR6bSLoln4qIlnzDKq3k6UhPUWFmZmb5yZKE3U/yFONAYCywt6RPRcRduUZWQi1tbR4TZmZmZrnK8nTkuYX7kl4P3AZUbRLW1oafjjQzM7NcZVm2aFThfkQsBI7JLaIy4HnCzMzMLG9ZBj49JOnXkk5on90+IpbnHFfJtLUFbeGWMDMzM8tXliRsX+Bq4KPAQkmXSdo337BKpzWdsN8tYWZmZpanbpOwSPwxIs4APgmcA8ySdL+kw3KPcBdrbUuSMD8daWZmZnnKMmP+bsDZJC1hrwCfJVmI+83Ar4HJOca3y7W0uSXMzMzM8pdliooHgF8AH4iIJQXHZ0u6qpN7KlZra3tLmJMwMzMzy0+WJGy/iHSgVJF0Ae6q0tLWBuB5wszMzCxXWZKwe7TtOooiGSp2dD4hldbWMWFOwszMzCw/WZKwwUAbMAN4JN9wSs9jwszMzGxXyPJ05FuB80imqrgMeEtEzMk7sFLx05FmZma2K2TNNJ4G7gU2ANPzC6f03BJmZmZmu0KWKSouAw4B7gY+ExErc4+qhFrTgfkeE2ZmZmZ5yjIm7BJgI/B24NJ06aKIiGG5RlYibgkzMzOzXaHbJCwi+tTgqBbPE2ZmZma7QJaWMCS9H3hnuntfRNyeX0il9boxQ7j9s0cwYdSgUodiZmZmVSzLmLBvA4cC16eH/lHSERFxSa6RlcjA+loOHDe81GGYmZlZlcvS1XgCcExEzIiIGcBx6bFuSTpO0gJJCyVtl7RJOkvS4+nr75Le1LPwzczMzCpT1vFeIwq2MzUTSaoFfgQcD0wFzpA0teiy54EjI+Ig4N+BqzPGY2ZmZlbRsowJ+xbwqKR7SZYseifwlQz3TQcWRsQiAEk3AicDT7VfEBF/L7j+QWB8xrjNzMzMKlqWpyNvkHQfybgwAV+KiJczlD0OWFywvwR4axfXfwK4K0O5ZmZmZhWv0+5ISe9r346I5RExMyL+B9go6YcZyu5ojofo5L2OIknCvtTJ+fMlzZY0e+XKqp4r1szMzPqIrsaEfV/SJwoPSDoTeBxYkaHsJcCEgv3xwLLiiyQdBPwUODkiVndUUERcHRHTImLamDFjMry1mZmZWXnrqjvyHcAdksYBNwI/BpqB90TEcxnKngVMkTQZWAqcDpxZeIGkicCtwEcj4pkdiN/MzMysInXaEhYRy4EjSZKxx4GfRsQJGRMwIqIFuIhkzcn5wM0RMU/SBZIuSC/7GrAb8GNJcyXN3om6mJmZmVUMRXQ4TGvrBVIdMINkmopTI2LzLoirU9OmTYvZs52rmZmZWfmTNCcipnV0rtPuSEkb2DqQXsBgYI2kVqp4AW8zMzOzXaHblrByI2kl8GLObzMaWJXze5Srvlr3vlpv6Lt176v1hr5bd9e77ymHuu8dER0+VVhxSdiuIGl2Z02H1a6v1r2v1hv6bt37ar2h79bd9e57yr3uWZctMjMzM7Ne5CTMzMzMrASchHWsLy8k3lfr3lfrDX237n213tB36+569z1lXXePCTMzMzMrAbeEmZmZmZWAk7Aiko6TtEDSQkmXlDqe3iRpgqR7Jc2XNE/SP6bHL5W0NF21YK6kEwru+XL6WSyQdGzpot85kl6Q9EThygySRkn6o6Rn058jC66vlnrvV/C9zpW0XtLnq/E7lzRD0gpJTxYc6/F3LOmQ9M/KQkk/kKRdXZee6qTul0t6WtLjkn4raUR6fJKkxoLv/qqCeyqq7p3Uu8d/tiut3tBp3W8qqPcLkuamx6vpO+/s91hl/l2PCL/SF1ALPAfsA9QDjwFTSx1XL9ZvL+DgdHso8AwwFbgU+GIH109NP4P+wOT0s6ktdT12sO4vAKOLjv0HcEm6fQnwnWqrd1F9a4GXgb2r8TsH3gkcDDy5M98x8DBwGMkk1XcBx5e6bjtY9/cCden2dwrqPqnwuqJyKqrundS7x3+2K63endW96Px3ga9V4Xfe2e+xivy77pawbU0HFkbEoohoJlm4/OQSx9RrImJ5RDySbm8gWdNzXBe3nAzcGBFNEfE8sJDkM6oWJwPXpdvXAR8oOF6N9X438FxEdDXZccXWPSL+DKwpOtyj71jSXsCwiHggkn+lf15wT9nqqO4R8YdI1vAFeBAY31UZlVj3Tr7zzlT9d94ubdE5FbihqzIqse5d/B6ryL/rTsK2NQ5YXLC/hK6TlIolaRLwFuCh9NBFabfFjIJm3Gr6PAL4g6Q5ks5Pj+0RyUL1pD93T49XU70Lnc62/yhX+3cOPf+Ox6Xbxccr3Xkk/9NvN1nSo5Lul/SO9Fg11b0nf7arqd7t3gG8EhHPFhyruu+86PdYRf5ddxK2rY76g6vu8VFJQ4BbgM9HxHrgJ8DrgDcDy0masaG6Po/DI+Jg4HjgQknv7OLaaqo3AJLqgfcDv04P9YXvvCud1bPq6i/pq0ALcH16aDkwMSLeAvwT8CtJw6ieuvf0z3a11LvQGWz7H66q+847+D3W6aUdHCub791J2LaWABMK9scDy0oUSy4k9SP5g3t9RNwKEBGvRERrRLQB/83W7qeq+TwiYln6cwXwW5I6vpI2Sbc3y69IL6+aehc4HngkIl6BvvGdp3r6HS9h2267iq6/pHOAE4Gz0i4X0m6Z1en2HJIxMvtSJXXfgT/bVVHvdpLqgFOAm9qPVdt33tHvMSr077qTsG3NAqZImpy2HJwOzCxxTL0mHSfwM2B+RHyv4PheBZd9EGh/2mYmcLqk/pImA1NIBjJWFEmDJQ1t3yYZsPwkSf3OSS87B/ifdLsq6l1km/8ZV/t3XqBH33HajbFB0tvSvy8fK7inokg6DvgS8P6I2FRwfIyk2nR7H5K6L6qWuvf0z3a11LvAe4CnI+K1rrZq+s47+z1Gpf5d39VPApT7CziB5GmL54CvljqeXq7bESTNrY8Dc9PXCcAvgCfS4zOBvQru+Wr6WSygzJ+a6aLe+5A8HfMYMK/9ewV2A+4Bnk1/jqqmehfUZRCwGhhecKzqvnOSJHM5sIXkf7mf2JHvGJhG8ov7OeBK0kmty/nVSd0XkoyFaf+7flV67YfSvwePAY8AJ1Vq3Tupd4//bFdavTure3r8WuCComur6Tvv7PdYRf5d94z5ZmZmZiXg7kgzMzOzEnASZmZmZlYCTsLMzMzMSsBJmJmZmVkJOAkzMzMzKwEnYWZVSlJD0f7HJV1ZqngqiaQBkn4nabak/yh1PGZWnepKHYCZWbmJiM3ASaWOw8yqm1vCzPogSXtLuidd5PgeSRPT49dKWlIwu/ZnJEW6UC6Szpb0sKS5kv5fwXUNkr4r6ZG0vDEdvOe1kp5P750r6e3p8Y9LWpkeWyPpw+nxn6QtUfMkfaOgnBckjU63R0t6oaicxyQtlHRGevxSSV8suP92Se9Kt8+Q9ISkJyV9p+CahoLtv0i6vYP6vNayKOl0SXdL6pe2ol2TlvuopKMKrg9J+6f7b0j3P15QryfSz+GJgnp1Vd6VBfFcWVDWIUoWap6TxtW+nMt9kqYV11PS9QWff/t3dEFHraeSpkm6r/jzMLOecxJmVr0GFiQ8c4F/Kzh3JfDziDiIZGHnHxScWwocm26fTDLzOpLeAJxGshj6m4FW4Kz0usEka1MeDNwPfL2TmC6OiDenr7+nx2qBG9IyC5cJ+2pETAMOAo6UdFCGOt8UEW8Cvgx8pKsLJY0FvgMcTbLY86GSPlB0zfuA4d2U827gH4EPR8QW4EKAiHgjyXJR10kakF7+MHBeun0e8FBRcUeln8NRBce6Kq+jePoBP0zjOQSYAfzfruoQEWcVfP7t39FVXd1jZjvP3ZFm1asx/cUKJC0nJMt0ABxGssgvJMu8FI57+gXwUUkvkSwB0r7I7buBQ4BZkgAGsnWR3Da2Lhj8S+BWshsIbO7g+KmSzif5d2ovYCrJUiUA90pqJUngCp0m6Z3AJJKlWtp9QdLZ6fZk4D+BQ4H7ImIlJK1BwDuB29J9kSx3chlwNh17I8mac+dExIb02BEkSRAR8bSkF0kWS4Zkfdq3pEnUm4HZnZRbqKvyTpN0RLo9Li1vP+BA4I/p91RLsrxNu+slNabbAzO8f/t7bAG+Abyc4R4zy8BJmJlBshZbu5eBfsDFwPfZ2ioj4LqI+HIPy+vOWGBZ4QElC+1+ETg0Il6VdC1Q2PpzVESsSrslCxOZmyLiIklTgNtJEhKAKyLiP9Oy27sW1U1cZwD30XXS8QbgTOAySXelY8m6K/f3JEnVXSTrmnanq/JuioiLIOmOLLh+XkQc1sk9Z0XE7PSehk6u2e490s/0PpLWUTPrBe6ONOub/g6cnm6fBfy16Pw1wO4R8UjBsXuAD0vaHUDSKEl7p+dqgA+n22d2UF6HJA0ETgT+VnRqGLARWCdpD+D4LOUV2ECyoG9XHiLp5hytZGzbGSRdqZDU5wts20LYkZsj4nbgN8DX0mN/Ju2mlbQvMJFk4eB2vwDeTtJimEV35RVbAIyRdFh6Tz9JB2R8r66swf9xN+tV/gtl1jd9Dpgh6WJgJXBu4cmIuAO4o+jYU5L+BfiDpBqS7qkLgRdJEqYDJM0B1pGMHcviLpKWlllF7/WYpEeBecAitk/SOtPeddYf+D9dXRgRyyV9GbiXpPXozoj4n/T0QOA3EbE27dLrzreAhyXdCPwYuErSE0AL8PGIaGovJyJWAAcAZCy7y/I6qFezkocbfiBpOMm/8/9F8lnuiFMkvRkYQtI6ama9RBE96TUwM9uepIaIGFLqOMzMKom7I83MzMxKwC1hZmZmZiXgljAzMzOzEnASZmZmZlYCTsLMzMzMSsBJmJmZmVkJOAkzMzMzKwEnYWZmZmYl8P8BnqexIHyodMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    "plt.xlabel(\"Номер гланвной компоненты\")\n",
    "plt.ylabel(\"Комулятивная сумма доли объясненной дисперсии\")\n",
    "plt.title(\"Вклад главных компонент\")\n",
    "plt.plot(range(1, scaled_data.shape[0]+1), ss[:])\n",
    "plt.scatter(col, ss[col])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "445d55c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=col+1)\n",
    "pca.fit(scaled_data)\n",
    "x_pca = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "ff215b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 116)"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "6aba190f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Итоговые данные \n",
    "data_pca = pd.DataFrame(data=x_pca,\n",
    "          index=pd.RangeIndex(range(0, scaled_data.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, col+1)))\n",
    "X_train = data_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "258079b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-227.624452</td>\n",
       "      <td>23.114436</td>\n",
       "      <td>45.998859</td>\n",
       "      <td>30.429224</td>\n",
       "      <td>10.766937</td>\n",
       "      <td>28.579861</td>\n",
       "      <td>-15.364518</td>\n",
       "      <td>67.727508</td>\n",
       "      <td>42.606689</td>\n",
       "      <td>5.488387</td>\n",
       "      <td>...</td>\n",
       "      <td>14.798696</td>\n",
       "      <td>-2.499685</td>\n",
       "      <td>-10.561960</td>\n",
       "      <td>-7.045339</td>\n",
       "      <td>-11.988090</td>\n",
       "      <td>9.170574</td>\n",
       "      <td>3.013181</td>\n",
       "      <td>2.774824</td>\n",
       "      <td>-1.689797</td>\n",
       "      <td>-8.083196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.173780</td>\n",
       "      <td>-50.700727</td>\n",
       "      <td>-36.805734</td>\n",
       "      <td>49.231019</td>\n",
       "      <td>-1.186746</td>\n",
       "      <td>-22.014536</td>\n",
       "      <td>14.515313</td>\n",
       "      <td>-12.539149</td>\n",
       "      <td>21.923205</td>\n",
       "      <td>-6.691319</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.705012</td>\n",
       "      <td>2.577868</td>\n",
       "      <td>10.209406</td>\n",
       "      <td>-7.727766</td>\n",
       "      <td>2.913083</td>\n",
       "      <td>-7.042295</td>\n",
       "      <td>0.889421</td>\n",
       "      <td>-2.898908</td>\n",
       "      <td>3.268386</td>\n",
       "      <td>-7.134964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.541151</td>\n",
       "      <td>-22.007590</td>\n",
       "      <td>9.191453</td>\n",
       "      <td>-64.862172</td>\n",
       "      <td>-15.026559</td>\n",
       "      <td>18.673351</td>\n",
       "      <td>-19.001203</td>\n",
       "      <td>39.375727</td>\n",
       "      <td>-2.815390</td>\n",
       "      <td>24.560810</td>\n",
       "      <td>...</td>\n",
       "      <td>1.877436</td>\n",
       "      <td>-4.299011</td>\n",
       "      <td>-7.039255</td>\n",
       "      <td>0.613479</td>\n",
       "      <td>-0.286574</td>\n",
       "      <td>-6.739499</td>\n",
       "      <td>0.542982</td>\n",
       "      <td>2.085152</td>\n",
       "      <td>-1.423430</td>\n",
       "      <td>5.378897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.986078</td>\n",
       "      <td>57.462595</td>\n",
       "      <td>-18.866503</td>\n",
       "      <td>42.295773</td>\n",
       "      <td>-5.215643</td>\n",
       "      <td>11.678576</td>\n",
       "      <td>-19.067189</td>\n",
       "      <td>29.713431</td>\n",
       "      <td>36.130609</td>\n",
       "      <td>-37.808531</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.715295</td>\n",
       "      <td>-4.704898</td>\n",
       "      <td>1.990218</td>\n",
       "      <td>13.533275</td>\n",
       "      <td>4.085548</td>\n",
       "      <td>-4.735775</td>\n",
       "      <td>1.650974</td>\n",
       "      <td>-8.248758</td>\n",
       "      <td>-2.003846</td>\n",
       "      <td>-3.749783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.779165</td>\n",
       "      <td>-37.875034</td>\n",
       "      <td>55.938902</td>\n",
       "      <td>-32.872510</td>\n",
       "      <td>-63.380924</td>\n",
       "      <td>-8.965753</td>\n",
       "      <td>2.447920</td>\n",
       "      <td>25.599496</td>\n",
       "      <td>2.389311</td>\n",
       "      <td>10.859616</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603873</td>\n",
       "      <td>8.946189</td>\n",
       "      <td>-1.031476</td>\n",
       "      <td>-7.081917</td>\n",
       "      <td>-3.725180</td>\n",
       "      <td>0.982444</td>\n",
       "      <td>-0.007442</td>\n",
       "      <td>4.195274</td>\n",
       "      <td>-3.589646</td>\n",
       "      <td>4.105844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>-143.790417</td>\n",
       "      <td>50.883213</td>\n",
       "      <td>29.373589</td>\n",
       "      <td>42.158512</td>\n",
       "      <td>74.770220</td>\n",
       "      <td>44.116099</td>\n",
       "      <td>-53.567054</td>\n",
       "      <td>-0.391163</td>\n",
       "      <td>34.308352</td>\n",
       "      <td>-42.632978</td>\n",
       "      <td>...</td>\n",
       "      <td>5.705171</td>\n",
       "      <td>-3.847420</td>\n",
       "      <td>-0.377579</td>\n",
       "      <td>1.462214</td>\n",
       "      <td>-3.596006</td>\n",
       "      <td>-1.278195</td>\n",
       "      <td>4.329614</td>\n",
       "      <td>0.781183</td>\n",
       "      <td>-0.092432</td>\n",
       "      <td>3.907760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>-72.298424</td>\n",
       "      <td>38.536187</td>\n",
       "      <td>35.149086</td>\n",
       "      <td>-16.202065</td>\n",
       "      <td>-60.077073</td>\n",
       "      <td>-74.178639</td>\n",
       "      <td>31.938201</td>\n",
       "      <td>14.717646</td>\n",
       "      <td>-20.793295</td>\n",
       "      <td>-8.801487</td>\n",
       "      <td>...</td>\n",
       "      <td>8.769900</td>\n",
       "      <td>-3.034461</td>\n",
       "      <td>-0.132447</td>\n",
       "      <td>1.841507</td>\n",
       "      <td>4.340256</td>\n",
       "      <td>0.242307</td>\n",
       "      <td>8.448501</td>\n",
       "      <td>-0.745156</td>\n",
       "      <td>4.422961</td>\n",
       "      <td>2.537134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>-74.603723</td>\n",
       "      <td>-19.186921</td>\n",
       "      <td>-71.709986</td>\n",
       "      <td>0.140715</td>\n",
       "      <td>-1.011537</td>\n",
       "      <td>-28.882427</td>\n",
       "      <td>3.888550</td>\n",
       "      <td>-52.770542</td>\n",
       "      <td>62.954027</td>\n",
       "      <td>-3.100046</td>\n",
       "      <td>...</td>\n",
       "      <td>11.044080</td>\n",
       "      <td>-3.144825</td>\n",
       "      <td>-0.496771</td>\n",
       "      <td>0.174189</td>\n",
       "      <td>-7.063924</td>\n",
       "      <td>-5.186575</td>\n",
       "      <td>-3.044987</td>\n",
       "      <td>-0.848291</td>\n",
       "      <td>1.694081</td>\n",
       "      <td>-0.865291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>-63.172995</td>\n",
       "      <td>27.856550</td>\n",
       "      <td>0.569857</td>\n",
       "      <td>32.243568</td>\n",
       "      <td>-45.959561</td>\n",
       "      <td>-11.269658</td>\n",
       "      <td>2.697485</td>\n",
       "      <td>20.740886</td>\n",
       "      <td>-14.669913</td>\n",
       "      <td>-63.644360</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.587157</td>\n",
       "      <td>6.942538</td>\n",
       "      <td>-6.494490</td>\n",
       "      <td>-2.457707</td>\n",
       "      <td>2.659121</td>\n",
       "      <td>-7.983095</td>\n",
       "      <td>-3.955801</td>\n",
       "      <td>1.216285</td>\n",
       "      <td>2.916785</td>\n",
       "      <td>-7.838773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>-13.431649</td>\n",
       "      <td>-41.746486</td>\n",
       "      <td>-41.265275</td>\n",
       "      <td>-65.132793</td>\n",
       "      <td>18.460227</td>\n",
       "      <td>-2.346108</td>\n",
       "      <td>-10.320582</td>\n",
       "      <td>8.888304</td>\n",
       "      <td>7.405561</td>\n",
       "      <td>-35.301548</td>\n",
       "      <td>...</td>\n",
       "      <td>4.302478</td>\n",
       "      <td>3.686054</td>\n",
       "      <td>-2.098783</td>\n",
       "      <td>-2.704978</td>\n",
       "      <td>-2.869337</td>\n",
       "      <td>0.308588</td>\n",
       "      <td>6.181956</td>\n",
       "      <td>3.174882</td>\n",
       "      <td>2.279126</td>\n",
       "      <td>4.782605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2032 rows × 116 columns</p>\n",
       "</div>"
      ],
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       "             0          1          2          3          4          5    \\\n",
       "0    -227.624452  23.114436  45.998859  30.429224  10.766937  28.579861   \n",
       "1      79.173780 -50.700727 -36.805734  49.231019  -1.186746 -22.014536   \n",
       "2      66.541151 -22.007590   9.191453 -64.862172 -15.026559  18.673351   \n",
       "3      89.986078  57.462595 -18.866503  42.295773  -5.215643  11.678576   \n",
       "4      -0.779165 -37.875034  55.938902 -32.872510 -63.380924  -8.965753   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "2027 -143.790417  50.883213  29.373589  42.158512  74.770220  44.116099   \n",
       "2028  -72.298424  38.536187  35.149086 -16.202065 -60.077073 -74.178639   \n",
       "2029  -74.603723 -19.186921 -71.709986   0.140715  -1.011537 -28.882427   \n",
       "2030  -63.172995  27.856550   0.569857  32.243568 -45.959561 -11.269658   \n",
       "2031  -13.431649 -41.746486 -41.265275 -65.132793  18.460227  -2.346108   \n",
       "\n",
       "            6          7          8          9    ...        106       107  \\\n",
       "0    -15.364518  67.727508  42.606689   5.488387  ...  14.798696 -2.499685   \n",
       "1     14.515313 -12.539149  21.923205  -6.691319  ...  -8.705012  2.577868   \n",
       "2    -19.001203  39.375727  -2.815390  24.560810  ...   1.877436 -4.299011   \n",
       "3    -19.067189  29.713431  36.130609 -37.808531  ...  -5.715295 -4.704898   \n",
       "4      2.447920  25.599496   2.389311  10.859616  ...   1.603873  8.946189   \n",
       "...         ...        ...        ...        ...  ...        ...       ...   \n",
       "2027 -53.567054  -0.391163  34.308352 -42.632978  ...   5.705171 -3.847420   \n",
       "2028  31.938201  14.717646 -20.793295  -8.801487  ...   8.769900 -3.034461   \n",
       "2029   3.888550 -52.770542  62.954027  -3.100046  ...  11.044080 -3.144825   \n",
       "2030   2.697485  20.740886 -14.669913 -63.644360  ...  -3.587157  6.942538   \n",
       "2031 -10.320582   8.888304   7.405561 -35.301548  ...   4.302478  3.686054   \n",
       "\n",
       "            108        109        110       111       112       113       114  \\\n",
       "0    -10.561960  -7.045339 -11.988090  9.170574  3.013181  2.774824 -1.689797   \n",
       "1     10.209406  -7.727766   2.913083 -7.042295  0.889421 -2.898908  3.268386   \n",
       "2     -7.039255   0.613479  -0.286574 -6.739499  0.542982  2.085152 -1.423430   \n",
       "3      1.990218  13.533275   4.085548 -4.735775  1.650974 -8.248758 -2.003846   \n",
       "4     -1.031476  -7.081917  -3.725180  0.982444 -0.007442  4.195274 -3.589646   \n",
       "...         ...        ...        ...       ...       ...       ...       ...   \n",
       "2027  -0.377579   1.462214  -3.596006 -1.278195  4.329614  0.781183 -0.092432   \n",
       "2028  -0.132447   1.841507   4.340256  0.242307  8.448501 -0.745156  4.422961   \n",
       "2029  -0.496771   0.174189  -7.063924 -5.186575 -3.044987 -0.848291  1.694081   \n",
       "2030  -6.494490  -2.457707   2.659121 -7.983095 -3.955801  1.216285  2.916785   \n",
       "2031  -2.098783  -2.704978  -2.869337  0.308588  6.181956  3.174882  2.279126   \n",
       "\n",
       "           115  \n",
       "0    -8.083196  \n",
       "1    -7.134964  \n",
       "2     5.378897  \n",
       "3    -3.749783  \n",
       "4     4.105844  \n",
       "...        ...  \n",
       "2027  3.907760  \n",
       "2028  2.537134  \n",
       "2029 -0.865291  \n",
       "2030 -7.838773  \n",
       "2031  4.782605  \n",
       "\n",
       "[2032 rows x 116 columns]"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843ad0d3",
   "metadata": {},
   "source": [
    "Получим новый X_test как проекцию данных прошлого X_test на собственные вектора, полученные при МГК для X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "db3dea96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559, 36864)"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Шаг 1 - Стандартизация X_test\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_test)\n",
