pred_analytics/lec5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Загрузка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ирисы Фишера -- набор данных для задачи классификации, на примере которого Рональд Фишер в 1936 году продемонстрировал работу разработанного им метода дискриминантного анализа. Иногда его также называют ирисами Андерсона, так как данные были собраны американским ботаником Эдгаром Андерсоном. Этот набор данных стал классическим и часто используется в литературе для иллюстрации работы различных статистических алгоритмов.\n",
    "\n",
    "Ирисы Фишера состоят из данных о 150 экземплярах ириса, по 50 экземпляров из трёх видов -- Ирис щетинистый (Iris setosa), Ирис виргинский (Iris virginica) и Ирис разноцветный (Iris versicolor).\n",
    "\n",
    "Для каждого экземпляра измерялись четыре характеристики (в сантиметрах):\n",
    "1. Длина наружной доли околоцветника (англ. sepal length);\n",
    "2. Ширина наружной доли околоцветника (англ. sepal width);\n",
    "3. Длина внутренней доли околоцветника (англ. petal length);\n",
    "4. Ширина внутренней доли околоцветника (англ. petal width).\n",
    "\n",
    "На основании этого набора данных требуется построить правило классификации, определяющее вид растения по данным измерений. Это задача многоклассовой классификации, так как имеется три класса -- три вида ириса.\n",
    "\n",
    "Один из классов (Iris setosa) линейно-разделим от двух остальных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "irisdf = pd.DataFrame(data=iris.data, columns=iris.feature_names)  # type: ignore\n",
    "display(irisdf.head())\n",
    "display(irisdf.tail())\n",
    "\n",
    "y = iris.target  # type: ignore\n",
    "display(y)\n",
    "\n",
    "y_names = iris.target_names  # type: ignore\n",
    "display(y_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация данных с учетом понимания из особенностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.visual import draw_data_2d\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "draw_data_2d(irisdf, 0, 1, y, y_names, plt.subplot(2, 2, 1))\n",
    "draw_data_2d(irisdf, 2, 3, y, y_names, plt.subplot(2, 2, 2))\n",
    "draw_data_2d(irisdf, 0, 2, y, y_names, plt.subplot(2, 2, 3))\n",
    "draw_data_2d(irisdf, 1, 3, y, y_names, plt.subplot(2, 2, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация данных без понимания их особенностей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "draw_data_2d(irisdf, 0, 1, subplot=plt.subplot(2, 2, 1))\n",
    "draw_data_2d(irisdf, 2, 3, subplot=plt.subplot(2, 2, 2))\n",
    "draw_data_2d(irisdf, 0, 2, subplot=plt.subplot(2, 2, 3))\n",
    "draw_data_2d(irisdf, 1, 3, subplot=plt.subplot(2, 2, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Иерархическая агломеративная кластеризация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также формируется дендрограмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import get_linkage_matrix, run_agglomerative\n",
    "from src.visual import draw_dendrogram\n",
    "from scipy.cluster import hierarchy\n",
    "\n",
    "tree = run_agglomerative(irisdf)\n",
    "linkage_matrix = get_linkage_matrix(tree)\n",
    "draw_dendrogram(linkage_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Получение результатов иерархической кластеризации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также производится сравнение с реальным разбиением\n",
    "\n",
    "https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2,\n",
       "       2, 2, 2, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2,\n",
       "       2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 3], dtype=int32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = hierarchy.fcluster(linkage_matrix, 10, criterion=\"distance\")\n",
    "display(result)\n",
    "display(y)\n",
    "result = [0 if val == 1 else 1 if val == 3 else 2 for val in result]\n",
    "\n",
    "plt.figure(figsize=(16, 24))\n",
    "draw_data_2d(irisdf, 0, 1, result, y_names, plt.subplot(4, 2, 1))\n",
    "draw_data_2d(irisdf, 0, 1, y, y_names, plt.subplot(4, 2, 2))\n",