    "scaled_data = scaler.transform(X_test)\n",
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "bac0add2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.841644</td>\n",
       "      <td>-0.961072</td>\n",
       "      <td>-1.002011</td>\n",
       "      <td>-1.065813</td>\n",
       "      <td>-0.997618</td>\n",
       "      <td>-0.803105</td>\n",
       "      <td>-0.912318</td>\n",
       "      <td>-1.291999</td>\n",
       "      <td>-1.398600</td>\n",
       "      <td>-1.431351</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.150495</td>\n",
       "      <td>-1.740591</td>\n",
       "      <td>-1.423093</td>\n",
       "      <td>-1.304987</td>\n",
       "      <td>-0.966324</td>\n",
       "      <td>-1.144233</td>\n",
       "      <td>-0.790282</td>\n",
       "      <td>-0.069804</td>\n",
       "      <td>0.217963</td>\n",
       "      <td>0.684992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.819150</td>\n",
       "      <td>-0.546253</td>\n",
       "      <td>0.179316</td>\n",
       "      <td>0.654822</td>\n",
       "      <td>0.824154</td>\n",
       "      <td>1.049860</td>\n",
       "      <td>1.153785</td>\n",
       "      <td>0.976068</td>\n",
       "      <td>0.800059</td>\n",
       "      <td>0.694434</td>\n",
       "      <td>...</td>\n",
       "      <td>1.782163</td>\n",
       "      <td>-0.042253</td>\n",
       "      <td>-1.752366</td>\n",
       "      <td>-1.140503</td>\n",
       "      <td>-0.341811</td>\n",
       "      <td>-0.737555</td>\n",
       "      <td>-0.520728</td>\n",
       "      <td>0.044482</td>\n",
       "      <td>0.217963</td>\n",
       "      <td>0.132766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.835240</td>\n",
       "      <td>2.235473</td>\n",
       "      <td>2.301331</td>\n",
       "      <td>2.551431</td>\n",
       "      <td>2.285010</td>\n",
       "      <td>1.756499</td>\n",
       "      <td>1.257834</td>\n",
       "      <td>0.990514</td>\n",
       "      <td>1.029984</td>\n",
       "      <td>1.014758</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098947</td>\n",
       "      <td>1.310127</td>\n",
       "      <td>1.524688</td>\n",
       "      <td>1.521152</td>\n",
       "      <td>1.560777</td>\n",
       "      <td>0.560686</td>\n",
       "      <td>-0.996412</td>\n",
       "      <td>-0.935110</td>\n",
       "      <td>-0.446712</td>\n",
       "      <td>-0.157879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.841644</td>\n",
       "      <td>-0.961072</td>\n",
       "      <td>-1.002011</td>\n",
       "      <td>-1.046260</td>\n",
       "      <td>-1.031991</td>\n",
       "      <td>-0.426231</td>\n",
       "      <td>0.351127</td>\n",
       "      <td>0.614910</td>\n",
       "      <td>0.627615</td>\n",
       "      <td>0.621634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149110</td>\n",
       "      <td>-0.183781</td>\n",
       "      <td>-1.062460</td>\n",
       "      <td>-0.826488</td>\n",
       "      <td>-0.632282</td>\n",
       "      <td>-0.596782</td>\n",
       "      <td>-0.441447</td>\n",
       "      <td>-0.526947</td>\n",
       "      <td>-0.573316</td>\n",
       "      <td>-0.564782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.260602</td>\n",
       "      <td>0.063775</td>\n",
       "      <td>-0.258212</td>\n",
       "      <td>-0.596549</td>\n",
       "      <td>-0.619515</td>\n",
       "      <td>-0.520449</td>\n",
       "      <td>-0.555580</td>\n",
       "      <td>-0.540792</td>\n",
       "      <td>-0.349566</td>\n",
       "      <td>-0.135496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199101</td>\n",
       "      <td>0.964169</td>\n",
       "      <td>-0.466632</td>\n",
       "      <td>-1.678816</td>\n",
       "      <td>-0.588711</td>\n",
       "      <td>-0.143180</td>\n",
       "      <td>0.541633</td>\n",
       "      <td>1.366930</td>\n",
       "      <td>1.389055</td>\n",
       "      <td>1.382540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>-0.346758</td>\n",
       "      <td>-0.375445</td>\n",
       "      <td>-0.476977</td>\n",
       "      <td>-0.537891</td>\n",
       "      <td>-0.602328</td>\n",
       "      <td>-0.489043</td>\n",
       "      <td>-0.362348</td>\n",
       "      <td>-0.367437</td>\n",
       "      <td>-0.277715</td>\n",
       "      <td>-0.179176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.699015</td>\n",
       "      <td>1.058522</td>\n",
       "      <td>0.505509</td>\n",
       "      <td>-0.796581</td>\n",
       "      <td>-1.155130</td>\n",
       "      <td>-1.316289</td>\n",
       "      <td>-1.408672</td>\n",
       "      <td>-1.539191</td>\n",
       "      <td>-1.348770</td>\n",
       "      <td>-1.204201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>-0.684181</td>\n",
       "      <td>-0.717061</td>\n",
       "      <td>-0.783247</td>\n",
       "      <td>-0.948497</td>\n",
       "      <td>-0.997618</td>\n",
       "      <td>-0.614668</td>\n",
       "      <td>-0.109659</td>\n",
       "      <td>0.037059</td>\n",
       "      <td>-0.507640</td>\n",
       "      <td>-1.038226</td>\n",
       "      <td>...</td>\n",
       "      <td>1.365567</td>\n",
       "      <td>-0.592640</td>\n",
       "      <td>-1.768046</td>\n",
       "      <td>-1.349847</td>\n",
       "      <td>-0.777518</td>\n",
       "      <td>-0.925253</td>\n",
       "      <td>-0.885419</td>\n",
       "      <td>-0.886130</td>\n",
       "      <td>-0.905653</td>\n",
       "      <td>-0.608379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>-0.706676</td>\n",
       "      <td>-0.155835</td>\n",
       "      <td>0.682474</td>\n",
       "      <td>1.495587</td>\n",
       "      <td>1.700668</td>\n",
       "      <td>1.520953</td>\n",
       "      <td>1.421338</td>\n",
       "      <td>1.221655</td>\n",
       "      <td>1.073095</td>\n",
       "      <td>0.869156</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334141</td>\n",
       "      <td>-0.246683</td>\n",
       "      <td>0.019438</td>\n",
       "      <td>0.833309</td>\n",
       "      <td>1.386494</td>\n",
       "      <td>2.093550</td>\n",
       "      <td>0.018381</td>\n",
       "      <td>-1.457558</td>\n",
       "      <td>-1.048084</td>\n",
       "      <td>-0.971685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>0.395571</td>\n",
       "      <td>-0.277841</td>\n",
       "      <td>-0.717618</td>\n",
       "      <td>-0.889839</td>\n",
       "      <td>-0.636701</td>\n",
       "      <td>-0.693184</td>\n",
       "      <td>-0.510988</td>\n",
       "      <td>-0.295206</td>\n",
       "      <td>-0.205863</td>\n",
       "      <td>-0.091815</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.867382</td>\n",
       "      <td>-0.828521</td>\n",
       "      <td>-0.968382</td>\n",
       "      <td>-1.334894</td>\n",
       "      <td>-0.864659</td>\n",
       "      <td>-0.205745</td>\n",
       "      <td>-0.060900</td>\n",
       "      <td>0.354686</td>\n",
       "      <td>1.246625</td>\n",
       "      <td>0.757653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>-0.841644</td>\n",
       "      <td>-0.961072</td>\n",
       "      <td>-0.827000</td>\n",
       "      <td>-0.479233</td>\n",
       "      <td>-0.121105</td>\n",
       "      <td>0.186190</td>\n",
       "      <td>0.470039</td>\n",
       "      <td>0.629357</td>\n",
       "      <td>0.656356</td>\n",
       "      <td>0.592513</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.317477</td>\n",
       "      <td>-0.372485</td>\n",
       "      <td>-0.513671</td>\n",
       "      <td>0.160418</td>\n",
       "      <td>0.820075</td>\n",
       "      <td>1.233269</td>\n",
       "      <td>2.063822</td>\n",
       "      <td>0.958767</td>\n",
       "      <td>-1.586153</td>\n",
       "      <td>-1.334991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 36864 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6      \\\n",
       "0   -0.841644 -0.961072 -1.002011 -1.065813 -0.997618 -0.803105 -0.912318   \n",
       "1   -0.819150 -0.546253  0.179316  0.654822  0.824154  1.049860  1.153785   \n",
       "2    1.835240  2.235473  2.301331  2.551431  2.285010  1.756499  1.257834   \n",
       "3   -0.841644 -0.961072 -1.002011 -1.046260 -1.031991 -0.426231  0.351127   \n",
       "4    0.260602  0.063775 -0.258212 -0.596549 -0.619515 -0.520449 -0.555580   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "554 -0.346758 -0.375445 -0.476977 -0.537891 -0.602328 -0.489043 -0.362348   \n",
       "555 -0.684181 -0.717061 -0.783247 -0.948497 -0.997618 -0.614668 -0.109659   \n",
       "556 -0.706676 -0.155835  0.682474  1.495587  1.700668  1.520953  1.421338   \n",
       "557  0.395571 -0.277841 -0.717618 -0.889839 -0.636701 -0.693184 -0.510988   \n",
       "558 -0.841644 -0.961072 -0.827000 -0.479233 -0.121105  0.186190  0.470039   \n",
       "\n",
       "        7         8         9      ...     36854     36855     36856  \\\n",
       "0   -1.291999 -1.398600 -1.431351  ... -2.150495 -1.740591 -1.423093   \n",
       "1    0.976068  0.800059  0.694434  ...  1.782163 -0.042253 -1.752366   \n",
       "2    0.990514  1.029984  1.014758  ...  1.098947  1.310127  1.524688   \n",
       "3    0.614910  0.627615  0.621634  ...  0.149110 -0.183781 -1.062460   \n",
       "4   -0.540792 -0.349566 -0.135496  ...  0.199101  0.964169 -0.466632   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "554 -0.367437 -0.277715 -0.179176  ...  0.699015  1.058522  0.505509   \n",
       "555  0.037059 -0.507640 -1.038226  ...  1.365567 -0.592640 -1.768046   \n",
       "556  1.221655  1.073095  0.869156  ... -0.334141 -0.246683  0.019438   \n",
       "557 -0.295206 -0.205863 -0.091815  ... -0.867382 -0.828521 -0.968382   \n",
       "558  0.629357  0.656356  0.592513  ... -0.317477 -0.372485 -0.513671   \n",
       "\n",
       "        36857     36858     36859     36860     36861     36862     36863  \n",
       "0   -1.304987 -0.966324 -1.144233 -0.790282 -0.069804  0.217963  0.684992  \n",
       "1   -1.140503 -0.341811 -0.737555 -0.520728  0.044482  0.217963  0.132766  \n",
       "2    1.521152  1.560777  0.560686 -0.996412 -0.935110 -0.446712 -0.157879  \n",
       "3   -0.826488 -0.632282 -0.596782 -0.441447 -0.526947 -0.573316 -0.564782  \n",
       "4   -1.678816 -0.588711 -0.143180  0.541633  1.366930  1.389055  1.382540  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "554 -0.796581 -1.155130 -1.316289 -1.408672 -1.539191 -1.348770 -1.204201  \n",
       "555 -1.349847 -0.777518 -0.925253 -0.885419 -0.886130 -0.905653 -0.608379  \n",
       "556  0.833309  1.386494  2.093550  0.018381 -1.457558 -1.048084 -0.971685  \n",
       "557 -1.334894 -0.864659 -0.205745 -0.060900  0.354686  1.246625  0.757653  \n",
       "558  0.160418  0.820075  1.233269  2.063822  0.958767 -1.586153 -1.334991  \n",
       "\n",
       "[559 rows x 36864 columns]"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Стандартизованные данные\n",
    "pd.DataFrame(data=scaled_data,\n",
    "          index=pd.RangeIndex(range(0, scaled_data.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, 36864)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "fe4ac12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559, 116)"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca = pca.transform(scaled_data)\n",
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "a4386803",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>5</th>\n",
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       "      <th>107</th>\n",
       "      <th>108</th>\n",
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       "      <th>115</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.666541</td>\n",
       "      <td>-58.040316</td>\n",
       "      <td>-32.480864</td>\n",
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       "      <td>-45.151191</td>\n",
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       "      <td>29.678363</td>\n",
       "      <td>22.649781</td>\n",
       "      <td>2.096266</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.046349</td>\n",
       "      <td>-4.624668</td>\n",
       "      <td>11.914252</td>\n",
       "      <td>-2.249966</td>\n",
       "      <td>8.685953</td>\n",
       "      <td>-8.363361</td>\n",
       "      <td>13.050123</td>\n",
       "      <td>-2.035401</td>\n",
       "      <td>-0.701827</td>\n",
       "      <td>-3.553227</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.647847</td>\n",
       "      <td>11.465784</td>\n",
       "      <td>-42.549133</td>\n",
       "      <td>95.721961</td>\n",
       "      <td>1.530222</td>\n",
       "      <td>-24.388021</td>\n",
       "      <td>-28.418619</td>\n",
       "      <td>17.118630</td>\n",
       "      <td>32.247297</td>\n",
       "      <td>-21.496937</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.917629</td>\n",
       "      <td>-0.985473</td>\n",
       "      <td>5.551020</td>\n",
       "      <td>7.425230</td>\n",
       "      <td>3.812444</td>\n",
       "      <td>4.160214</td>\n",
       "      <td>-11.065195</td>\n",
       "      <td>4.066103</td>\n",
       "      <td>-7.946923</td>\n",
       "      <td>-2.724392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-51.670016</td>\n",
       "      <td>-38.364888</td>\n",
       "      <td>-80.636142</td>\n",
       "      <td>-16.828268</td>\n",
       "      <td>42.963965</td>\n",
       "      <td>-31.659939</td>\n",
       "      <td>-31.839509</td>\n",
       "      <td>-23.427929</td>\n",
       "      <td>45.069530</td>\n",
       "      <td>5.740882</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.601041</td>\n",
       "      <td>0.391056</td>\n",
       "      <td>5.753030</td>\n",
       "      <td>3.774305</td>\n",
       "      <td>6.812536</td>\n",
       "      <td>-7.123961</td>\n",
       "      <td>-3.590038</td>\n",
       "      <td>3.748233</td>\n",
       "      <td>-0.208354</td>\n",
       "      <td>-3.424251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.368740</td>\n",
       "      <td>22.812428</td>\n",
       "      <td>-55.704439</td>\n",
       "      <td>5.983971</td>\n",
       "      <td>-26.018790</td>\n",
       "      <td>23.863386</td>\n",
       "      <td>-5.944421</td>\n",
       "      <td>31.749691</td>\n",
       "      <td>28.318036</td>\n",
       "      <td>-12.114937</td>\n",
       "      <td>...</td>\n",
       "      <td>5.505619</td>\n",
       "      <td>-2.669773</td>\n",
       "      <td>-9.234622</td>\n",
       "      <td>-3.494941</td>\n",
       "      <td>2.583233</td>\n",
       "      <td>-5.200884</td>\n",
       "      <td>0.350458</td>\n",
       "      <td>1.371580</td>\n",
       "      <td>-11.274806</td>\n",
       "      <td>5.990974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86.633581</td>\n",
       "      <td>75.711730</td>\n",
       "      <td>-28.326937</td>\n",
       "      <td>-25.348964</td>\n",
       "      <td>65.733410</td>\n",
       "      <td>11.804103</td>\n",
       "      <td>26.809973</td>\n",
       "      <td>37.170359</td>\n",
       "      <td>-43.357841</td>\n",
       "      <td>-30.784343</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948896</td>\n",
       "      <td>-2.328353</td>\n",
       "      <td>-6.304309</td>\n",
       "      <td>5.075185</td>\n",
       "      <td>-3.449991</td>\n",
       "      <td>-2.478765</td>\n",
       "      <td>-1.805214</td>\n",
       "      <td>-0.988959</td>\n",
       "      <td>0.464502</td>\n",
       "      <td>-1.149478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>142.421477</td>\n",
       "      <td>-62.423106</td>\n",
       "      <td>-41.640600</td>\n",
       "      <td>-26.702455</td>\n",
       "      <td>-17.814665</td>\n",
       "      <td>38.906038</td>\n",
       "      <td>-41.529441</td>\n",
       "      <td>-22.118340</td>\n",
       "      <td>-24.131963</td>\n",
       "      <td>38.529375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992306</td>\n",
       "      <td>-9.152005</td>\n",
       "      <td>9.498956</td>\n",
       "      <td>-7.086603</td>\n",
       "      <td>-4.681059</td>\n",
       "      <td>-3.403392</td>\n",
       "      <td>10.291477</td>\n",
       "      <td>-2.673468</td>\n",
       "      <td>-5.469002</td>\n",
       "      <td>-15.438386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>69.354526</td>\n",
       "      <td>20.880512</td>\n",
       "      <td>-28.005964</td>\n",
       "      <td>-9.321754</td>\n",
       "      <td>-16.877462</td>\n",
       "      <td>60.650531</td>\n",
       "      <td>11.675073</td>\n",
       "      <td>26.392656</td>\n",
       "      <td>1.929899</td>\n",
       "      <td>-28.075136</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.770188</td>\n",
       "      <td>-9.811784</td>\n",
       "      <td>8.251683</td>\n",
       "      <td>1.843368</td>\n",
       "      <td>-3.267764</td>\n",
       "      <td>-0.885733</td>\n",
       "      <td>2.330528</td>\n",
       "      <td>-1.514074</td>\n",
       "      <td>-2.168376</td>\n",
       "      <td>1.908290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>-28.675672</td>\n",
       "      <td>38.815311</td>\n",
       "      <td>12.266821</td>\n",
       "      <td>-28.127602</td>\n",
       "      <td>-41.690391</td>\n",
       "      <td>-36.029557</td>\n",
       "      <td>-3.365189</td>\n",
       "      <td>-21.055385</td>\n",