    "draw_data_2d(irisdf, 2, 3, result, y_names, plt.subplot(4, 2, 3))\n",
    "draw_data_2d(irisdf, 2, 3, y, y_names, plt.subplot(4, 2, 4))\n",
    "draw_data_2d(irisdf, 0, 2, result, y_names, plt.subplot(4, 2, 5))\n",
    "draw_data_2d(irisdf, 0, 2, y, y_names, plt.subplot(4, 2, 6))\n",
    "draw_data_2d(irisdf, 1, 3, result, y_names, plt.subplot(4, 2, 7))\n",
    "draw_data_2d(irisdf, 1, 3, y, y_names, plt.subplot(4, 2, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Неиерархическая четка кластеризация (k-means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 1 (53):\n",
      "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 57, 93, 98\n",
      "\n",
      "--------\n",
      "Cluster 2 (97):\n",
      "50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149\n",
      "\n",
      "--------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5.00566038, 3.36981132, 1.56037736, 0.29056604],\n",
       "       [6.30103093, 2.88659794, 4.95876289, 1.69587629]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import print_cluster_result, run_kmeans\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "labels, centers = run_kmeans(irisdf, 2, random_state)\n",
    "print_cluster_result(irisdf, 2, labels)\n",
    "display(centers)\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация результатов кластеризации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.visual import draw_cluster_results\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "draw_cluster_results(irisdf, 0, 1, labels, centers, plt.subplot(2, 2, 1))\n",
    "draw_cluster_results(irisdf, 2, 3, labels, centers, plt.subplot(2, 2, 2))\n",
    "draw_cluster_results(irisdf, 0, 2, labels, centers, plt.subplot(2, 2, 3))\n",
    "draw_cluster_results(irisdf, 1, 3, labels, centers, plt.subplot(2, 2, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Разбиение на 3 кластера и сравнение с реальным разбиением"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels, centers = run_kmeans(irisdf, 3, random_state)\n",
    "labels = [2 if val == 1 else 1 if val == 2 else val for val in labels]\n",
    "\n",
    "plt.figure(figsize=(16, 24))\n",
    "draw_data_2d(irisdf, 0, 1, labels, y_names, plt.subplot(4, 2, 1))\n",
    "draw_data_2d(irisdf, 0, 1, y, y_names, plt.subplot(4, 2, 2))\n",
    "draw_data_2d(irisdf, 2, 3, labels, y_names, plt.subplot(4, 2, 3))\n",
    "draw_data_2d(irisdf, 2, 3, y, y_names, plt.subplot(4, 2, 4))\n",
    "draw_data_2d(irisdf, 0, 2, labels, y_names, plt.subplot(4, 2, 5))\n",
    "draw_data_2d(irisdf, 0, 2, y, y_names, plt.subplot(4, 2, 6))\n",
    "draw_data_2d(irisdf, 1, 3, labels, y_names, plt.subplot(4, 2, 7))\n",
    "draw_data_2d(irisdf, 1, 3, y, y_names, plt.subplot(4, 2, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Понижение размерности до n=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.68412563,  0.31939725],\n",
       "       [-2.71414169, -0.17700123],\n",
       "       [-2.88899057, -0.14494943],\n",
       "       [-2.74534286, -0.31829898],\n",
       "       [-2.72871654,  0.32675451],\n",
       "       [-2.28085963,  0.74133045],\n",
       "       [-2.82053775, -0.08946138],\n",
       "       [-2.62614497,  0.16338496],\n",
       "       [-2.88638273, -0.57831175],\n",
       "       [-2.6727558 , -0.11377425],\n",
       "       [-2.50694709,  0.6450689 ],\n",
       "       [-2.61275523,  0.01472994],\n",
       "       [-2.78610927, -0.235112  ],\n",
       "       [-3.22380374, -0.51139459],\n",
       "       [-2.64475039,  1.17876464],\n",
       "       [-2.38603903,  1.33806233],\n",
       "       [-2.62352788,  0.81067951],\n",
       "       [-2.64829671,  0.31184914],\n",
       "       [-2.19982032,  0.87283904],\n",
       "       [-2.5879864 ,  0.51356031],\n",
       "       [-2.31025622,  0.39134594],\n",
       "       [-2.54370523,  0.43299606],\n",
       "       [-3.21593942,  0.13346807],\n",
       "       [-2.30273318,  0.09870885],\n",
       "       [-2.35575405, -0.03728186],\n",
       "       [-2.50666891, -0.14601688],\n",
       "       [-2.46882007,  0.13095149],\n",
       "       [-2.56231991,  0.36771886],\n",
       "       [-2.63953472,  0.31203998],\n",
       "       [-2.63198939, -0.19696122],\n",
       "       [-2.58739848, -0.20431849],\n",
       "       [-2.4099325 ,  0.41092426],\n",
       "       [-2.64886233,  0.81336382],\n",
       "       [-2.59873675,  1.09314576],\n",
       "       [-2.63692688, -0.12132235],\n",
       "       [-2.86624165,  0.06936447],\n",
       "       [-2.62523805,  0.59937002],\n",
       "       [-2.80068412,  0.26864374],\n",
       "       [-2.98050204, -0.48795834],\n",
       "       [-2.59000631,  0.22904384],\n",
       "       [-2.77010243,  0.26352753],\n",
       "       [-2.84936871, -0.94096057],\n",
       "       [-2.99740655, -0.34192606],\n",
       "       [-2.40561449,  0.18887143],\n",
       "       [-2.20948924,  0.43666314],\n",
       "       [-2.71445143, -0.2502082 ],\n",
       "       [-2.53814826,  0.50377114],\n",
       "       [-2.83946217, -0.22794557],\n",
       "       [-2.54308575,  0.57941002],\n",
       "       [-2.70335978,  0.10770608],\n",
       "       [ 1.28482569,  0.68516047],\n",
       "       [ 0.93248853,  0.31833364],\n",
       "       [ 1.46430232,  0.50426282],\n",
       "       [ 0.18331772, -0.82795901],\n",
       "       [ 1.08810326,  0.07459068],\n",
       "       [ 0.64166908, -0.41824687],\n",
       "       [ 1.09506066,  0.28346827],\n",
       "       [-0.74912267, -1.00489096],\n",
       "       [ 1.04413183,  0.2283619 ],\n",
       "       [-0.0087454 , -0.72308191],\n",
       "       [-0.50784088, -1.26597119],\n",
       "       [ 0.51169856, -0.10398124],\n",
       "       [ 0.26497651, -0.55003646],\n",
       "       [ 0.98493451, -0.12481785],\n",
       "       [-0.17392537, -0.25485421],\n",
       "       [ 0.92786078,  0.46717949],\n",
       "       [ 0.66028376, -0.35296967],\n",
       "       [ 0.23610499, -0.33361077],\n",
       "       [ 0.94473373, -0.54314555],\n",
       "       [ 0.04522698, -0.58383438],\n",
       "       [ 1.11628318, -0.08461685],\n",
       "       [ 0.35788842, -0.06892503],\n",
       "       [ 1.29818388, -0.32778731],\n",
       "       [ 0.92172892, -0.18273779],\n",
       "       [ 0.71485333,  0.14905594],\n",
       "       [ 0.90017437,  0.32850447],\n",
       "       [ 1.33202444,  0.24444088],\n",
       "       [ 1.55780216,  0.26749545],\n",
       "       [ 0.81329065, -0.1633503 ],\n",
       "       [-0.30558378, -0.36826219],\n",
       "       [-0.06812649, -0.70517213],\n",
       "       [-0.18962247, -0.68028676],\n",
       "       [ 0.13642871, -0.31403244],\n",
       "       [ 1.38002644, -0.42095429],\n",
       "       [ 0.58800644, -0.48428742],\n",
       "       [ 0.80685831,  0.19418231],\n",
       "       [ 1.22069088,  0.40761959],\n",
       "       [ 0.81509524, -0.37203706],\n",
       "       [ 0.24595768, -0.2685244 ],\n",
       "       [ 0.16641322, -0.68192672],\n",
       "       [ 0.46480029, -0.67071154],\n",
       "       [ 0.8908152 , -0.03446444],\n",
       "       [ 0.23054802, -0.40438585],\n",
       "       [-0.70453176, -1.01224823],\n",
       "       [ 0.35698149, -0.50491009],\n",
       "       [ 0.33193448, -0.21265468],\n",
       "       [ 0.37621565, -0.29321893],\n",
       "       [ 0.64257601,  0.01773819],\n",
       "       [-0.90646986, -0.75609337],\n",
       "       [ 0.29900084, -0.34889781],\n",
       "       [ 2.53119273, -0.00984911],\n",
       "       [ 1.41523588, -0.57491635],\n",
       "       [ 2.61667602,  0.34390315],\n",
       "       [ 1.97153105, -0.1797279 ],\n",
       "       [ 2.35000592, -0.04026095],\n",
       "       [ 3.39703874,  0.55083667],\n",
       "       [ 0.52123224, -1.19275873],\n",
       "       [ 2.93258707,  0.3555    ],\n",
       "       [ 2.32122882, -0.2438315 ],\n",
       "       [ 2.91675097,  0.78279195],\n",
       "       [ 1.66177415,  0.24222841],\n",
       "       [ 1.80340195, -0.21563762],\n",
       "       [ 2.1655918 ,  0.21627559],\n",