       "      <td>-5.172927</td>\n",
       "      <td>-9.023398</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.994459</td>\n",
       "      <td>1.240386</td>\n",
       "      <td>-2.688699</td>\n",
       "      <td>-0.391098</td>\n",
       "      <td>-1.534427</td>\n",
       "      <td>-2.191711</td>\n",
       "      <td>3.735122</td>\n",
       "      <td>3.284968</td>\n",
       "      <td>-1.448197</td>\n",
       "      <td>-4.701598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>67.271133</td>\n",
       "      <td>-21.117625</td>\n",
       "      <td>73.969430</td>\n",
       "      <td>11.079330</td>\n",
       "      <td>4.647108</td>\n",
       "      <td>-34.209092</td>\n",
       "      <td>-2.534805</td>\n",
       "      <td>18.665645</td>\n",
       "      <td>5.041265</td>\n",
       "      <td>0.717906</td>\n",
       "      <td>...</td>\n",
       "      <td>2.559862</td>\n",
       "      <td>-3.666465</td>\n",
       "      <td>-0.731124</td>\n",
       "      <td>-9.175337</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>1.283706</td>\n",
       "      <td>0.516120</td>\n",
       "      <td>1.429801</td>\n",
       "      <td>-0.021286</td>\n",
       "      <td>9.952168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>3.697254</td>\n",
       "      <td>24.923749</td>\n",
       "      <td>65.737696</td>\n",
       "      <td>46.386831</td>\n",
       "      <td>-19.820755</td>\n",
       "      <td>-27.869271</td>\n",
       "      <td>-40.838977</td>\n",
       "      <td>6.987934</td>\n",
       "      <td>3.532814</td>\n",
       "      <td>14.321606</td>\n",
       "      <td>...</td>\n",
       "      <td>9.431059</td>\n",
       "      <td>7.003049</td>\n",
       "      <td>-0.920410</td>\n",
       "      <td>-5.269689</td>\n",
       "      <td>2.215073</td>\n",
       "      <td>5.780281</td>\n",
       "      <td>8.615612</td>\n",
       "      <td>6.132362</td>\n",
       "      <td>9.462100</td>\n",
       "      <td>1.045306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>559 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5    \\\n",
       "0     86.666541 -58.040316 -32.480864  60.783152   7.084201 -45.151191   \n",
       "1     -6.647847  11.465784 -42.549133  95.721961   1.530222 -24.388021   \n",
       "2    -51.670016 -38.364888 -80.636142 -16.828268  42.963965 -31.659939   \n",
       "3     61.368740  22.812428 -55.704439   5.983971 -26.018790  23.863386   \n",
       "4     86.633581  75.711730 -28.326937 -25.348964  65.733410  11.804103   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "554  142.421477 -62.423106 -41.640600 -26.702455 -17.814665  38.906038   \n",
       "555   69.354526  20.880512 -28.005964  -9.321754 -16.877462  60.650531   \n",
       "556  -28.675672  38.815311  12.266821 -28.127602 -41.690391 -36.029557   \n",
       "557   67.271133 -21.117625  73.969430  11.079330   4.647108 -34.209092   \n",
       "558    3.697254  24.923749  65.737696  46.386831 -19.820755 -27.869271   \n",
       "\n",
       "           6          7          8          9    ...        106       107  \\\n",
       "0    -0.886952  29.678363  22.649781   2.096266  ...  -1.046349 -4.624668   \n",
       "1   -28.418619  17.118630  32.247297 -21.496937  ...  -6.917629 -0.985473   \n",
       "2   -31.839509 -23.427929  45.069530   5.740882  ... -13.601041  0.391056   \n",
       "3    -5.944421  31.749691  28.318036 -12.114937  ...   5.505619 -2.669773   \n",
       "4    26.809973  37.170359 -43.357841 -30.784343  ...   0.948896 -2.328353   \n",
       "..         ...        ...        ...        ...  ...        ...       ...   \n",
       "554 -41.529441 -22.118340 -24.131963  38.529375  ...   0.992306 -9.152005   \n",
       "555  11.675073  26.392656   1.929899 -28.075136  ... -12.770188 -9.811784   \n",
       "556  -3.365189 -21.055385  -5.172927  -9.023398  ...  -1.994459  1.240386   \n",
       "557  -2.534805  18.665645   5.041265   0.717906  ...   2.559862 -3.666465   \n",
       "558 -40.838977   6.987934   3.532814  14.321606  ...   9.431059  7.003049   \n",
       "\n",
       "           108       109       110       111        112       113        114  \\\n",
       "0    11.914252 -2.249966  8.685953 -8.363361  13.050123 -2.035401  -0.701827   \n",
       "1     5.551020  7.425230  3.812444  4.160214 -11.065195  4.066103  -7.946923   \n",
       "2     5.753030  3.774305  6.812536 -7.123961  -3.590038  3.748233  -0.208354   \n",
       "3    -9.234622 -3.494941  2.583233 -5.200884   0.350458  1.371580 -11.274806   \n",
       "4    -6.304309  5.075185 -3.449991 -2.478765  -1.805214 -0.988959   0.464502   \n",
       "..         ...       ...       ...       ...        ...       ...        ...   \n",
       "554   9.498956 -7.086603 -4.681059 -3.403392  10.291477 -2.673468  -5.469002   \n",
       "555   8.251683  1.843368 -3.267764 -0.885733   2.330528 -1.514074  -2.168376   \n",
       "556  -2.688699 -0.391098 -1.534427 -2.191711   3.735122  3.284968  -1.448197   \n",
       "557  -0.731124 -9.175337  0.311173  1.283706   0.516120  1.429801  -0.021286   \n",
       "558  -0.920410 -5.269689  2.215073  5.780281   8.615612  6.132362   9.462100   \n",
       "\n",
       "           115  \n",
       "0    -3.553227  \n",
       "1    -2.724392  \n",
       "2    -3.424251  \n",
       "3     5.990974  \n",
       "4    -1.149478  \n",
       "..         ...  \n",
       "554 -15.438386  \n",
       "555   1.908290  \n",
       "556  -4.701598  \n",
       "557   9.952168  \n",
       "558   1.045306  \n",
       "\n",
       "[559 rows x 116 columns]"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Итоговые данные \n",
    "data_pca = pd.DataFrame(data=x_pca,\n",
    "          index=pd.RangeIndex(range(0, scaled_data.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, col+1)))\n",
    "X_test = data_pca\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "88ff8b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 116)"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "4862d7b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559, 116)"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be0f884",
   "metadata": {},
   "source": [
    "# ML (Машинное обучение после снижения размерности данных c помощью МГК)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad3c91e",
   "metadata": {},
   "source": [
    "Загрузим необходимые библиотеки для машинного обучения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "ab6572f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import random\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB  \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "random.seed(17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "095efbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Импортируем модули, необходимые для визуализации дерева решений.\n",
    "from IPython.display import SVG\n",
    "from graphviz import Source\n",
    "from IPython.display import display\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7161be4e",
   "metadata": {},
   "source": [
    "Для оценки качества предсказания будем использовать метрику accuracy так как нет предпочтения какой-то конкретной эмоции в процессе классификации"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae37d15a",
   "metadata": {},
   "source": [
    "## Классификатор Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "e8422518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_depth</th>\n",
       "      <th>Train_score</th>\n",
       "      <th>Test_score</th>\n",
       "      <th>Validation_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.308071</td>\n",
       "      <td>0.252236</td>\n",
       "      <td>0.291816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.362697</td>\n",
       "      <td>0.277281</td>\n",
       "      <td>0.353834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.425197</td>\n",
       "      <td>0.329159</td>\n",
       "      <td>0.429139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.505906</td>\n",
       "      <td>0.275492</td>\n",
       "      <td>0.504921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.622539</td>\n",
       "      <td>0.271914</td>\n",
       "      <td>0.616639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.771654</td>\n",
       "      <td>0.221825</td>\n",
       "      <td>0.739184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>0.878445</td>\n",
       "      <td>0.211091</td>\n",
       "      <td>0.839075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.959646</td>\n",
       "      <td>0.277281</td>\n",
       "      <td>0.900096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0.993602</td>\n",
       "      <td>0.254025</td>\n",
       "      <td>0.940939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.999016</td>\n",
       "      <td>0.273703</td>\n",
       "      <td>0.946353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  Train_score  Test_score  Validation_score\n",
       "0          1     0.308071    0.252236          0.291816\n",
       "0          2     0.362697    0.277281          0.353834\n",
       "0          3     0.425197    0.329159          0.429139\n",
       "0          4     0.505906    0.275492          0.504921\n",
       "0          5     0.622539    0.271914          0.616639\n",
       "0          6     0.771654    0.221825          0.739184\n",
       "0          7     0.878445    0.211091          0.839075\n",
       "0          8     0.959646    0.277281          0.900096\n",
       "0          9     0.993602    0.254025          0.940939\n",
       "0         10     0.999016    0.273703          0.946353"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Зададим диапазон исследуемых значений.\n",
    "max_depth_values = range(1, 51)\n",
    "scores_iris_data = pd.DataFrame()\n",
    "rs = np.random.seed(17)\n",
    "scores_data=pd.DataFrame()\n",
    "\n",
    "for max_depth in max_depth_values:\n",
    "    # Изменяем глубину обучения дерева по циклу от 1 до 50 с шагом 1.\n",
    "    clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=max_depth, random_state=rs)\n",
    "    # Обучаем дерево решений (с ограниченной глубиной) на подмножестве train.\n",
    "    clf.fit(X_train, y_train)\n",
    "    # Записываем в отдельную переменную число правильных ответов на обученной модели дерева\n",
    "    train_score = clf.score(X_train, y_train)\n",
    "    No_validation_score = clf.score(X_test, y_test)\n",
    "    # Записываем в отдельную переменную число правильных ответов на обученной модели дерева\n",
    "    test_val_score = cross_val_score(clf, X_train, y_train, cv=5).mean()\n",
    "    # Создаем временный DataFrame.\n",
    "    temp_score_data = pd.DataFrame({'max_depth':[max_depth],\n",
    "                                    'Train_score':[train_score],\n",
    "                                    'Test_score':[No_validation_score],  \n",
    "                                    'Validation_score':[test_val_score]})\n",
    "    # Наращиваем DataFrame \"scores_iris_data\".\n",
    "    scores_data = scores_data.append(temp_score_data)\n",
    "scores_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d26b0c5",
   "metadata": {},
   "source": [
    "Обратим внимание, что кросс-валидация будет менее информативна так как данные будут валидироваться из трейновой выборки, которая и является той, на которой мы МГК обучали. Тестовая не участвовала в обучении МГК (PCA) так что там не будет никаких подгонов при расчете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "019e3161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 6))\n",
    "scores_data_long = pd.melt(scores_data, id_vars=['max_depth'],\n",
    "                           value_vars=['Train_score','Test_score','Validation_score'],\n",
    "                           var_name='set_type', value_name='score')\n",
    "sns.lineplot(x='max_depth', y='score', hue='set_type', data=scores_data_long);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0444450f",
   "metadata": {},
   "source": [
    "Такой большой разброс accuracy по тестовой и валидационной выборки вызван тем, что мы делали upsampling и при кросс-валидации используется train выборка. Тем самым часто при CV возникает пересечение и повторение данных, что приводит к псевдо-хорошим результатам. На тесте как раз такого не происходит, ведь выборки никак не пересекаются. \n",
    "Далее мы будем проводить GridSearchCV для поиска наилучших параметров так что стоит учитывать, этот выше описанный факт чтобы итоговая модельимела более высокую предсказательную способность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "e9d782f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'min_samples_leaf': 2, 'min_samples_split': 3}"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Подбор лучших параметров:\n",
    "par = {'max_depth':range(3, 6), 'min_samples_split':range(2, 6),\n",
    "     'min_samples_leaf':range(2, 6)}\n",
    "search = GridSearchCV(clf,par,cv=5)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "cc7863ca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"2381\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">11 &lt;= -6.295</text>\n",
       "<text text-anchor=\"middle\" x=\"2381\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.531</text>\n",
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       "<text text-anchor=\"middle\" x=\"594\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">8 &lt;= 45.45</text>\n",
       "<text text-anchor=\"middle\" x=\"594\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.292</text>\n",
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       "<text text-anchor=\"middle\" x=\"1178\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">1 &lt;= 47.448</text>\n",
       "<text text-anchor=\"middle\" x=\"1178\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.168</text>\n",
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       "<text text-anchor=\"middle\" x=\"305\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.143</text>\n",
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       "<text text-anchor=\"middle\" x=\"594\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">106 &lt;= -9.525</text>\n",
       "<text text-anchor=\"middle\" x=\"594\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.756</text>\n",
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       "<text text-anchor=\"middle\" x=\"98\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 173</text>\n",
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       "<text text-anchor=\"middle\" x=\"305\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 112</text>\n",
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       "<text text-anchor=\"middle\" x=\"679\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 45</text>\n",
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       "<text text-anchor=\"middle\" x=\"2604\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.171</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 448</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [23, 25, 105, 15, 90, 185, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 5- happiness</text>\n",
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       "<!-- 17&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\"><title>17-&gt;25</title>\n",
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       "<text text-anchor=\"middle\" x=\"1860\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">43 &lt;= -22.569</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.039</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 199</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [59, 25, 0, 80, 15, 20, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 3- disgust</text>\n",
       "</g>\n",
       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\"><title>18-&gt;19</title>\n",
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       "<text text-anchor=\"middle\" x=\"2161\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">19 &lt;= 4.663</text>\n",
       "<text text-anchor=\"middle\" x=\"2161\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.973</text>\n",
       "<text text-anchor=\"middle\" x=\"2161\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 172</text>\n",
       "<text text-anchor=\"middle\" x=\"2161\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [32, 5, 30, 5, 90, 5, 5]</text>\n",
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       "<!-- 18&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\"><title>18-&gt;22</title>\n",
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       "<text text-anchor=\"middle\" x=\"1659\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.169</text>\n",
       "<text text-anchor=\"middle\" x=\"1659\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 26</text>\n",
       "<text text-anchor=\"middle\" x=\"1659\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0, 0, 0, 15, 10, 0]</text>\n",
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       "</g>\n",
       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\"><title>19-&gt;20</title>\n",
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       "<text text-anchor=\"middle\" x=\"1860\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.684</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 173</text>\n",
       "<text text-anchor=\"middle\" x=\"1860\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [58, 25, 0, 80, 0, 10, 0]</text>\n",
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       "<!-- 19&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\"><title>19-&gt;21</title>\n",