       "       [ 1.34616358, -0.77681835],\n",
       "       [ 1.58592822, -0.53964071],\n",
       "       [ 1.90445637,  0.11925069],\n",
       "       [ 1.94968906,  0.04194326],\n",
       "       [ 3.48705536,  1.17573933],\n",
       "       [ 3.79564542,  0.25732297],\n",
       "       [ 1.30079171, -0.76114964],\n",
       "       [ 2.42781791,  0.37819601],\n",
       "       [ 1.19900111, -0.60609153],\n",
       "       [ 3.49992004,  0.4606741 ],\n",
       "       [ 1.38876613, -0.20439933],\n",
       "       [ 2.2754305 ,  0.33499061],\n",
       "       [ 2.61409047,  0.56090136],\n",
       "       [ 1.25850816, -0.17970479],\n",
       "       [ 1.29113206, -0.11666865],\n",
       "       [ 2.12360872, -0.20972948],\n",
       "       [ 2.38800302,  0.4646398 ],\n",
       "       [ 2.84167278,  0.37526917],\n",
       "       [ 3.23067366,  1.37416509],\n",
       "       [ 2.15943764, -0.21727758],\n",
       "       [ 1.44416124, -0.14341341],\n",
       "       [ 1.78129481, -0.49990168],\n",
       "       [ 3.07649993,  0.68808568],\n",
       "       [ 2.14424331,  0.1400642 ],\n",
       "       [ 1.90509815,  0.04930053],\n",
       "       [ 1.16932634, -0.16499026],\n",
       "       [ 2.10761114,  0.37228787],\n",
       "       [ 2.31415471,  0.18365128],\n",
       "       [ 1.9222678 ,  0.40920347],\n",
       "       [ 1.41523588, -0.57491635],\n",
       "       [ 2.56301338,  0.2778626 ],\n",
       "       [ 2.41874618,  0.3047982 ],\n",
       "       [ 1.94410979,  0.1875323 ],\n",
       "       [ 1.52716661, -0.37531698],\n",
       "       [ 1.76434572,  0.07885885],\n",
       "       [ 1.90094161,  0.11662796],\n",
       "       [ 1.39018886, -0.28266094]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(irisdf)\n",
    "reduced_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация данных после понижения размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "draw_data_2d(\n",
    "    pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
    "    0,\n",
    "    1,\n",
    "    subplot=plt.subplot(1, 2, 1),\n",
    ")\n",
    "draw_data_2d(\n",
    "    pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
    "    0,\n",
    "    1,\n",
    "    y,\n",
    "    y_names,\n",
    "    plt.subplot(1, 2, 2),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация результатов неиерархической кластеризации для двух кластеров с учетом понижения размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import fit_kmeans\n",
    "from src.visual import draw_clusters\n",
    "\n",
    "\n",
    "kmeans = fit_kmeans(reduced_data, 2, random_state)\n",
    "draw_clusters(reduced_data, kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация результатов неиерархической кластеризации для трех кластеров с учетом понижения размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = fit_kmeans(reduced_data, 3, random_state)\n",
    "draw_clusters(reduced_data, kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Сравнение результатов кластеризации с реальным разбиением с учетом понижения размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [2 if val == 1 else 1 if val == 2 else val for val in kmeans.labels_]\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "draw_data_2d(\n",
    "    pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
    "    0,\n",
    "    1,\n",
    "    labels,\n",
    "    y_names,\n",
    "    plt.subplot(2, 2, 1),\n",
    ")\n",
    "draw_data_2d(\n",
    "    pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
    "    0,\n",
    "    1,\n",
    "    result,\n",
    "    y_names,\n",
    "    plt.subplot(2, 2, 2),\n",
    ")\n",
    "draw_data_2d(\n",
    "    pd.DataFrame({\"Column1\": reduced_data[:, 0], \"Column2\": reduced_data[:, 1]}),\n",
    "    0,\n",
    "    1,\n",
    "    y,\n",
    "    y_names,\n",
    "    plt.subplot(2, 2, 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Выбор количества кластеров на основе инерции\n",
    "\n",