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       "<text text-anchor=\"middle\" x=\"2061\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.867</text>\n",
       "<text text-anchor=\"middle\" x=\"2061\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 112</text>\n",
       "<text text-anchor=\"middle\" x=\"2061\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [17, 0, 0, 5, 90, 0, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"2061\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 4- fear</text>\n",
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       "<!-- 22&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\"><title>22-&gt;23</title>\n",
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       "<g id=\"node25\" class=\"node\"><title>24</title>\n",
       "<polygon fill=\"#bdf6c4\" stroke=\"black\" points=\"2343,-68 2167,-68 2167,-0 2343,-0 2343,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"2255\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.896</text>\n",
       "<text text-anchor=\"middle\" x=\"2255\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 60</text>\n",
       "<text text-anchor=\"middle\" x=\"2255\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [15, 5, 30, 0, 0, 5, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"2255\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 2- contempt</text>\n",
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       "<!-- 22&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\"><title>22-&gt;24</title>\n",
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       "<text text-anchor=\"middle\" x=\"2604\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">62 &lt;= 11.936</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.974</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 103</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [8, 0, 0, 15, 0, 80, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"2604\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 5- happiness</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\"><title>25-&gt;26</title>\n",
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       "<!-- 29 -->\n",
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       "<text text-anchor=\"middle\" x=\"2827\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">47 &lt;= -3.096</text>\n",
       "<text text-anchor=\"middle\" x=\"2827\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.11</text>\n",
       "<text text-anchor=\"middle\" x=\"2827\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 345</text>\n",
       "<text text-anchor=\"middle\" x=\"2827\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [15, 25, 105, 0, 90, 105, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"2827\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 2- contempt</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\"><title>25-&gt;29</title>\n",
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       "<!-- 27 -->\n",
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       "<polygon fill=\"#b84ae7\" stroke=\"black\" points=\"2530.5,-68 2361.5,-68 2361.5,-0 2530.5,-0 2530.5,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"2446\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.404</text>\n",
       "<text text-anchor=\"middle\" x=\"2446\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 87</text>\n",
       "<text text-anchor=\"middle\" x=\"2446\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 0, 0, 0, 0, 80, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"2446\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 5- happiness</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\"><title>26-&gt;27</title>\n",
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       "<text text-anchor=\"middle\" x=\"2633\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"middle\" x=\"2633\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0, 0, 15, 0, 0, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"2633\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 3- disgust</text>\n",
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       "<!-- 26&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\"><title>26-&gt;28</title>\n",
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       "<text text-anchor=\"middle\" x=\"2827\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.512</text>\n",
       "<text text-anchor=\"middle\" x=\"2827\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 167</text>\n",
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       "<!-- 29&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\"><title>29-&gt;30</title>\n",
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       "<text text-anchor=\"middle\" x=\"3031\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.872</text>\n",
       "<text text-anchor=\"middle\" x=\"3031\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 178</text>\n",
       "<text text-anchor=\"middle\" x=\"3031\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [13, 25, 60, 0, 0, 75, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"3031\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 5- happiness</text>\n",
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       "<!-- 29&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\"><title>29-&gt;31</title>\n",
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       "<text text-anchor=\"middle\" x=\"2856\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">56 &lt;= -1.978</text>\n",
       "<text text-anchor=\"middle\" x=\"2856\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.12</text>\n",
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       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\"><title>32-&gt;33</title>\n",
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       "<text text-anchor=\"middle\" x=\"3519\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.952</text>\n",
       "<text text-anchor=\"middle\" x=\"3519\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 429</text>\n",
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       "<text text-anchor=\"middle\" x=\"2853\" y=\"-268.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 45</text>\n",
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       "<!-- 33&#45;&gt;34 -->\n",
       "<g id=\"edge34\" class=\"edge\"><title>33-&gt;34</title>\n",
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       "<text text-anchor=\"middle\" x=\"3040\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">5 &lt;= 38.512</text>\n",
       "<text text-anchor=\"middle\" x=\"3040\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.337</text>\n",
       "<text text-anchor=\"middle\" x=\"3040\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"middle\" x=\"3040\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 0, 0, 0, 0, 0, 30]</text>\n",
       "<text text-anchor=\"middle\" x=\"3040\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 6- surprise</text>\n",
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       "<!-- 33&#45;&gt;35 -->\n",
       "<g id=\"edge35\" class=\"edge\"><title>33-&gt;35</title>\n",
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       "<text text-anchor=\"middle\" x=\"3034\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"3034\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"3034\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 0, 0, 0, 0, 0, 0]</text>\n",
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       "<!-- 35&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\"><title>35-&gt;36</title>\n",
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       "<!-- 37 -->\n",
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       "<text text-anchor=\"middle\" x=\"3218\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"3218\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 30</text>\n",
       "<text text-anchor=\"middle\" x=\"3218\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 30]</text>\n",
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       "<!-- 35&#45;&gt;37 -->\n",
       "<g id=\"edge37\" class=\"edge\"><title>35-&gt;37</title>\n",
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       "<!-- 39 -->\n",
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       "<text text-anchor=\"middle\" x=\"3519\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">32 &lt;= -0.196</text>\n",
       "<text text-anchor=\"middle\" x=\"3519\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 2.331</text>\n",
       "<text text-anchor=\"middle\" x=\"3519\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 192</text>\n",
       "<text text-anchor=\"middle\" x=\"3519\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [62, 30, 30, 5, 0, 20, 45]</text>\n",
       "<text text-anchor=\"middle\" x=\"3519\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0- neutral</text>\n",
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       "<!-- 38&#45;&gt;39 -->\n",
       "<g id=\"edge39\" class=\"edge\"><title>38-&gt;39</title>\n",
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       "<text text-anchor=\"middle\" x=\"4101\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">7 &lt;= 20.881</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.134</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 237</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [52, 0, 0, 10, 0, 5, 170]</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 6- surprise</text>\n",
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       "<!-- 38&#45;&gt;46 -->\n",
       "<g id=\"edge46\" class=\"edge\"><title>38-&gt;46</title>\n",
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       "<text text-anchor=\"middle\" x=\"3417\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">10 &lt;= -7.379</text>\n",
       "<text text-anchor=\"middle\" x=\"3417\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.776</text>\n",
       "<text text-anchor=\"middle\" x=\"3417\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 77</text>\n",
       "<text text-anchor=\"middle\" x=\"3417\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [27, 15, 30, 5, 0, 0, 0]</text>\n",
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       "<!-- 39&#45;&gt;40 -->\n",
       "<g id=\"edge40\" class=\"edge\"><title>39-&gt;40</title>\n",
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       "<!-- 43 -->\n",
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       "<text text-anchor=\"middle\" x=\"3621\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">35 &lt;= 13.696</text>\n",
       "<text text-anchor=\"middle\" x=\"3621\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.874</text>\n",
       "<text text-anchor=\"middle\" x=\"3621\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 115</text>\n",
       "<text text-anchor=\"middle\" x=\"3621\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [35, 15, 0, 0, 0, 20, 45]</text>\n",
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       "<!-- 39&#45;&gt;43 -->\n",
       "<g id=\"edge43\" class=\"edge\"><title>39-&gt;43</title>\n",
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       "<text text-anchor=\"middle\" x=\"3228\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.758</text>\n",
       "<text text-anchor=\"middle\" x=\"3228\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 36</text>\n",
       "<text text-anchor=\"middle\" x=\"3228\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0, 30, 5, 0, 0, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"3228\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 2- contempt</text>\n",
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       "<!-- 40&#45;&gt;41 -->\n",
       "<g id=\"edge41\" class=\"edge\"><title>40-&gt;41</title>\n",
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       "<text text-anchor=\"middle\" x=\"3419\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.947</text>\n",
       "<text text-anchor=\"middle\" x=\"3419\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 41</text>\n",
       "<text text-anchor=\"middle\" x=\"3419\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [26, 15, 0, 0, 0, 0, 0]</text>\n",
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       "<!-- 40&#45;&gt;42 -->\n",
       "<g id=\"edge42\" class=\"edge\"><title>40-&gt;42</title>\n",
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       "<!-- 44 -->\n",
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       "<text text-anchor=\"middle\" x=\"3620\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.897</text>\n",
       "<text text-anchor=\"middle\" x=\"3620\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 74</text>\n",
       "<text text-anchor=\"middle\" x=\"3620\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [29, 15, 0, 0, 0, 20, 10]</text>\n",
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       "<!-- 43&#45;&gt;44 -->\n",
       "<g id=\"edge44\" class=\"edge\"><title>43-&gt;44</title>\n",
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       "<text text-anchor=\"middle\" x=\"3817\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.601</text>\n",
       "<text text-anchor=\"middle\" x=\"3817\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 41</text>\n",
       "<text text-anchor=\"middle\" x=\"3817\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [6, 0, 0, 0, 0, 0, 35]</text>\n",
       "<text text-anchor=\"middle\" x=\"3817\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 6- surprise</text>\n",
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       "<!-- 43&#45;&gt;45 -->\n",
       "<g id=\"edge45\" class=\"edge\"><title>43-&gt;45</title>\n",
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       "<text text-anchor=\"middle\" x=\"4101\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">16 &lt;= 29.868</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.822</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 212</text>\n",
       "<text text-anchor=\"middle\" x=\"4101\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [37, 0, 0, 5, 0, 0, 170]</text>\n",
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       "<!-- 46&#45;&gt;47 -->\n",
       "<g id=\"edge47\" class=\"edge\"><title>46-&gt;47</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M4101,-222.907C4101,-214.649 4101,-205.864 4101,-197.302\"/>\n",
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       "<text text-anchor=\"middle\" x=\"4385\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">105 &lt;= 7.744</text>\n",
       "<text text-anchor=\"middle\" x=\"4385\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.371</text>\n",
       "<text text-anchor=\"middle\" x=\"4385\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 25</text>\n",
       "<text text-anchor=\"middle\" x=\"4385\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [15, 0, 0, 5, 0, 5, 0]</text>\n",
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       "<!-- 46&#45;&gt;50 -->\n",
       "<g id=\"edge50\" class=\"edge\"><title>46-&gt;50</title>\n",
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       "<!-- 47&#45;&gt;48 -->\n",
       "<g id=\"edge48\" class=\"edge\"><title>47-&gt;48</title>\n",
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       "<text text-anchor=\"middle\" x=\"4385\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15</text>\n",
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       "<polygon fill=\"#ffffff\" stroke=\"black\" points=\"4650,-68 4488,-68 4488,-0 4650,-0 4650,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"4569\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.0</text>\n",
       "<text text-anchor=\"middle\" x=\"4569\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\n",
       "<text text-anchor=\"middle\" x=\"4569\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 0, 0, 5, 0, 5, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"4569\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 3- disgust</text>\n",
       "</g>\n",
       "<!-- 50&#45;&gt;52 -->\n",
       "<g id=\"edge52\" class=\"edge\"><title>50-&gt;52</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M4453.25,-103.882C4469.99,-93.9191 4487.86,-83.2862 4504.42,-73.4299\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"4506.67,-76.1651 4513.48,-68.0433 4503.09,-70.1497 4506.67,-76.1651\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_tree=search.best_estimator_\n",
    "clf_best=best_tree\n",
    "graph=Source(tree.export_graphviz(best_tree, out_file=None\n",
    "                                  , feature_names=list(X_train)\n",
    "                                  , class_names=['0- neutral', '1- anger', '2- contempt', '3- disgust',\n",
    "         '4- fear', '5- happiness', '6- surprise']\n",
    "                                  , filled=True)) \n",
    "display(SVG(graph.pipe(format='svg')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "4656ac41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.62\n",
      "Точность модели на тестовой выборке:  0.26\n"
     ]
    }
   ],
   "source": [
    "train_accuracy = clf_best.score(X_train, y_train)\n",
    "test_accuracy = clf_best.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (test_accuracy, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "3a0b1045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Дерево решений\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Дерево решений\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, clf_best.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "e5b30cef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.50      0.39       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.16      0.17      0.16        60\n",
      "           4       0.00      0.00      0.00        75\n",
      "           5       0.35      0.50      0.41        70\n",
      "           6       0.36      0.50      0.42        80\n",
      "\n",
      "    accuracy                           0.26       559\n",
      "   macro avg       0.17      0.24      0.20       559\n",
      "weighted avg       0.18      0.26      0.22       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, clf_best.predict(X_test),zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8f064f",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 26%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b545595",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139466b7",
   "metadata": {},
   "source": [
    "Основная идея бэггинга заключается в том, чтобы обучить несколько одинаковых моделей на разных образцах. Распределение выборки неизвестно, поэтому модели получатся разными.\n",
    "Рандомный лес - бэггинг на деревьях"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b3b6a46",
   "metadata": {},
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d9bb61",
   "metadata": {},
   "source": [
    "Бэггинг позволяет снизить дисперсию (variance) обучаемого классификатора, уменьшая величину, на сколько ошибка будет отличаться, если обучать модель на разных наборах данных, или другими словами, предотвращает переобучение. Эффективность бэггинга достигается благодаря тому, что базовые алгоритмы, обученные по различным подвыборкам, получаются достаточно различными, и их ошибки взаимно компенсируются при голосовании, а также за счёт того, что объекты-выбросы могут не попадать в некоторые обучающие подвыборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "fbdec363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4, 'n_estimators': 13}"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(random_state=17)\n",
    "par = {'n_estimators': range(13, 15), 'max_depth' : range(3, 5)}\n",
    "search = GridSearchCV(clf, par, cv=5, n_jobs=-1)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "dea4abbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=4, n_estimators=13, random_state=17)"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best_rf = search.best_estimator_\n",
    "clf_best_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "0d66ea9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.69\n",
      "Точность модели на тестовой выборке:  0.31\n"
     ]
    }
   ],
   "source": [
    "train_accuracy_rf = clf_best_rf.score(X_train, y_train)\n",
    "test_accuracy_rf = clf_best_rf.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (train_accuracy_rf, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (test_accuracy_rf, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "499a3e0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Случайный лес\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Случайный лес\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, clf_best_rf.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "b7e3bcd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.27      0.96      0.43       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.00      0.00      0.00        60\n",
      "           4       0.00      0.00      0.00        75\n",
      "           5       0.49      0.43      0.46        70\n",
      "           6       0.52      0.31      0.39        80\n",
      "\n",
      "    accuracy                           0.31       559\n",
      "   macro avg       0.18      0.24      0.18       559\n",
      "weighted avg       0.20      0.31      0.21       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, clf_best_rf.predict(X_test), zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c83401",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 31%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07dbadf",
   "metadata": {},
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "57687d87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.93\n",
      "Точность модели на тестовой выборке:  0.63\n",
      "Матрица несоответствий метода \"Линейный дискриминантный анализ\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LDA_model = LinearDiscriminantAnalysis()\n",
    "LDA_model.fit(X_train, y_train)\n",
    "LDA_prediction = LDA_model.predict(X_test)\n",
    "\n",
    "LDA_train_accuracy = LDA_model.score(X_train, y_train)\n",
    "LDA_test_accuracy = LDA_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (LDA_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (LDA_test_accuracy, 2))\n",
    "\n",
    "print('Матрица несоответствий метода \"Линейный дискриминантный анализ\":\\n')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(LDA_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "291b47da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.85      0.58       124\n",
      "           1       0.58      0.33      0.42        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.52      0.42      0.46        60\n",
      "           4       0.73      0.60      0.66        75\n",
      "           5       0.98      0.93      0.96        70\n",
      "           6       0.96      1.00      0.98        80\n",
      "\n",
      "    accuracy                           0.63       559\n",
      "   macro avg       0.60      0.59      0.58       559\n",
      "weighted avg       0.61      0.63      0.59       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, LDA_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2631c60",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 63%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79157063",
   "metadata": {},
   "source": [
    "## Бустинг:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b9e225",
   "metadata": {},
   "source": [
    "Используя различные аппроксимации для пороговой функции потерь $[z < 0]$, будем получать различные виды бустинга. Пример:\n",
    "- $e^{-z}$ - AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22068b2",
   "metadata": {},
   "source": [
    "Сделаем __AdaBoost__ для деревьев (пеньков скорее так как желательно использовать недообучающиеся модели)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add06354",
   "metadata": {},
   "source": [
    "## Метод адаптивного бустинга:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "29ad9c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "                   base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                         min_samples_leaf=5,\n",
       "                                                         min_samples_split=20),\n",
       "                   learning_rate=0.8, n_estimators=100)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2,\n",
    "                                                min_samples_split=20, min_samples_leaf=5), \n",
    "                         algorithm=\"SAMME\",n_estimators=100, learning_rate=0.8)\n",
    "bdt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "1151ae13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.94      0.39       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.36      0.17      0.23        60\n",
      "           4       0.00      0.00      0.00        75\n",
      "           5       1.00      0.21      0.35        70\n",
      "           6       0.95      0.44      0.60        80\n",
      "\n",
      "    accuracy                           0.31       559\n",
      "   macro avg       0.36      0.25      0.22       559\n",
      "weighted avg       0.35      0.31      0.24       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, bdt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa6f306",
   "metadata": {},
   "source": [
    "Найдем лучшие параметры вариируя параетры бустинга:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "de41c27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nbdt = AdaBoostClassifier(DecisionTreeClassifier(random_state=17, max_depth=2,\\n                                                min_samples_split=20, min_samples_leaf=5))\\npar = {'n_estimators': range(100, 700, 100), \\n     'learning_rate': [x / 10.0 for x in range(7, 10, 2)] }\\nsearch = GridSearchCV(bdt, par, cv=4, n_jobs=-1)\\nsearch.fit(X_train, y_train)\\nprint('search.best_params_')\\nbdt_best = search.best_estimator_\\nbdt_best.fit(X_train, y_train)\\n\""
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(random_state=17, max_depth=2,\n",
    "                                                min_samples_split=20, min_samples_leaf=5))\n",
    "par = {'n_estimators': range(100, 700, 100), \n",
    "     'learning_rate': [x / 10.0 for x in range(7, 10, 2)] }\n",
    "search = GridSearchCV(bdt, par, cv=4, n_jobs=-1)\n",
    "search.fit(X_train, y_train)\n",
    "print('search.best_params_')\n",
    "bdt_best = search.best_estimator_\n",
    "bdt_best.fit(X_train, y_train)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "0b52507e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"AdaBoost\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"AdaBoost\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, bdt.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d726cf",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 31%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6b7a84",
   "metadata": {},
   "source": [
    "## Метод Градиентного бустинга:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b1fabe",
   "metadata": {},
   "source": [
    "Подберем наилучший параметр learning rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "9ac01a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the dataset\n",
    "def get_dataset():\n",
    "    X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n",
    "    return X, y\n",
    " \n",
    "# get a list of models to evaluate\n",
    "def get_models():\n",
    "    models = dict()\n",
    "    # define learning rates to explore\n",
    "    for i in [0.01, 0.1, 0.5]:\n",
    "        key = '%.4f' % i\n",
    "        models[key] = GradientBoostingClassifier(learning_rate=i)\n",
    "    return models\n",
    " \n",
    "# evaluate a given model using cross-validation\n",
    "def evaluate_model(model, X, y):\n",
    "    # define the evaluation procedure\n",
    "    # evaluate the model and collect the results\n",
    "    scores = cross_val_score(model, X, y_train, scoring='accuracy', cv=10, n_jobs=-1)\n",
    "    return scores\n",
    " \n",
    "# define dataset\n",
    "X, y = get_dataset()\n",
    "# get the models to evaluate\n",
    "models = get_models()\n",
    "# evaluate the models and store results\n",
    "results, names = list(), list()\n",
    "for name, model in models.items():\n",
    "    # evaluate the model\n",
    "    scores = evaluate_model(model, X_train, y_train)\n",
    "    # store the results\n",
    "    results.append(scores)\n",
    "    names.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "ae1410a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.boxplot(results, labels=names, showmeans=True)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad569558",
   "metadata": {},
   "source": [
    "0.1 показал выше параметр accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "948881dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.997537 using {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 100}\n",
      "0.946849 (0.006547) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 30}\n",
      "0.976869 (0.005422) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 50}\n",
      "0.990652 (0.005588) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 100}\n",
      "0.984744 (0.004094) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 30}\n",
      "0.994096 (0.005302) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.995079 (0.005396) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
      "0.992616 (0.004544) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 30}\n",
      "0.995079 (0.004406) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 50}\n",
      "0.997537 (0.003972) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# define the model with default hyperparameters\n",
    "model = GradientBoostingClassifier()\n",
    "# define the grid of values to search\n",
    "grid = dict()\n",
    "grid['n_estimators'] = [30, 50, 100]\n",
    "grid['learning_rate'] = [0.1]\n",
    "grid['max_depth'] = [2, 3, 4]\n",
    "# define the grid search procedure\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid,\n",
    "                           n_jobs=-1, cv=10, scoring='accuracy')\n",
    "# execute the grid search\n",
    "grid_result = grid_search.fit(X_train, y_train)\n",
    "# summarize the best score and configuration\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "# summarize all scores that were evaluated\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "7191e2a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Градиентный бустинг\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Градиентный бустинг\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, grid_result.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70264151",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 35%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e62670",
   "metadata": {},
   "source": [
    "## Логистическая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "e32b8fb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.97\n",
      "Точность модели на тестовой выборке:  0.6\n",
      "Матрица несоответствий метода \"Логистическая регрессия\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR_model = LogisticRegression(solver='liblinear')\n",
    "# Обучим модель на обучающей выборке\n",
    "LR_model.fit(X_train, y_train) \n",
    "# Предскажем класс тестовой выборки\n",
    "LR_prediction = LR_model.predict(X_test) \n",
    "\n",
    "LR_train_accuracy = LR_model.score(X_train, y_train)\n",
    "LR_test_accuracy = LR_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (LR_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (LR_test_accuracy, 2))\n",
    "\n",
    "print('Матрица несоответствий метода \"Логистическая регрессия\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, LR_model.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "8bbf8b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.80      0.61       124\n",
      "           1       1.00      0.22      0.36        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.46      0.42      0.44        60\n",
      "           4       0.55      0.60      0.57        75\n",
      "           5       0.70      0.93      0.80        70\n",
      "           6       0.79      1.00      0.88        80\n",
      "\n",
      "    accuracy                           0.60       559\n",
      "   macro avg       0.57      0.57      0.52       559\n",
      "weighted avg       0.60      0.60      0.54       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, LR_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65abc61",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 60%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64e0ec9",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "64fb8e4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  1.0\n",
      "Точность модели на тестовой выборке:  0.25\n",
      "Матрица несоответствий метода \"KNN\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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T3b3U3UsPK+keSRvzF72v2S8vVunkW3X1/3tIc8uW65rv/SKStuLQp0dXrVlXuf/12nWV6lXSJcGI8pelvkhSr+652EuKO+nMccP1+tJVCUeUnyydlyz1RcrOz1hWJZ5U4/DP//B3ev2p27Rg1i3679umaOzIoZr2va8kHVbeRgwbqBWrKvTBmg3as7dKs55bqInjhicdVl6y1JcdO3drW3AR3I6duzVn3lvN9mKYLJ2XLPUlSz9jDP8iNYqKCnX7jRfq/Oumqbradek5o3XMEc3zL1aW+lLx8VZd+e37JElVVTU67/SR+vzoYxKOKj9ZOi9Z6kuWfsayyg423xDKgc0ek3SKpBJJ6yTd4u731fU9J44o9RfnvhZJPHFr06ow6RBwEDv3VCcdQmjateZnLK2y8nM2/nMn6fWFC0IvAdv2PtIHTflJ2IfV2/9+Zpm7l4Z+4EaIrFJ194ujOjYAAGnE8C8AIFYmqSADtzceDEkVABC7lFxXFLoWcfUvAABxoFIFAMQuLbfAhI1KFQCAkFCpAgDiZdmdUyWpAgBildv6LZtZleFfAABCQqUKAIhZetbqDRuVKgAAISGpAgBiZxb+o/427QYzW2Jmi83sMTNra2bdzOw5M1se/Flc/5EOjaQKAIhd3Fu/mVlfSddJKnX3YyUVSrpI0k2SXnD3oZJeCF7njaQKAGgpiiS1M7MiSe0lrZU0SdJDwdcfknRuUxogqQIA4hXB0G99w7/uvkbSjyStklQuabO7z5bU093Lg8+US+rRlK6RVAEAWVFiZgtqPabu+0IwVzpJ0uGS+kjqYGZfDjsAbqkBAMQqwsUfNtSxSflpkt539wrl2p8laYykdWbW293Lzay3pPVNCYBKFQDQEqySNNrM2lsuo58qaZmkpyVNCT4zRdJTTWmEShUAELu4135w99fMbKakhZKqJL0uabqkjpJmmNmVyiXeC5rSDkkVABC7JFZUcvdbJN1ywNu7lataQ5GqpGomtSpkRBrRade6MOkQ0AJk5eesIJsrCUYqVUkVANAyZHTpXy5UAgAgLFSqAIB4WXb3UyWpAgBilbtPNekoosHwLwAAIaFSBQDEjE3KAQBAPahUAQCxy2ihSlIFAMSP4V8AAFAnKlUAQLwasKl4c0WlCgBASKhUAQCxinCT8sRRqQIAEBIqVQBA7LJaqZJUAQCxy2hOZfgXAICwUKkCAGKX1eFfKlUAAEJCpQoAiFeGF38gqQIAYmVs/db8ff37j+ioM2/W2Iv/NelQQvH8K0v12fNv04jzvqf/fHB20uE0CX1JJ/qSTlnqSxZFllTNrL+Z/dHMlpnZEjP7RlRtNcTFZ5+kGT/+WpIhhKa6ukbfun2GHr/ra3p1xnf0xOwyvfVeedJh5YW+pBN9Sacs9cUs/EcaRFmpVkn6J3c/RtJoSdeY2bAI26vTmBOHqLhz+6SaD1XZkpUa3L9Eg/qVqHWrIk2eMELPzFmUdFh5oS/pRF/SKUt9yarIkqq7l7v7wuD5VknLJPWNqr2WpLxis/r2LN7/uk/PYpVXbE4wovzRl3SiL+mUpb4UmIX+SINYLlQys0GSTpT02kG+NlXSVEnqP2BAHOE0e+7+qfdS8vPUaPQlnehLOmWpL8017vpEfqGSmXWU9ISk6919y4Ffd/fp7l7q7qUlJd2jDicT+vToqjXrKve/XruuUr1KuiQYUf7oSzrRl3TKUl+yKtKkamatlEuoj7j7rCjbaklGDBuoFasq9MGaDdqzt0qznluoieOGJx1WXuhLOtGXdMpKX3IXFlnojzSIbPjXcj28T9Iyd/+PqNppqP/znQc0d+G72rhpm449+7u6aepZ+vI5JycdVl6Kigp1+40X6vzrpqm62nXpOaN1zBG9kw4rL/QlnehLOmWpL1llBxujD+XAZp+T9CdJb0qqCd7+trs/c6jvGTGy1F/+8/xI4olbQUE6fmsCgHyNPalUZWULQv/HrMvAY3zMTQ+GfVg9+7XRZe5eGvqBGyGyStXdX1Zug3cAAP5KWoZrw9ZiVlQCACBqrP0LAIhdRgtVKlUAAMJCpQoAiJUpt1NNFlGpAgAQEipVAEDssnrXIUkVABCvFK2AFDaGfwEACAmVKgAgdhktVKlUAQAIC5UqACBWJqVmU/GwkVQBALHLaE5l+BcAgLBQqQIAYsctNQAAoE5UqgCAWJlld06VpAoAiF1Wr/5l+BcAgJBQqQIAYpfNOpWkihZm557qpEMITbvWhUmHAOAAJFUAQOy4pQYAANSJShUAEKvc2r9JRxGNQyZVM/uJJD/U1939ukgiAgBkW4Y3Ka+rUl0QWxQAAGTAIZOquz9U+7WZdXD37dGHBADIuowWqvVfqGRmJ5vZUknLgtfHm9ndkUcGAEAz05Crf38s6QxJGyXJ3f8iaVyEMQEAMs6CedUwH2nQoKt/3f3DAwLOzh30AIBYtcirf2v50MzGSHIzay3pOgVDwQAA4BMNSapXS7pLUl9JayT9XtI1UQYFAMi2tAzXhq3epOruGyRdGkMsAAA0aw25+newmf3azCrMbL2ZPWVmg+MIDgCQTRbBIw0acvXvo5JmSOotqY+kxyU9FmVQAIDsMsttUh72Iw0aklTN3X/p7lXB42HVsXwhAABpZWZdzWymmb1lZsuCtRi6mdlzZrY8+LM43+MfMqkGjXST9Eczu8nMBpnZQDO7UdJv820QAACz8B8NdJekZ939aEnHK3c3y02SXnD3oZJeCF7npa4LlcqUq0j3hXpVra+5pO/n2ygAAHEzs87KLV50uSS5+x5Je8xskqRTgo89JOlFSf83nzbqWvv38HwOCABAfRK6pWawpApJD5jZ8coVj9+Q1NPdyyXJ3cvNrEe+DTRoRSUzO1bSMElt973n7r/It1EAACJQYma1d1ib7u7Ta70ukjRC0tfd/TUzu0tNGOo9mHqTqpndolxZPEzSM5ImSnpZEkkVAJCXiArVDe5eWsfXV0ta7e6vBa9nKpdU15lZ76BK7S1pfb4BNOTq3y9KOlXSR+5+hXITu23ybRAA0LKZwr+dpiG31Lj7R8otvXtU8NapkpZKelrSlOC9KZKeyrdvDRn+3enuNWZWFUzyrlduXLpZ+fr3H9HsuYtVUtxJcx/7dtLhNNnzryzVzXfOVHVNjS6bNEY3XH560iHlLUt9GXX+rerYvo0KCgpUVFigZ+//ZtIh5S1L54W+oJavS3okWMv+PUlXKFdgzjCzKyWtknRBvgdvSFJdYGZdJf1cuUndbZLm1fdNZtZW0kvKVbVFkma6+y35BtpUF599kv7+gnH62q2/TCqE0FRX1+hbt8/Qkz+9Vn16dtX4KXdo4rjjdPTg3kmH1mhZ6ss+j//kWh3WtWPSYTRJls4LfUmhxt0CEyp3f0PSwYaITw3j+PUO/7r719x9k7v/t6QJkqYEw8D12S1pvLsfL+kESWea2egmRdsEY04couLO7ZNqPlRlS1ZqcP8SDepXotatijR5wgg9M2dR0mHlJUt9yZIsnRf6gjjVtfjDiAMfkrpJKgqe18lztgUvWwUPVmIKQXnFZvXt+cmCH316Fqu8YnOCEeUvS32Rcr99X3zDPTrjq3fo4adeSTqcvGXpvNCXdGqJm5TfWcfXXNL4+g5uZoXKDRkPkTSt1hVXtT8zVdJUSeo/YEB9h4Qk90//bpKSn6dGy1JfJOmpe65Xr+5dtKFyqy66/m4NGdhDo08YknRYjZal80Jf0qkhV8k2R3Ut/vD5ph7c3aslnRDMyT5pZse6++IDPjNd0nRJGjGylEq2Afr06Ko16yr3v167rlK9SrokGFH+stQXSerVPRd7SXEnnTluuF5fuqpZJtUsnRf6gjjF8suCu29SbtmnM+NoL+tGDBuoFasq9MGaDdqzt0qznluoieOGJx1WXrLUlx07d2vb9l37n8+Z91bzu4AkkKXzQl/Sx9Qyh3+bxMy6S9rr7pvMrJ2k0yT9e1Tt1ef/fOcBzV34rjZu2qZjz/6ubpp6lr58zslJhdMkRUWFuv3GC3X+ddNUXe269JzROuaI5vmPd5b6UvHxVl357fskSVVVNTrv9JH6/OhjEo4qP1k6L/QFcbKDjdGHcmCz4cotTFyo4B4gd7+tru8ZMbLUX/7z/EjiiVtBQTp+a8Jf27mnOukQQtOudWHSISDjxp5UqrKyBaH/Y9ZzyLF+8Z0zwz6s7jr3mLJ6VlSKXEOWKTRJl0oa7O63mdkASb3cvc57Vd19kaQTwwkTAJAlWa07GjKnerekkyVdHLzeKmlaZBEBANBMNWRO9SR3H2Fmr0uSu1cGyzsBANBouU3Fs1mqNqRS3Rvcb+rS/guQaiKNCgCAZqghlep/SXpSUg8z+4Fyu9Z8J9KoAACZltU51XqTqrs/YmZlyi02bJLOdfdlkUcGAEAz05CrfwdI2iHp17Xfc/dVUQYGAMiujE6pNmj497fKzaeapLaSDpf0tqTPRBgXACCjTGrQpuLNUUOGf4+r/TrYoeaqyCICAKCZavQyhe6+0Mw+G0UwAICWocXtUrOPmf1jrZcFkkZIqogsIgAAmqmGVKqdaj2vUm6O9YlowgEAtAQZnVKtO6kGiz50dPdvxRQPACDjzCyzFyodcljbzIqCTcZHxBgPAADNVl2V6jzlEuobZva0pMclbd/3RXefFXFsAICMymih2qA51W6SNkoar0/uV3VJJFUAAGqpK6n2CK78XaxPkuk+0exsDgBoEVri2r+Fkjrqr5PpPiRVAEBeWuqKSuXufltskQAA0MzVlVSz+WsEACBxGS1U60yqp8YWRS01no2R5QJ+J0mldq0Lkw4BQIYdMqm6+8dxBgIAaCEsuxcqZXVNYwAAYtfoXWoAAGgqy+gUGUkVABCr3C01SUcRDYZ/AQAICZUqACB2VKoAAKBOVKoAgNhZRld/IKkCAGLFhUoAAKBeVKoAgHhZdtf+pVIFACAkVKoAgNi1xP1UAQAIHRcqAQCAelGpAgBil9HRXypVAADCQqUKAIiZqSCjW79RqQIAEBIqVQBArEzZnVMlqQIA4mXZvaWmxSTVNesqdc2tv9T6jVtVUGC67NwxuupLpyQdVt6ef2Wpbr5zpqpranTZpDG64fLTkw4pb/QlnehLOmWpL1kU+ZyqmRWa2etm9puo26pLYWGBbr3uPL3yP/+sZ+/9R90/8096+/3yJEPKW3V1jb51+ww9ftfX9OqM7+iJ2WV66z36kjT6kk70JZ0KzEJ/pEEcFyp9Q9KyGNqpU6+SLjr+6P6SpI4d2urIQT1Vvn5zwlHlp2zJSg3uX6JB/UrUulWRJk8YoWfmLEo6rLzQl3SiL+mUpb5kVaRJ1cz6SfqCpHujbKexVq3dqDffWaORxw5MOpS8lFdsVt+exftf9+lZrPKK5vkLAn1JJ/qSTlnpy74LlcJ+pEHUc6o/lnSjpE6H+oCZTZU0VZL69x8QcTjSth27dcXN9+lfrp+sTh3aRd5eFNz9U++l5QeqsehLOtGXdMpSX9IyXBu2yCpVMztb0np3L6vrc+4+3d1L3b20pHv3qMKRJO2tqtYVN9+nL55RqrM/f3ykbUWpT4+uWrOucv/rtesq1aukS4IR5Y++pBN9Sacs9SWrohz+HSvpHDNbKelXksab2cMRtlcnd9f1P3hURw7qqX+4ZHxSYYRixLCBWrGqQh+s2aA9e6s067mFmjhueNJh5YW+pBN9Sacs9YXh30Zy95sl3SxJZnaKpG+6+5ejaq8+r/3lPc343XwNO6KPTrns3yVJ//wPZ2vCmM8kFVLeiooKdfuNF+r866aputp16TmjdcwRvZMOKy/0JZ3oSzplqS9ZZQcbow+9kU+S6tl1fW7EyFJ/6ZV5kccTh6JCVoAE0LyNPalUZWULQq8BDz9muN/yi/Dvsrxi1MAydy8N/cCNEMviD+7+oqQX42gLAJByJllaxmtDRjkFAEBIWswyhQCA9MhmnUqlCgBAaKhUAQCxMrH4AwAAqAeVKgAgdtmsU0mqAIAEZHT0l+FfAEDLceAe32bWzcyeM7PlwZ/F9R2jLiRVAEDMTGbhPxrowD2+b5L0grsPlfRC8DpvJFUAQItwiD2+J0l6KHj+kKRzm9IGc6oAgFiZEqvofqxP7/Hd093LJcndy82sR1MaIKkCAGIX0dq/JWa2oNbr6e4+PWhv/x7fwSYvkSCpAgCyYkMdu9Ts2+P7LEltJXUO9vheZ2a9gyq1t6T1TQmAOVUAQOwsgkdd3P1md+/n7oMkXSTpD8Ee309LmhJ8bIqkp5rSL5IqAKAl+6GkCWa2XNKE4HXeGP4FAMQr4f1Ua+/x7e4bJZ0a1rFJqgCAWCV49W/kstovAABiR6UKAIhdksO/UaJSBQAgJKmqVE1SUSF5HtHZuac66RBC0651YdIhhIpz07Jks06lUgUAIDSpqlQBAC1DRqdUSaoAgHjlbqnJZlZl+BcAgJBQqQIAYpfV4V8qVQAAQkKlCgCImckyOqdKUgUAxI7hXwAAUCcqVQBArLilBgAA1ItKFQAQL8vunCpJFQAQu6wmVYZ/AQAICZUqACB2Wb1PlUoVAICQUKkCAGJlkgqyWaiSVAEA8WP4FwAA1IlKFQAQu6zeUtOikurzryzVzXfOVHVNjS6bNEY3XH560iHljb6k06jzb1XH9m1UUFCgosICPXv/N5MOKW+cl3TK0nnJokiTqpmtlLRVUrWkKncvjbK9ulRX1+hbt8/Qkz+9Vn16dtX4KXdo4rjjdPTg3kmFlDf6km6P/+RaHda1Y9JhNAnnJZ2ydF6YU83f5939hCQTqiSVLVmpwf1LNKhfiVq3KtLkCSP0zJxFSYaUN/qCqHFe0onzkn4t5kKl8orN6tuzeP/rPj2LVV6xOcGI8kdf0stMuviGe3TGV+/Qw0+9knQ4eeO8pFNWzsu+W2rCfqRB1HOqLmm2mbmkn7n79AM/YGZTJU2VpP4DBkQXiPun3muuE+X0Jb2euud69ereRRsqt+qi6+/WkIE9NPqEIUmH1Wicl3TKznkxhn/zNNbdR0iaKOkaMxt34Afcfbq7l7p7afeS7pEF0qdHV61ZV7n/9dp1lepV0iWy9qJEX9KrV/dc7CXFnXTmuOF6femqhCPKD+clnbJ2XrIo0qTq7muDP9dLelLSqCjbq8uIYQO1YlWFPlizQXv2VmnWcws1cdzwpMJpEvqSTjt27ta27bv2P58z761meQGJxHlJq8ycl2Drt7AfaRDZ8K+ZdZBU4O5bg+enS7otqvbqU1RUqNtvvFDnXzdN1dWuS88ZrWOOaJ5/sehLOlV8vFVXfvs+SVJVVY3OO32kPj/6mISjyg/nJZ2ydF6yyg42Rh/Kgc0GK1edSrnk/ai7/6Cu7xk5stTnvrYgkngASdq5pzrpEELTrnVh0iGEinOTPmNPKlVZ2YLQa8CjjzvR75v1h7APq88d2a0s6TtNIqtU3f09ScdHdXwAQPOUu/o3JeO1IWsxt9QAABC1FrVMIQAgHbJZp1KpAgAQGipVAED8MlqqklQBALFjRSUAAFAnKlUAQOwyekcNlSoAAGGhUgUAxC6jhSqVKgAAYaFSBQDEL6OlKkkVABArE7fUAACAelCpAgDilaJNxcNGpQoAQEioVAEAsctooUpSBQAkIKNZleFfAABCQqUKAIiZcUsNAACoG5UqWpR2rQuTDiE0ayt3Jh1CqPoUt0s6hNBk5dzsqa6J7NhZvaWGpAoAiJUps9cpMfwLAEBYqFQBAPHLaKlKpQoAQEioVAEAseOWGgAAUCcqVQBA7LilBgCAkGQ0pzL8CwBAWKhUAQDxyvDqD1SqAIAWwcz6m9kfzWyZmS0xs28E73czs+fMbHnwZ3G+bZBUAQCxswj+a4AqSf/k7sdIGi3pGjMbJukmSS+4+1BJLwSv80JSBQDEypS7+jfsR33cvdzdFwbPt0paJqmvpEmSHgo+9pCkc/PtG0kVANDimNkgSSdKek1ST3cvl3KJV1KPfI/LhUoAgNhFdJ1SiZktqPV6urtP/1TbZh0lPSHpenffYiHeNEtSBQBkxQZ3L63rA2bWSrmE+oi7zwreXmdmvd293Mx6S1qfbwAM/wIA4mcRPOprMleS3idpmbv/R60vPS1pSvB8iqSn8u0WlSoAIHYJLag/VtJlkt40szeC974t6YeSZpjZlZJWSbog3wZIqgCAFsHdX9aha9pTw2iDpAoAiB0L6mfA868s1c13zlR1TY0umzRGN1x+etIh5Y2+pFNz7st37pyhOa8uVbeuHfXUz78pSZr2i9ma+bvXVNylgyTp+q9O1LhRxyQZZl44L4hLpEnVzLpKulfSsZJc0lfd/c9Rtnko1dU1+tbtM/TkT69Vn55dNX7KHZo47jgdPbh3EuE0CX1Jp+bel3MnlOqSc8bo5tt/9Vfvf2Xy3+iKC05JJqgQcF7SKaOFauRX/94l6Vl3P1rS8cqtXpGIsiUrNbh/iQb1K1HrVkWaPGGEnpmzKKlwmoS+pFNz70vp8MHq0ql90mGEjvOCOEWWVM2ss6Rxyl2+LHff4+6bomqvPuUVm9W35ydrJPfpWazyis1JhdMk9CWdstSX2h59+hWdd9Wd+s6dM7R5646kw2k0zktKJXBLTRyirFQHS6qQ9ICZvW5m95pZhwM/ZGZTzWyBmS2o2FARWTDu/qn3mutEOX1Jpyz1ZZ8v/d3JevbBm/TEPTeoe7dOumP6b5IOqdE4L+mTy4GJLKgfuSiTapGkEZLucfcTJW3XQVb+d/fp7l7q7qXdS7pHFkyfHl21Zl3l/tdr11WqV0mXyNqLEn1Jpyz1ZZ+S4k4qLCxQQUGBvjjxJL351qqkQ2o0zgviFGVSXS1ptbu/FryeqVySTcSIYQO1YlWFPlizQXv2VmnWcws1cdzwpMJpEvqSTlnqyz4VG7fsf/783MUaOqhXgtHkh/OSQhHsUJOW0YfIrv5194/M7EMzO8rd31buxtqlUbVXn6KiQt1+44U6/7ppqq52XXrOaB1zRPO4+u9A9CWdmntfvvmvj2j+ohXatHm7xl/yL7rmstM1f9EKvbVircykPj276XvfOD/pMBuN84I42cHmG0I7uNkJyt1S01rSe5KucPfKQ31+5MhSn/vagkN9GUAtayt3Jh1CqPoUt0s6hNBk5dxMmjBWb76xMPQa8LgTRvj/zp4b9mE1pGf7svoW1I9apPepuvsbkhLtIAAghVIyXBs2dqkBACAkLWqZQgBAGqTnFpiwUakCABASKlUAQOzScgtM2EiqAIBYpWhVwdAx/AsAQEioVAEA8ctoqUqlCgBASKhUAQCx45YaAABQJypVAEDsuKUGAICQZDSnMvwLAEBYqFQBAPFK0abiYaNSBQAgJFSqAIAEZLNUJakCAGJlYvgXAADUg0oVABC7jBaqVKoAAIQlVZXqwoVlG9q1sg8ibqZE0oaI24gLfUkn+pJO9KXxBkZ14KzOqaYqqbp796jbMLMF7l4adTtxoC/pRF/Sib6kCwvqAwCAOqWqUgUAtBDZLFRbZKU6PekAQkRf0om+pBN9QeTM3ZOOAQDQghx/4kifPefV0I/bq0vrsqTnmltipQoAQCRaVFI1szPN7G0ze9fMbko6nnyZ2f1mtt7MFicdS1OZWX8z+6OZLTOzJWb2jaRjypeZtTWzeWb2l6AvtyYdU1OYWaGZvW5mv0k6lqYys5Vm9qaZvWFmC5KOpynMrKuZzTSzt4K/NycnHVNjmUXzSIMWc6GSmRVKmiZpgqTVkuab2dPuvjTZyPLyoKSfSvpFwnGEoUrSP7n7QjPrJKnMzJ5rpudlt6Tx7r7NzFpJetnMfufu4Y9zxeMbkpZJ6px0ICH5vLtn4T7VuyQ96+5fNLPWktonHVA+uKWm+Rsl6V13f8/d90j6laRJCceUF3d/SdLHSccRBncvd/eFwfOtyv0j3jfZqPLjOduCl62CR7O8aMHM+kn6gqR7k44FnzCzzpLGSbpPktx9j7tvSjQo/JWWlFT7Svqw1uvVaqb/eGeVmQ2SdKKk1xIOJW/BkOkbktZLes7dm2tffizpRkk1CccRFpc028zKzGxq0sE0wWBJFZIeCIbm7zWzDkkHlReL4JECLSmpHux/ebOsIrLIzDpKekLS9e6+Jel48uXu1e5+gqR+kkaZ2bEJh9RoZna2pPXuXpZ0LCEa6+4jJE2UdI2ZjUs6oDwVSRoh6R53P1HSdknN9vqQLGpJSXW1pP61XveTtDahWFBLMP/4hKRH3H1W0vGEIRiSe1HSmclGkpexks4xs5XKTZOMN7OHkw2padx9bfDneklPKjcd1BytlrS61gjITOWSbLOT0UK1RSXV+ZKGmtnhweT+RZKeTjimFs/MTLn5oWXu/h9Jx9MUZtbdzLoGz9tJOk3SW4kGlQd3v9nd+7n7IOX+nvzB3b+ccFh5M7MOwUVwCoZKT5fULK+cd/ePJH1oZkcFb50qqTle1MfVv82du1eZ2bWSfi+pUNL97r4k4bDyYmaPSTpFUomZrZZ0i7vfl2xUeRsr6TJJbwZzkZL0bXd/JrmQ8tZb0kPBleYFkma4e7O/HSUDekp6Mvf7m4okPeruzyYbUpN8XdIjQXHwnqQrEo4HtbCiEgAgVieMKPU//Cn8a/gO61jEikoAAGRFixn+BQCkgyk9c6Bho1IFACAkJFUAAELC8C8AIHYM/wIpZWbVwe4ji83scTPLe4FxM3vQzL4YPL/XzIbV8dlTzGxMHm2sNLOShr5/wGe21fX1g3z+e2b2zcbGCCA/JFVkwU53P8Hdj5W0R9LVtb8Y3DfaaO7+9/XslnOKpEYnVQC5XWrC/i8NSKrImj9JGhJUkX80s0eVW1ii0MzuMLP5ZrbIzK6Scis6mdlPzWypmf1WUo99BzKzF82sNHh+ppktDPZKfSFY/P9qSTcEVfLfBCsqPRG0Md/Mxgbfe5iZzQ4WQP+ZGrCimpn9b7D4+5IDF4A3szuDWF4ws+7Be0eY2bPB9/zJzI4O5f8mgEZhThWZYWZFyi2Yvm+1nFGSjnX394PEtNndP2tmbSTNNbPZyu2Kc5Sk45RbeWeppPsPOG53ST+XNC44Vjd3/9jM/lvSNnf/UfC5RyX9p7u/bGYDlFu96xhJt0h62d1vM7MvSGrILilfDdpop9zev0+4+0ZJHSQtdPd/MrP/Fxz7WknTJV3t7svN7CRJd0san8f/RiB6KVpWMGwkVWRBu1pLHP5JubWEx0ia5+7vB++fLmn4vvlSSV0kDVVub8rH3L1a0loz+8NBjj9a0kv7juXuh9rL9jRJw+yTfy06B2vOjpM0Ofje35pZZQP6dJ2ZnRc87x/EulG5rdj+J3j/YUmzgh1+xkh6vFbbbRrQBpCINC2AHzaSKrJgZ7Dd2n5Bctle+y1JX3f33x/wubNU/xaA1oDPSLnplJPdfedBYmnweqBmdopyCfpkd99hZi9KanuIj3vQ7qYD/x8AiB9zqmgpfi/pH4Jt5mRmRwY7lrwk6aJgzrW3pM8f5Hv/LOlvzezw4Hu7Be9vldSp1udmKzcUq+BzJwRPX5J0afDeREnF9cTaRVJlkFCPVq5S3qdA0r5q+xLlhpW3SHrfzC4I2jAzO76eNoBkZXTvN5IqWop7lZsvXWhmiyX9TLmRmiclLZf0pqR7JM058BvdvUK5edBZZvYXfTL8+mtJ5+27UEnSdZJKgwuhluqTq5BvlTTOzBYqNwy9qp5Yn5VUZGaLJH1f0qu1vrZd0mfMrEy5OdPbgvcvlXRlEN8SSZMa8P8EQMjYpQYAEKsRI0v9pVfmh37cTm0LEt+lhjlVAEDssnr1L8O/AACEhEoVABC7jBaqVKoAAISFShUAEL+MlqpUqgCA2CWxoH6whvfbZvaumd0URb9IqgCAzAt2q5qm3PrgwyRdXNfWjvli+BcAECtTIrfUjJL0rru/J0lm9ivlFkmpa3vHRqNSBQC0BH0lfVjr9ergvVBRqQIAYrVwYdnv27WykggO3dbMFtR6Pd3dpwfPD1Ybh76kIEkVABArdz8zgWZXK7eN4j79JK0NuxGGfwEALcF8SUPN7HAzay3pIklPh90IlSoAIPPcvcrMrlVuG8hCSfe7+5Kw22GXGgAAQsLwLwAAISGpAgAQEpIqAAAhIakCABASkioAACEhqQIAEBKSKgAAISGpAgAQkv8Pds0KzRdZHqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNN_model = KNeighborsClassifier(n_neighbors = 1)\n",
    "KNN_model.fit(X_train, y_train)\n",
    "KNN_prediction = KNN_model.predict(X_test)\n",
    "\n",
    "KNN_train_accuracy = KNN_model.score(X_train, y_train)\n",
    "KNN_test_accuracy = KNN_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (KNN_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (KNN_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода \"KNN\":\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(KNN_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "a6aab85c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.23      0.94      0.37       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.83      0.08      0.15        60\n",
      "           4       0.00      0.00      0.00        75\n",
      "           5       0.50      0.07      0.12        70\n",
      "           6       0.50      0.19      0.27        80\n",
      "\n",
      "    accuracy                           0.25       559\n",
      "   macro avg       0.30      0.18      0.13       559\n",
      "weighted avg       0.28      0.25      0.15       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, KNN_prediction, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a85d33c",
   "metadata": {},
   "source": [
    "Один из самых низких показателей на тестовой выборке  <br>\n",
    "__Итог: accuracy = 25%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7670ba",
   "metadata": {},
   "source": [
    "## Наивный байесовский алгоритм классификации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "598d856d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.9\n",
      "Точность модели на тестовой выборке:  0.31\n",
      "Матрица несоответствий метода \"Наивный байесовский алгоритм\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GNB_model = GaussianNB() \n",
    "GNB_model.fit(X_train, y_train)\n",
    "GNB_prediction = GNB_model.predict(X_test) \n",
    "\n",
    "GNB_train_accuracy = GNB_model.score(X_train, y_train)\n",
    "GNB_test_accuracy = GNB_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (GNB_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (GNB_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода \"Наивный байесовский алгоритм\":\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(GNB_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "684ccb56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.84      0.38       124\n",
      "           1       0.28      0.11      0.16        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.37      0.17      0.23        60\n",
      "           4       0.00      0.00      0.00        75\n",
      "           5       0.83      0.36      0.50        70\n",
      "           6       0.96      0.31      0.47        80\n",
      "\n",
      "    accuracy                           0.31       559\n",
      "   macro avg       0.38      0.26      0.25       559\n",
      "weighted avg       0.38      0.31      0.27       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, GNB_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cabda7",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 31%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a23db12",
   "metadata": {},
   "source": [
    "## Метод опорных векторов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "e7c2ae2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'degree': 1}"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC()\n",
    "par={'degree': range(1,15)}\n",
    "search=GridSearchCV(clf, par, cv=5, n_jobs=-1)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "e8e4e0a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.94\n",
      "Точность модели на тестовой выборке:  0.59\n",
      "Матрица несоответствий метода опорных векторов:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVC_model = SVC(gamma='scale', kernel='poly', degree=1) \n",
    "SVC_model.fit(X_train, y_train)\n",
    "SVC_prediction = SVC_model.predict(X_test) \n",
    "\n",
    "SVC_train_accuracy = SVC_model.score(X_train, y_train)\n",
    "SVC_test_accuracy = SVC_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (SVC_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (SVC_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода опорных векторов:\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(SVC_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "368fefce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.39      0.91      0.54       124\n",
      "           1       0.34      0.11      0.17        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.97      0.58      0.73        60\n",
      "           4       0.97      0.40      0.57        75\n",
      "           5       0.98      0.93      0.96        70\n",
      "           6       0.82      0.94      0.88        80\n",
      "\n",
      "    accuracy                           0.59       559\n",
      "   macro avg       0.64      0.55      0.55       559\n",
      "weighted avg       0.62      0.59      0.55       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, SVC_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11de944e",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 59%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cb280b",
   "metadata": {},
   "source": [
    "**Высокие уровни точности показали: ЛДА после снижения размерности (63%), SVM после снижения размерности (59%), логистическая регрессия после снижения размерности (60%).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d442ab",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c524f88b",
   "metadata": {},
   "source": [
    "# Воспользуемся LDA после PCA для сижения размерности с 138 признаков до числа классов - 1, то есть до 6 признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458917bb",
   "metadata": {},
   "source": [
    "### Проведем те же самые процессы обучения, но на преобразованных по-новому данных:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f0be26",
   "metadata": {},
   "source": [
    "### Осуществим снижение размерности при использовании LDA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "d9458f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LinearDiscriminantAnalysis(n_components = 6)\n",
    "lda_result = lda.fit_transform(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "c38dd543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2032, 6)"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "7aa4d073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Итоговые данные \n",
    "data_lda = pd.DataFrame(data=lda_result,\n",
    "          index=pd.RangeIndex(range(0, lda_result.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, lda_result.shape[1])))\n",
    "X_train = data_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "1366a2f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-1.974578</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "a2aebaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(559, 6)"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_result = lda.transform(X_test)\n",
    "lda_result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "90b48ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Итоговые данные \n",
    "data_lda = pd.DataFrame(data=lda_result,\n",
    "          index=pd.RangeIndex(range(0, lda_result.shape[0])),\n",
    "          columns=pd.RangeIndex(range(0, lda_result.shape[1])))\n",
    "X_test = data_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "d33f0ccb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>-3.572123</td>\n",
       "      <td>2.705434</td>\n",
       "      <td>-1.451672</td>\n",
       "      <td>-1.089019</td>\n",
       "      <td>0.014124</td>\n",
       "      <td>0.923076</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.565926</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.660716</td>\n",
       "      <td>0.039449</td>\n",
       "      <td>0.992124</td>\n",
       "      <td>-0.004451</td>\n",
       "      <td>-1.243534</td>\n",
       "      <td>-0.891688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.063436</td>\n",
       "      <td>-1.371174</td>\n",
       "      <td>3.180567</td>\n",
       "      <td>-2.089581</td>\n",
       "      <td>-1.371058</td>\n",
       "      <td>-0.462701</td>\n",
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       "          0         1         2         3         4         5\n",
       "0 -4.634088  2.011877 -0.648952 -0.472628  0.263528  0.124827\n",
       "1 -3.572123  2.705434 -1.451672 -1.089019  0.014124  0.923076\n",
       "2 -1.565926  1.442323  0.292461 -0.790509  0.431468  0.506549\n",
       "3 -0.660716  0.039449  0.992124 -0.004451 -1.243534 -0.891688\n",
       "4 -0.063436 -1.371174  3.180567 -2.089581 -1.371058 -0.462701"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df9ec14",
   "metadata": {},
   "source": [
    "# Обучим те же модели:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf42726",
   "metadata": {},
   "source": [
    "## Классификатор Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "7da4de4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6, 'min_samples_leaf': 2, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Подбор лучших параметров:\n",
    "par = {'max_depth':range(3, 7), 'min_samples_split':range(2, 6),\n",
    "     'min_samples_leaf':range(2, 6)}\n",
    "search = GridSearchCV(clf,par,cv=5)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "82fdb47a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"660.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0 &lt;= -3.404</text>\n",
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       "<text text-anchor=\"middle\" x=\"282.5\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0 &lt;= -2.389</text>\n",
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       "<text text-anchor=\"middle\" x=\"656.5\" y=\"-283.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
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       "<g id=\"edge12\" class=\"edge\"><title>11-&gt;12</title>\n",
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       "<text text-anchor=\"middle\" x=\"1726.5\" y=\"-528.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">2 &lt;= -1.148</text>\n",
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       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\"><title>14-&gt;15</title>\n",
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       "<text text-anchor=\"middle\" x=\"2457.5\" y=\"-528.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">1 &lt;= 0.099</text>\n",
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       "<!-- 14&#45;&gt;46 -->\n",
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       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\"><title>15-&gt;16</title>\n",
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       "<g id=\"edge17\" class=\"edge\"><title>16-&gt;17</title>\n",
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       "<text text-anchor=\"middle\" x=\"1284.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">3 &lt;= 0.109</text>\n",
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       "<text text-anchor=\"middle\" x=\"1481.5\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">5 &lt;= -2.232</text>\n",
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "best_tree=search.best_estimator_\n",
    "clf_best=best_tree\n",
    "graph=Source(tree.export_graphviz(best_tree, out_file=None\n",
    "                                  , feature_names=list(X_train)\n",
    "                                  , class_names=['0- neutral', '1- anger', '2- contempt', '3- disgust',\n",
    "         '4- fear', '5- happiness', '6- surprise']\n",
    "                                  , filled=True)) \n",
    "display(SVG(graph.pipe(format='svg')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "537255de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.93\n",
      "Точность модели на тестовой выборке:  0.5\n"
     ]
    }
   ],
   "source": [
    "train_accuracy = clf_best.score(X_train, y_train)\n",
    "test_accuracy = clf_best.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (test_accuracy, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "d54d97d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Дерево решений\":\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Дерево решений\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, clf_best.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "b93f9b69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.62      0.57       124\n",
      "           1       0.52      0.44      0.48        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.18      0.42      0.25        60\n",
      "           4       0.94      0.20      0.33        75\n",
      "           5       0.45      0.71      0.56        70\n",
      "           6       1.00      0.88      0.93        80\n",
      "\n",
      "    accuracy                           0.50       559\n",
      "   macro avg       0.52      0.47      0.45       559\n",
      "weighted avg       0.55      0.50      0.48       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, clf_best.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0240a886",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 50%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "626108f2",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "019310fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4, 'n_estimators': 15}"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(random_state=17)\n",
    "par = {'n_estimators': range(15, 20), 'max_depth' : range(2, 5)}\n",
    "search = GridSearchCV(clf, par, cv=5, n_jobs=-1)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "87c92ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=4, n_estimators=15, random_state=17)"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best_rf = search.best_estimator_\n",
    "clf_best_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "2c11a41a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.88\n",
      "Точность модели на тестовой выборке:  0.54\n"
     ]
    }
   ],
   "source": [
    "train_accuracy_rf = clf_best_rf.score(X_train, y_train)\n",
    "test_accuracy_rf = clf_best_rf.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (train_accuracy_rf, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (test_accuracy_rf, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "1e880d65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Случайный лес\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Случайный лес\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, clf_best_rf.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "d226ad5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.36      0.98      0.53       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       1.00      0.25      0.40        60\n",
      "           3       0.74      0.33      0.46        60\n",
      "           4       0.75      0.40      0.52        75\n",
      "           5       0.72      0.93      0.81        70\n",
      "           6       1.00      0.62      0.77        80\n",
      "\n",
      "    accuracy                           0.54       559\n",
      "   macro avg       0.65      0.50      0.50       559\n",
      "weighted avg       0.60      0.54      0.49       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, clf_best_rf.predict(X_test), zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01bdfcdd",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 54%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cccc00",
   "metadata": {},
   "source": [
    "## Метод адаптивного бустинга:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "d930dcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "                   base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                         min_samples_leaf=5,\n",
       "                                                         min_samples_split=20),\n",
       "                   learning_rate=0.8, n_estimators=100)"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2,\n",
    "                                                min_samples_split=20, min_samples_leaf=5), \n",
    "                         algorithm=\"SAMME\",n_estimators=100, learning_rate=0.8)\n",
    "bdt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "4f33ec0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.87      0.48       124\n",
      "           1       0.39      0.22      0.28        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.80      0.58      0.67        60\n",
      "           4       1.00      0.40      0.57        75\n",
      "           5       1.00      0.79      0.88        70\n",
      "           6       0.80      0.56      0.66        80\n",
      "\n",
      "    accuracy                           0.52       559\n",
      "   macro avg       0.62      0.49      0.51       559\n",
      "weighted avg       0.60      0.52      0.51       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, bdt.predict(X_test), zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7f1311",
   "metadata": {},
   "source": [
    "Найдем лучшие параметры вариируя параетры бустинга:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "d4c97d51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"AdaBoost\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"AdaBoost\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, bdt.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183f7e89",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 52%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4062bdd0",
   "metadata": {},
   "source": [
    "## Метод Градиентного бустинга:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3e226c",
   "metadata": {},
   "source": [
    "Подберем наилучший параметр learning rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "ba2a5233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.989662 using {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 100}\n",
      "0.961605 (0.017039) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 30}\n",
      "0.973911 (0.015587) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 50}\n",
      "0.983761 (0.009357) with: {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 100}\n",
      "0.976376 (0.012225) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 30}\n",
      "0.983758 (0.010345) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.985724 (0.011116) with: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n",
      "0.983751 (0.012081) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 30}\n",
      "0.986214 (0.011194) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 50}\n",
      "0.989662 (0.006775) with: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# define the model with default hyperparameters\n",
    "model = GradientBoostingClassifier()\n",
    "# define the grid of values to search\n",
    "grid = dict()\n",
    "grid['n_estimators'] = [30, 50, 100]\n",
    "grid['learning_rate'] = [0.1]\n",
    "grid['max_depth'] = [2, 3, 4]\n",
    "# define the grid search procedure\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid,\n",
    "                           n_jobs=-1, cv=10, scoring='accuracy')\n",
    "# execute the grid search\n",
    "grid_result = grid_search.fit(X_train, y_train)\n",
    "# summarize the best score and configuration\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "# summarize all scores that were evaluated\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "f08cf7ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица несоответствий метода \"Градиентный бустинг\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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m9q6ZPWxmrc2sk5k9b2bvJ78W1ruhOqioiohI7JlZD+AKoNTdhwC5wHnANcCL7t4feDH5OGUqqiIiEjqz4G8NkAe0MbM8oC2wCpgE3J/8/v3A6U3JSwcqiYhI6NJ0oFKRmc2t9Xiau08DcPeVZvafwHJgO/Ccuz9nZsXuXp58TbmZdW1KACqqIiISFxUHukpNcl/pJOBgYAPwqJl9LegAVFRFRCRc0Zyn+iXgQ3dfC2BmM4HRwGoz657sUrsDa5oyiPapiohIc7AcONrM2lpi7vkEYBHwJHBx8jUXA080ZRB1qiIiEqooFn9w99lmNgOYB1QBbwHTgHxgupl9g0ThPacp46ioiohIs+Du1wPX7/P0ThJdayBUVEVEJHQxXaVQRVVERMIX17V/M6qotmuTx4jDiqMOQ2KsU37LqEMQkRjLqKIqIiLNQ0wbVZ1SIyIiEhR1qiIiEi7TPlUREZFAJM5TjTqK9ND0r4iISEDUqYqISMgadlHxbKROVUREJCDqVEVEJHQxbVRVVEVEJHya/hUREZE6qVMVEZFwRXOR8lCoUxUREQmIOlUREQlVFBcpD4s6VRERkYCoUxURkdDFtVNVURURkdDFtKZq+ldERCQo6lRFRCR0cZ3+VacqIiISEHWqIiISrhgv/qCiKiIiobIYX/ot1kW1TYsczh/eg+4dWoM7D85byerNu/j6yF50ateC9Vt3c/c/lrN9d03UoTbaC28sZOptM6iuqeHCSaO5avKJUYeUMuWSmZRLZopTLnGUtn2qZtbLzF42s0VmtsDMrkzXWAdy9tDuLFy9hZuef5//eHEJn2zeyfgBRSxeu4UbnnufxWu3cOKhXcIOq8mqq2v40a3TefT27/Dm9Ot47Lky3ltaHnVYKVEumUm5ZKY45WIW/C0TpPNApSrgB+4+CDga+K6ZDU7jeJ/ROi+HfkXt+PuySgCq3dm+u4ah3Tswe/kGAGYv38DQkg5hhRSYsgXL6NuriD49i2jZIo8zxw9n1qvzow4rJcolMymXzBSnXOIqbUXV3cvdfV7y/mZgEdAjXePtq3O7lmzZWcXXjurBj8f14/zhJbTMNdq3ymPTjioANu2oon2r7JsBL1+7kR7FhXsflxQXUr52Y4QRpU65ZCblkpnilEuOWeC3TBBKRTGzPsCRwOz9fG8KMAWgXVH3wMbMNehV0IZH/1nOR5XbOWtoN8YPyL6p3v1x9889lyGfp0ZTLplJuWSmOOWSrXHXJ+3nqZpZPvAY8D1337Tv9919mruXuntpmw6Fn99Aiiq3V7Fh+24+qtwOwNsrN9GroA2bd1bRoXXib4kOrfPYvLMqsDHDUtK1gJWrK/c+XrW6km5FHSOMKHXKJTMpl8wUp1ziKq1F1cxakCioD7r7zHSOta/NO6uo3L6brvktARjQNZ9PNu3gnfJNjDqoAIBRBxUwv/xzdT7jDR/cmyXL1/LRygp27a5i5vPzmDB2aNRhpUS5ZCblkpnikkviwCIL/JYJ0jb9a4kM7wYWufuv0jVOXR79ZzmTR/QiN8eo2LqLB8pWkGPG10f24pg+hVRu283dsz+OIrQmycvL5darz+WsK+6kutq5YOLRDOoX3NR5mJRLZlIumSlOucSV7W+OPpANmx0L/BV4B9hzIui17j7rQD/Tpd9hPuk//pyWeMJ2x5lDog5BRKRJxowqpaxsbuAtYMfeg3z0NfcFvVme+c7RZe5eGviGGyFtnaq7/43EBd5FREQ+I1Oma4OmBfVFREQCkn0naYqISNaLaaOqTlVERCQo6lRFRCRURuJKNXGkTlVERCQg6lRFRCR0OfFsVFVURUQkZBm0AlLQNP0rIiISEHWqIiISupg2qupURUREgqJOVUREQmWQMRcVD5qKqoiIhC6mNVXTvyIiIkFRpyoiIqHTKTUiIiJSJ3WqIiISKrP47lNVURURkdDF9ehfTf+KiIgERJ2qiIiELp59aoYV1YLWLThzcNeow5AYW7BiU9QhBOawnh2iDkFE9pFRRVVERJoHnVIjIiIidVKnKiIioUqs/Rt1FOlxwKJqZr8B/EDfd/cr0hKRiIjEW4wvUl5Xpzo3tChERERi4IBF1d3vr/3YzNq5+9b0hyQiInEX00a1/gOVzOwYM1sILEo+PsLMfpv2yERERLJMQ47+/TVwErAOwN3/CYxNY0wiIhJzltyvGuQtEzTo6F93/3ifgKvTE46IiMRdszz6t5aPzWw04GbWEriC5FSwiIiIfKohRfVS4HagB7ASeBb4bjqDEhGReMuU6dqg1VtU3b0CuCCEWERERLJaQ47+7WtmfzGztWa2xsyeMLO+YQQnIiLxZGm4NWhcswIzm2Fm75nZouQZLp3M7Hkzez/5tTDVvBpy9O9DwHSgO1ACPAo8nOqAIiLSvJklLlIe9K2BbgeecfeBwBEkjhG6BnjR3fsDLyYfp6QhRdXc/U/uXpW8PUAdyxeKiIhkIjPrQOKU0LsB3H2Xu28AJgF7Fjy6Hzg91THqWvu3U/Luy2Z2DfAIiWL6FeDpVAcUERGJ6DilvsBa4F4zOwIoA64Eit29HMDdy80s5Qt713WgUhmJIron9W/V+p4DN6Y6qIiISBoUmVntdeunufu0Wo/zgOHA5e4+28xupwlTvftT19q/Bwc5kIiIyB5pOqWmwt1L6/j+CmCFu89OPp5BoqiuNrPuyS61O7Am1QAatKKSmQ0BBgOt9zzn7n9MdVAREZGwufsnZvaxmQ1w98XACcDC5O1i4Obk1ydSHaPeompm1wPHkyiqs4AJwN8AFVUREUlJhGs/XA48mFwhcClwCYmDdqeb2TeA5cA5qW68IZ3q2SQOO37L3S8xs2LgrlQHFBGR5s1o1CkwgXL3t4H9TRGfEMT2G1JUt7t7jZlVJQ9HXkPiCKqMtmtXFT/5+X3s3l1NdU0Nx4wYxHlnHc+fZ77CC6+8RYf2bQE4/5xxHDWsf8TRNt4Lbyxk6m0zqK6p4cJJo7lq8olRh5SybM5ldcUGbrr9UdZXbsFyjInjR3Dul8dw9yMv8Jfn51LQoR0A3/raiRxz1ICIo22cbH5f9qVcJCwNKapzzawA+AOJI4K3AP+o74fMrDXwGtAqOc4Md78+9VAbp0WLXH469SLatG5JVVU11914L8OPOASA004axaRTR4cVSuCqq2v40a3TefyOyygpLmDcxb9kwtjDGdi3e9ShNVq255Kbk8Nlk09hQL8ebNu+k6//4A5GDEt8zs798hjOP/0LEUeYmmx/X2pTLhnImvFFyt39O+6+wd1/B4wHLnb3Sxqw7Z3AOHc/AhgGnGxmRzcp2kYwM9q0bgkkPohV1TVhDZ12ZQuW0bdXEX16FtGyRR5njh/OrFfnRx1WSrI9l6JOHRjQrwcAbdu0ok/PrlSs2xRxVE2X7e9LbcpFwlTX4g/D6/qeu8+ra8Pu7iS6WoAWyVuoKzFV19Rw9U/+wCer13Pyl0Zw6CE9eWv+B/zfC3N45fX5HHJwCRefP578dm3CDKvJytdupEfxp0tTlhQXUvbusugCaoJY5bKmkn99uIrBh/Zi/nsfMXPW33n2lbcY0K8Hl11yCh3ys+dzFqv3RblkpOZ4lZrb6vieA+Pq27iZ5ZKYMj4EuLPWuUG1XzMFmALQpXvP+jbZKLk5Odz282+xdesObrn9zyz/eA0nnVDK2aePxTAefuxl7n/oeb77zYmBjptuib9XPitbP59xyWXb9p38+y0PcuXXT6Vd29accfIoJp8zDjP4w0MvcMe9s7j28rOiDrPB4vK+gHLJVA1ZIzcbHTAvd/9iHbd6C2pyG9XuPgzoCYxMnu+672umuXupu5d2LOycciJ1adeuNUMG9uGt+R9Q0DGf3JwccnKM8ccP5/0lK9MyZjqVdC1g5erKvY9Xra6kW1HHCCNKXRxyqaqq5rpbH+LEscM47pjER7xTQXtyc3PIyclh4okjWPT+xxFH2ThxeF/2UC4SplD+WEguWPwKcHIY4wFs3LSVrVt3ALBz127mL1hKj5IiKjds3vua2XPf46CeKS/xGJnhg3uzZPlaPlpZwa7dVcx8fh4Txg6NOqyUZHsu7s5/3DmT3j27cN6kY/c+X7H+0/2qr725gL69i6MIL2XZ/r7Uplwyj5GY/g36lgkatKJSKsysC7Db3TeYWRvgS8At6RpvX5UbtnDHtCeorqnBa5zRowZTeuSh3P67x1n20Wow6FpUwKVfPzWskAKTl5fLrVefy1lX3El1tXPBxKMZ1C/Ljv5LyvZc5i/6iGdfeYt+vbsx+arfAInTZ1746z95/8NyzIxuXQv40aWnRxtoI2X7+1KbcpEw2f7m6APZsNlQEpfQySW5WoW731DXz/Q/bJjf/ufn0hJP2MYNzL4OuDlYsCL7j8zd47CeHaIOQWJuzKhSysrmBt4CFh8yxL9624ygN8vtpw8qq2ft37RryDKFBlwA9HX3G8zsIKCbu9d5rqq7zweODCZMERGJk5zMmK0NXEP2qf4WOAb4avLxZuDOtEUkIiKSpRqyT3WUuw83s7cA3L0yuRCxiIhIo5nF9zzVhnSqu5PnmzrsPQApPssTiYiIBKQhnep/A48DXc3s5ySuWnNdWqMSEZFYi+s+1XqLqrs/aGZlJC6LY8Dp7r4o7ZGJiIhkmYYc/XsQsA34S+3n3H15OgMTEZH4iuku1QZN/z5NYn+qAa2Bg4HFwGFpjEtERGLKILKLlKdbQ6Z/D6/9OHn1mm+lLSIREZEs1ehlCt19npmNSEcwIiLSPMT1KjUN2af6/VoPc4DhwNq0RSQiIpKlGtKptq91v4rEPtbH0hOOiIg0BzHdpVp3UU0u+pDv7j8KKR4REYk5M4vtgUoHnNY2szx3ryYx3SsiIiL1qKtT/QeJgvq2mT0JPAps3fNNd5+Z5thERCSmYtqoNmifaidgHTCOT89XdUBFVUREpJa6imrX5JG/7/JpMd0jPVc2FxGRZqE5rv2bC+Tz2WK6h4qqiIikpLmuqFTu7jeEFomIiEiWq6uoxvPPCBERiVxMG9U6i+oJoUWRtKumhuWbt4U9rDQjh/XsEHUIgVlVuT3qEOQASgrbRB2CROSARdXd14cZiIiINBMW3wOV4rqmsYiISOgafZUaERGRprKYHrajoioiIqFKnFITdRTpoelfERGRgKhTFRGR0KlTFRERkTqpUxURkdBZTFd/UFEVEZFQ6UAlERERqZc6VRERCZfFd+1fdaoiIiIBUacqIiKha47XUxUREQmcDlQSERGReqlTFRGR0MV09ledqoiISFDUqYqISMiMnJhe+k2dqoiISEDUqYqISKiM+O5TVVEVEZFwWXxPqYl9Ua2pqeFXNz9Ax4J8vvmdM3nmqdd58/V3aNe+DQCnTvwCg4f0jTjKxnvhjYVMvW0G1TU1XDhpNFdNPjHqkFKmXDLDdbdN59U3F9KpIJ8n/vBDAO7843PM+L/ZFHZsB8D3vj6BsSMHRRlmg8Qpl31l82esOUh7UTWzXGAusNLdT0v3ePt67eV5FHfrxI4du/Y+d9y4o/ji+BFhhxKY6uoafnTrdB6/4zJKigsYd/EvmTD2cAb27R51aI2mXDLH6eNLOX/iaKbe+shnnr/ozC9wyTnHRxNUiuKUS23Z/hmrLa4rKoVxoNKVwKIQxvmcDZWbWfjuUo4eMzSK4dOmbMEy+vYqok/PIlq2yOPM8cOZ9er8qMNKiXLJHKVD+9KxfduowwhEnHKpLds/Y81BWouqmfUETgXuSuc4B/L4jJf48hljP7dD/K+vvsWtN93Hw396hm3bdkQRWpOUr91Ij+LCvY9LigspX7sxwohSp1wy30NPvsEZ37qN626bzsbN26IOp0myPZe4fMb2HKgU9C0TpLtT/TVwNVBzoBeY2RQzm2tmc7dUrgts4AXvLKF9flt6HdTtM8+PGTuM6274N3547cV06NCOJx57JbAxw+Lun3suUz5QjaVcMttXvnwMz9x3DY/9z1V06dSeX057KuqQUhaHXOL0GcsxC/yWCdJWVM3sNGCNu5fV9Tp3n+bupe5eml/YObDxP1yyknffWcIN103jj/c8xfuLl/PAvU/TvkM7cnJyyMkxjjl2KMuXlQc2ZlhKuhawcnXl3serVlfSrahjhBGlTrlktqLC9uTm5pCTk8PZE0bxznvLow4pZXHIJY6fsbhJZ6c6BphoZsuAR4BxZvZAGsf7jNNOH8tPf3Ep/++mKVz09dPoP+AgvnbJqWzcuGXva+a//T7dS4rCCikwwwf3ZsnytXy0soJdu6uY+fw8JozNzv3GyiWzrV23ae/9F15/l/59utXx6swWh1zi9BmL6/Rv2o7+dfepwFQAMzse+KG7fy1d4zXUXx5/jVUr1gDQqXNHzjl/fMQRNV5eXi63Xn0uZ11xJ9XVzgUTj2ZQv+w7+g+USyb54S8eZM78JWzYuJVx59/Edy88kTnzl/DeklWYQUlxJ3565VlRh9kgccqltmz/jDUHtr85+sAH+bSo1nlKTe9BQ33qPU+mPZ4wTB7RJ+oQJOZWVW6POgQ5gJLCNlGHEIgxo0opK5sbeA948KChfv0fg9+nfcnI3mXuXhr4hhshlMUf3P0V4JUwxhIRkQxnYJkyXxswLagvIiISkNgvUygiIpknnn2qOlUREZHAqFMVEZFQGVr7V0REROqhTlVEREIXzz5VnaqIiEQgqhWVzCzXzN4ys6eSjzuZ2fNm9n7ya2F926iLiqqIiDQn+16O9BrgRXfvD7yYfJwyFVUREQmZYRb8rd5R93850knA/cn79wOnNyUzFVUREWkufs3nL0da7O7lAMmvXZsygIqqiIiEykgUn6BvQNGe63Mnb1P2jtnAy5E2lY7+FRGR0KVp7d+KOhbU33M50lOA1kCH5OVIV5tZd3cvN7PuwJqmBKBOVUREYs/dp7p7T3fvA5wHvJS8HOmTwMXJl10MPNGUcdSpiohI6DLoPNWbgelm9g1gOXBOUzamoioiIs1K7cuRuvs64ISgtq2iKiIi4Yrx9VRVVEVEJFR7jv6No7jmJSIiEjp1qiIiErq4Tv+qUxUREQlIRnWq+S3z+MJBRVGHIZIVSgrbRB1CoH716gdRhxCY7x93SNQhZLx49qnqVEVERAKTUZ2qiIg0DzHdpaqiKiIi4UqcUhPPqqrpXxERkYCoUxURkdDFdfpXnaqIiEhA1KmKiEjIDIvpPlUVVRERCZ2mf0VERKRO6lRFRCRUOqVGRERE6qVOVUREwmXx3aeqoioiIqGLa1HV9K+IiEhA1KmKiEjo4nqeqjpVERGRgKhTFRGRUBmQE89GVUVVRETCp+lfERERqZM6VRERCV1cT6mJbVH9ZO0Grv/VdNZVbiYnxzjjpJF8ddKxbNy8jam3PET56kq6Fxdy8zXn0yG/bdThNtoLbyxk6m0zqK6p4cJJo7lq8olRh5Qy5ZKZsj2X//753bRs1ZKcHCMnJ4d/+975bN+2g8f+NIuNlZvoWNiBsy48hTZtW0cdaqNk+/sSd2ktqma2DNgMVANV7l6azvFqy8vN4apvnMrAQ3qwddtOLvzebxh1ZH/+8kIZI484hMnnHM99j77CfY++yhWXTAgrrEBUV9fwo1un8/gdl1FSXMC4i3/JhLGHM7Bv96hDazTlkpnikstF3z6btu3a7H38+ktzOLh/L8aMG8HrL83h9Zfm8KXTvhBhhI0Tl/cFtE+1Kb7o7sPCLKgARZ06MPCQHgC0a9uKPr26sGbdJl6dvZDTThgOwGknDOeVNxeEGVYgyhYso2+vIvr0LKJlizzOHD+cWa/OjzqslCiXzBSnXGpbvGApQ0sHAzC0dDCLFyyNOKLGiev7EifN4kClVavXs3jpKoYM6MX6DVso6tQBSBTeyg1bIo6u8crXbqRHceHexyXFhZSv3RhhRKlTLpkpDrkYxoPTZvKH/3qIeW++A8DWzVtp36EdAO07tGPblm1RhthocXhf4NNTaoK+ZYJ071N14Dkzc+D37j5t3xeY2RRgCkD3Hr0CD2Db9p1c/YsH+cE3v0x+lu07ORB3/9xz2brTX7lkpjjkMvmyc2nfMZ+tm7fxwLSZdO5SWP8PZbg4vC8JpunfFI1x9+HABOC7ZjZ23xe4+zR3L3X30sLORYEOXlVVzdW/eICTjx/GuNFDAOhUkE/F+k0AVKzfRGFBfqBjhqGkawErV1fufbxqdSXdijpGGFHqlEtmikMu7Tsm/t9u174tA4f0Y9XHq2nXvh2bN20FYPOmrbTNsoMU4/C+xF1ai6q7r0p+XQM8DoxM53j7jM0Nt8/g4F5d+doZnx6IcNyowTz14jwAnnpxHseNGhxWSIEZPrg3S5av5aOVFezaXcXM5+cxYezQqMNKiXLJTNmey66du9m5Y9fe+0v/tZwu3TozYHBf5s9dCMD8uQsZcFjfKMNstGx/X/ZKXvot6FsmSNv0r5m1A3LcfXPy/onADekab1//XPgRs15+i0P6dOP8y28H4DsXncTFZx/H1Jsf4onn5tCtSwE3T70grJACk5eXy61Xn8tZV9xJdbVzwcSjGdQv+47+A+WSqbI9l61btjH9vr8AUFNTw5AjB3LIwD6U9CrmsT/N4u1/LKBDQXvOvujUiCNtnGx/X5oD298cfSAbNutLojuFRPF+yN1/XtfPHHbEcJ8+67W0xBO2fsXZN60sEqVfvfpB1CEE5vvHHRJ1CIEYM6qUsrK5gfeAAw8/0u+e+VLQm+XYQzuVhX2myb7S1qm6+1LgiHRtX0REslPi6N8Mma8NWLM4pUZERCQMsV2mUEREMlc8+1R1qiIiIoFRpyoiIuGLaauqoioiIqHTikoiIiJSJ3WqIiISupieUaNOVUREJCjqVEVEJHQxbVTVqYqIiARFnaqIiIQvpq2qiqqIiITK0Ck1IiIiUg91qiIiEq4Muqh40NSpioiIBESdqoiIhC6mjaqKqoiIRCCmVVXTvyIiIgFRpyoiIiEznVIjIiIidcuoTrV1Xg79ivOjDkNEIvD94w6JOoTAjLrxxahDCMT75ZvTtu24nlKTUUVVRETiz4jtcUqa/hUREQmKOlUREQlfTFtVdaoiIiIBUacqIiKh0yk1IiIiUid1qiIiEjqdUiMiIhKQmNZUTf+KiIgERUVVRETCZWm61TesWS8ze9nMFpnZAjO7Mvl8JzN73szeT34tTDU1FVUREWkuqoAfuPsg4Gjgu2Y2GLgGeNHd+wMvJh+nREVVRERCZ2n4rz7uXu7u85L3NwOLgB7AJOD+5MvuB05PNS8dqCQiIqEyoj/618z6AEcCs4Fidy+HROE1s66pbldFVURE4qLIzObWejzN3aft+yIzywceA77n7psswAqvoioiIqFLU6Na4e6ldY5r1oJEQX3Q3Wcmn15tZt2TXWp3YE2qAWifqoiINAuWaEnvBha5+69qfetJ4OLk/YuBJ1IdQ52qiIiEL5p9qmOAC4F3zOzt5HPXAjcD083sG8By4JxUB1BRFRGR0EWxoL67/40Dl/MTghhD078iIiIBUacqIiKhi/qUmnRpVkX1hTcWMvW2GVTX1HDhpNFcNfnEqENKmXLJTMolM2VzLueN6sWkI0twhyVrtnDjk4to3SKHm84aQknHNqzauJ1/f+xdNu+oijpUIc3Tv2ZWYGYzzOy95FqLx6RzvLpUV9fwo1un8+jt3+HN6dfx2HNlvLe0PKpwmkS5ZCblkpmyOZcu7VvxlRG9mHzXHM7//WxycozxhxVz0Zg+zP2wkrN/+3fmfljJRWN6Rx1qo0Ww9G8o0r1P9XbgGXcfCBxBYkmoSJQtWEbfXkX06VlEyxZ5nDl+OLNenR9VOE2iXDKTcslM2Z5Lbo7RKi+HXDNa5+VSsWUnYwcU8fT8xB8GT88v57gBXSKOUvZIW1E1sw7AWBLnBOHuu9x9Q7rGq0/52o30KP70wgMlxYWUr90YVThNolwyk3LJTNmcy9rNO3nwzeU8ceUYnr7qWLbsrGL20vV0ateSdVt2AbBuyy4K27aMONIUxLRVTWen2hdYC9xrZm+Z2V1m1m7fF5nZFDOba2Zz11asTVsw7v6557J1R7lyyUzKJTNlcy7tW+cx9tAizvjNG5z667/RpmUuJx/eLeqwmixRA8NfUD8M6SyqecBw4H/c/UhgK/u5nI67T3P3Uncv7VKUvimMkq4FrFxduffxqtWVdCvqmLbx0km5ZCblkpmyOZcRB3di1YYdbNi2m+oa5+X31nB4z46s37qLzvmJ7rRzfksqt+2KOFLZI51FdQWwwt1nJx/PIFFkIzF8cG+WLF/LRysr2LW7ipnPz2PC2KFRhdMkyiUzKZfMlM25rN64gyE9O9AqL/GrekSfTiyr2MpfF1dw6tDuAJw6tDuvLa6IMszGs8RsQdC3TJC2U2rc/RMz+9jMBrj7YhKrVSxM13j1ycvL5darz+WsK+6kutq5YOLRDOrXPapwmkS5ZCblkpmyOZcFqzbx0qI1/PGbI6mucf71yWb+d95K2rTM5RdnHc7EYSV8smkH1854J+pQJcn2t78hsI2bDQPuAloCS4FL3L3yQK8/6qhSf3323AN9W0QkK4y68cWoQwjE+3/4DttWLQ68Bzx82HD/3+deD3qzHFLctqy+q9SkW1oXf3D3t4FIExQRkQyUIdO1QdPavyIiIgFpVssUiohIJsicU2CCpk5VREQkIOpURUQkdJlyCkzQVFRFRCRUGbSqYOA0/SsiIhIQdaoiIhK+mLaq6lRFREQCok5VRERCp1NqREREpE7qVEVEJHQ6pUZERCQgMa2pmv4VEREJijpVEREJVwZdVDxo6lRFREQCok5VREQiEM9WVUVVRERCZWj6V0REROqhTlVEREIX00ZVnaqIiEhQMqpTnTevrKJNC/sozcMUARVpHiMsyiUzKZfMpFwar3e6NhzXfaoZVVTdvUu6xzCzue5emu5xwqBcMpNyyUzKJbNoQX0RERGpU0Z1qiIi0kzEs1Ftlp3qtKgDCJByyUzKJTMpF0k7c/eoYxARkWbkiCOP8udefTPw7Xbr2LIs6n3NzbFTFRERSYtmVVTN7GQzW2xmH5jZNVHHkyozu8fM1pjZu1HH0lRm1svMXjazRWa2wMyujDqmVJlZazP7h5n9M5nLz6KOqSnMLNfM3jKzp6KOpanMbJmZvWNmb5vZ3KjjaQozKzCzGWb2XvL/m2OijqmxzNJzywTN5kAlM8sF7gTGAyuAOWb2pLsvjDaylNwH3AH8MeI4glAF/MDd55lZe6DMzJ7P0vdlJzDO3beYWQvgb2b2f+4e/DxXOK4EFgEdog4kIF909zicp3o78Iy7n21mLYG2UQeUCp1Sk/1GAh+4+1J33wU8AkyKOKaUuPtrwPqo4wiCu5e7+7zk/c0kfon3iDaq1HjCluTDFslbVh60YGY9gVOBu6KORT5lZh2AscDdAO6+y903RBqUfEZzKqo9gI9rPV5Blv7yjisz6wMcCcyOOJSUJadM3wbWAM+7e7bm8mvgaqAm4jiC4sBzZlZmZlOiDqYJ+gJrgXuTU/N3mVm7qINKiaXhlgGaU1Hd3z95VnYRcWRm+cBjwPfcfVPU8aTK3avdfRjQExhpZkMiDqnRzOw0YI27l0UdS4DGuPtwYALwXTMbG3VAKcoDhgP/4+5HAluBrD0+JI6aU1FdAfSq9bgnsCqiWKSW5P7Hx4AH3X1m1PEEITkl9wpwcrSRpGQMMNHMlpHYTTLOzB6INqSmcfdVya9rgMdJ7A7KRiuAFbVmQGaQKLJZJ6aNarMqqnOA/mZ2cHLn/nnAkxHH1OyZmZHYP7TI3X8VdTxNYWZdzKwgeb8N8CXgvUiDSoG7T3X3nu7eh8T/Jy+5+9ciDitlZtYueRAcyanSE4GsPHLe3T8BPjazAcmnTgCy8aA+Hf2b7dy9yswuA54FcoF73H1BxGGlxMweBo4HisxsBXC9u98dbVQpGwNcCLyT3BcJcK27z4oupJR1B+5PHmmeA0x396w/HSUGioHHE3+/kQc85O7PRBtSk1wOPJhsDpYCl0Qcj9SiFZVERCRUw4aX+kt/Df4Yvs75eVpRSUREJC6azfSviIhkBiNz9oEGTZ2qiIhIQFRURUREAqLpXxERCZ2mf0UylJlVJ68+8q6ZPWpmKS8wbmb3mdnZyft3mdngOl57vJmNTmGMZWZW1NDn93nNlrq+v5/X/9TMftjYGEUkNSqqEgfb3X2Yuw8BdgGX1v5m8rzRRnP3f6vnajnHA40uqiKSuEpN0P9lAhVViZu/Aocku8iXzewhEgtL5JrZL81sjpnNN7NvQWJFJzO7w8wWmtnTQNc9GzKzV8ysNHn/ZDObl7xW6ovJxf8vBa5KdslfSK6o9FhyjDlmNib5s53N7LnkAui/pwErqpnZ/yYXf1+w7wLwZnZbMpYXzaxL8rl+ZvZM8mf+amYDA/nXFJFG0T5ViQ0zyyOxYPqe1XJGAkPc/cNkYdro7iPMrBXwupk9R+KqOAOAw0msvLMQuGef7XYB/gCMTW6rk7uvN7PfAVvc/T+Tr3sI+C93/5uZHURi9a5BwPXA39z9BjM7FWjIVVK+nhyjDYlr/z7m7uuAdsA8d/+Bmf2/5LYvA6YBl7r7+2Y2CvgtMC6Ff0aR9MugZQWDpqIqcdCm1hKHfyWxlvBo4B/u/mHy+ROBoXv2lwIdgf4krk35sLtXA6vM7KX9bP9o4LU923L3A13L9kvAYPv0t0WH5JqzY4Ezkz/7tJlVNiCnK8zsjOT9XslY15G4FNufk88/AMxMXuFnNPBorbFbNWAMkUhk0gL4QVNRlTjYnrzc2l7J4rK19lPA5e7+7D6vO4X6LwFoDXgNJHanHOPu2/cTS4PXAzWz40kU6GPcfZuZvQK0PsDLPTnuhn3/DUQkfNqnKs3Fs8C3k5eZw8wOTV6x5DXgvOQ+1+7AF/fzs38HjjOzg5M/2yn5/Gagfa3XPUdiKpbk64Yl774GXJB8bgJQWE+sHYHKZEEdSKJT3iMH2NNtn09iWnkT8KGZnZMcw8zsiHrGEIlWTK/9pqIqzcVdJPaXzjOzd4Hfk5ipeRx4H3gH+B/g1X1/0N3XktgPOtPM/smn069/Ac7Yc6AScAVQmjwQaiGfHoX8M2Csmc0jMQ29vJ5YnwHyzGw+cCPwZq3vbQUOM7MyEvtMb0g+fwHwjWR8C4BJDfg3EZGA6So1IiISquFHlfprb8wJfLvtW+dEfpUa7VMVEZHQxfXoX03/ioiIBESdqoiIhC6mjao6VRERkaCoUxURkfDFtFVVpyoiIqGLYkH95Brei83sAzO7Jh15qaiKiEjsJa9WdSeJ9cEHA1+t69KOqdL0r4iIhMqI5JSakcAH7r4UwMweIbFISl2Xd2w0daoiItIc9AA+rvV4RfK5QKlTFRGRUM2bV/ZsmxZWlIZNtzazubUeT3P3acn7++uNA19SUEVVRERC5e4nRzDsChKXUdyjJ7Aq6EE0/SsiIs3BHKC/mR1sZi2B84Angx5EnaqIiMSeu1eZ2WUkLgOZC9zj7guCHkdXqREREQmIpn9FREQCoqIqIiISEBVVERGRgKioioiIBERFVUREJCAqqiIiIgFRURUREQmIiqqIiEhA/j8nfateiwfGewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Матрица несоответствий метода \"Градиентный бустинг\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, grid_result.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "2ce123c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.93      0.48       124\n",
      "           1       0.00      0.00      0.00        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.68      0.42      0.52        60\n",
      "           4       1.00      0.20      0.33        75\n",
      "           5       0.76      0.71      0.74        70\n",
      "           6       0.99      1.00      0.99        80\n",
      "\n",
      "    accuracy                           0.51       559\n",
      "   macro avg       0.53      0.47      0.44       559\n",
      "weighted avg       0.51      0.51      0.44       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, grid_result.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e607b03",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 51%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2f4442",
   "metadata": {},
   "source": [
    "## Логистическая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "4a5ba683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.92\n",
      "Точность модели на тестовой выборке:  0.57\n",
      "Матрица несоответствий метода \"Логистическая регрессия\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_model = LogisticRegression(solver='liblinear')\n",
    "# Обучим модель на обучающей выборке\n",
    "LR_model.fit(X_train, y_train) \n",
    "# Предскажем класс тестовой выборки\n",
    "LR_prediction = LR_model.predict(X_test) \n",
    "\n",
    "LR_train_accuracy = LR_model.score(X_train, y_train)\n",
    "LR_test_accuracy = LR_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (LR_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (LR_test_accuracy, 2))\n",
    "\n",
    "print('Матрица несоответствий метода \"Логистическая регрессия\":\\n')\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(y_test, LR_model.predict(X_test)))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "961073ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.78      0.61       124\n",
      "           1       0.37      0.22      0.28        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.54      0.58      0.56        60\n",
      "           4       0.41      0.20      0.27        75\n",
      "           5       0.80      1.00      0.89        70\n",
      "           6       0.75      1.00      0.86        80\n",
      "\n",
      "    accuracy                           0.57       559\n",
      "   macro avg       0.48      0.54      0.50       559\n",
      "weighted avg       0.49      0.57      0.51       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, LR_model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca09b1b",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 57%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad400bc",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "ee3accfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.96\n",
      "Точность модели на тестовой выборке:  0.57\n",
      "Матрица несоответствий метода \"KNN\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNN_model = KNeighborsClassifier(n_neighbors = 10)\n",
    "KNN_model.fit(X_train, y_train)\n",
    "KNN_prediction = KNN_model.predict(X_test)\n",
    "\n",
    "KNN_train_accuracy = KNN_model.score(X_train, y_train)\n",
    "KNN_test_accuracy = KNN_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (KNN_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (KNN_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода \"KNN\":\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(KNN_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "7dd91b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.37      0.86      0.51       124\n",
      "           1       0.48      0.11      0.18        90\n",
      "           2       0.75      0.25      0.38        60\n",
      "           3       0.52      0.50      0.51        60\n",
      "           4       0.71      0.20      0.31        75\n",
      "           5       0.93      1.00      0.97        70\n",
      "           6       0.97      0.88      0.92        80\n",
      "\n",
      "    accuracy                           0.57       559\n",
      "   macro avg       0.68      0.54      0.54       559\n",
      "weighted avg       0.65      0.57      0.53       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, KNN_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda0816c",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 57%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7bec1d",
   "metadata": {},
   "source": [
    "## Наивный байесовский алгоритм классификации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "defa8a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.94\n",
      "Точность модели на тестовой выборке:  0.54\n",
      "Матрица несоответствий метода \"Наивный байесовский алгоритм\":\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GNB_model = GaussianNB() \n",
    "GNB_model.fit(X_train, y_train)\n",
    "GNB_prediction = GNB_model.predict(X_test) \n",
    "\n",
    "GNB_train_accuracy = GNB_model.score(X_train, y_train)\n",
    "GNB_test_accuracy = GNB_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (GNB_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (GNB_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода \"Наивный байесовский алгоритм\":\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(GNB_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "bc7f2911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.35      0.87      0.50       124\n",
      "           1       0.42      0.11      0.18        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.83      0.75      0.79        60\n",
      "           4       1.00      0.20      0.33        75\n",
      "           5       0.92      0.93      0.92        70\n",
      "           6       0.70      0.75      0.72        80\n",
      "\n",
      "    accuracy                           0.54       559\n",
      "   macro avg       0.60      0.52      0.49       559\n",
      "weighted avg       0.58      0.54      0.49       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, GNB_prediction, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551a5b05",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 54%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5adcd7",
   "metadata": {},
   "source": [
    "## Метод опорных векторов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "33a6068f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность модели на обучающей выборке:  0.94\n",
      "Точность модели на тестовой выборке:  0.53\n",
      "Матрица несоответствий метода опорных векторов:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVC_model = SVC(gamma='scale', kernel='poly', degree=1) \n",
    "SVC_model.fit(X_train, y_train)\n",
    "SVC_prediction = SVC_model.predict(X_test) \n",
    "\n",
    "SVC_train_accuracy = SVC_model.score(X_train, y_train)\n",
    "SVC_test_accuracy = SVC_model.score(X_test, y_test)\n",
    "print ('Точность модели на обучающей выборке: ', round (SVC_train_accuracy, 2))\n",
    "print ('Точность модели на тестовой выборке: ', round (SVC_test_accuracy, 2))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "print('Матрица несоответствий метода опорных векторов:\\n')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix(SVC_prediction, y_test))\n",
    "disp.plot(cmap = 'Blues', ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "8040c9c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " clasification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.40      0.87      0.54       124\n",
      "           1       0.49      0.22      0.31        90\n",
      "           2       0.00      0.00      0.00        60\n",
      "           3       0.48      0.50      0.49        60\n",
      "           4       0.75      0.20      0.32        75\n",
      "           5       0.99      1.00      0.99        70\n",
      "           6       0.95      1.00      0.98        80\n",
      "\n",
      "    accuracy                           0.58       559\n",
      "   macro avg       0.58      0.54      0.52       559\n",
      "weighted avg       0.58      0.58      0.53       559\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('\\n clasification report:\\n', classification_report(y_test, SVC_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95987c09",
   "metadata": {},
   "source": [
    "__Итог: accuracy = 53%__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbdd3e62",
   "metadata": {},
   "source": [
    "**После проведения снижения размерности после PCA на LDA мы получили выше предсказательные способности всех классификаторов.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1e909b",
   "metadata": {},
   "source": [
    "**Высокие уровни точности показали: Адаптивный бустинг после PCA и LDA (54%), Логистическая регрессия после PCA и LDA (57%), KNN после PCA и LDA (59%)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea6590a",
   "metadata": {},
   "source": [
    "### ВЫВОД:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741b058f",
   "metadata": {},
   "source": [
    "### Таким образом, лучшими алгоритмомами для данной задачи стали алгоритм К ближайших соседей с параметром K=10, осуществеленный после снижения размерности с помощью метода главных компонент (оставление 90 % дисперсии) и последовательно примененного линейного дискриминантного анализа для снижения размерности до величины числа классов - 1 (до 6 признаков) (acc = 59%), а также ЛДА в качестве классификатора после снижения размерности с помощью МГК (acc = 64%)."
   ]
  }
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