    "Инерция -- сумма квадратов расстояний выборок до ближайшего центра кластера, взвешенная по весам выборок, если таковые имеются."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(2, 13)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[152.3479517603579,\n",
       " 78.85144142614601,\n",
       " 57.25600931571815,\n",
       " 49.84981451052335,\n",
       " 39.03998724608725,\n",
       " 35.73675177600178,\n",
       " 30.186555194805198,\n",
       " 28.74142653983444,\n",
       " 26.572328111946536,\n",
       " 25.281078256538784,\n",
       " 24.845747490899196]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import get_clusters_inertia\n",
    "from src.visual import draw_elbow_diagram\n",
    "\n",
    "\n",
    "inertias, clusters_range = get_clusters_inertia(irisdf, random_state)\n",
    "display(clusters_range)\n",
    "display(inertias)\n",
    "draw_elbow_diagram(inertias, clusters_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Выбор количества кластеров на основе коэффициента силуэта\n",
    "\n",
    "Коэффициент силуэта рассчитывается с использованием среднего расстояния внутри кластера (а) и среднего расстояния до ближайшего кластера (b) для каждого образца. Коэффициент силуэта для образца равен (b - a) / max(a, b). Для пояснения: b — это расстояние между образцом и ближайшим кластером, частью которого образец не является. Обратите внимание, что коэффициент силуэта определяется только в том случае, если количество меток равно 2 <= n_labels <= n_samples - 1.\n",
    "\n",
    "Эта функция возвращает средний коэффициент силуэта по всем образцам. \n",
    "\n",
    "Лучшее значение — 1, худшее — -1. Значения около 0 указывают на перекрывающиеся кластеры. Отрицательные значения обычно указывают на то, что образец был отнесен к неправильному кластеру."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(2, 13)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[0.6810461692117462,\n",
       " 0.5528190123564095,\n",
       " 0.4974551890173751,\n",
       " 0.37267902612312465,\n",
       " 0.36483400396700255,\n",
       " 0.3177403273169618,\n",
       " 0.35745369258527154,\n",
       " 0.3357877717812255,\n",
       " 0.31153956064328736,\n",
       " 0.2897041029666296,\n",
       " 0.28314769898585324]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import get_clusters_silhouette_scores\n",
    "from src.visual import draw_silhouettes_diagram\n",
    "\n",
    "silhouette_scores, clusters_range = get_clusters_silhouette_scores(irisdf, random_state)\n",
    "display(clusters_range)\n",
    "display(silhouette_scores)\n",
    "draw_silhouettes_diagram(silhouette_scores, clusters_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример анализа силуэтов для разбиения от 2 до 12 кластеров\n",
    "\n",
    "max_clusters = int(math.sqrt(len(df)))\n",
    "\n",
    "https://scikit-learn.org/1.5/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZoAAAKgCAYAAAAS1si3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/GU6VOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1xT1/8/8FdCQtgblCUgLnChuBc4Ko6666yKqNXWuq2f1g5Xq7baWq1W7fhUq/bTOlq11lX31rr3rIiKC1FBNknO7w9/3C+BAEHGZbyePniY3HXeuSvnvnPuuQohhAARERERERERERER0StSyh0AEREREREREREREZVuTDQTERERERERERERUYEw0UxEREREREREREREBcJEMxEREREREREREREVCBPNRERERERERERERFQgTDQTERERERERERERUYEw0UxEREREREREREREBcJEMxEREREREREREREVCBPNRERERERERERERFQgTDQTUYmzYsUKKBQK6S+z0NBQafiQIUOk4bdv3zaYZ9++fcUbNJVI06dPl/YJX1/fYi17yJAhUtmhoaFFVs7Dhw8REREBT09PqFQqqcyNGzcWWZlU+uV0Li0vcvueofKrpOwXJSWOkqCg66Ksr0s56zlERETGMNFMREXut99+Q1hYGCpUqAC1Wg17e3v4+fkhNDQU48aNw44dO+QOsUQzNYle3hNH5ZEQAm+88QZWrFiB+/fvQ6fTyR0SAGDfvn0G++yKFSsMxj979gyNGjWSxiuVSixdutTovAqFAl27djVazo4dO7JNy32/dCjryR+ikiTrefX27dtyh1QoeB6RHxt6mObcuXOYM2cOOnbsiOrVq8Pe3h4ajQbe3t7o06cPDhw4IHeIRESFRiV3AERUtg0ePBirVq0yGBYfH4/4+Hjcvn0b+/fvR1RUFMLCwqTxDRs2xLx584o7VKJC1a9fP9SqVQsA4O3tXSRl3LlzB4cPH5bev/7662jZsiWUSqVUdkkTExOD1157DefOnQMAKJVK/Pjjj4iIiMhxni1btuDWrVuoXLmywfCFCxcWaaxUdvF7hqh04LFKZcH7779vtGHNvXv3sG7dOqxbtw5ffPEF/vOf/8gQHRFR4WKimYiKzPbt2w2SzMHBwQgLC4ONjQ1iYmJw+vRpHD16NNt8NWvWRM2aNYszVKJC16FDB3To0KFIy4iKijJ4v2DBAvj7+xdpmWlpaRBCQKPR5Hvehw8fom3btrh8+TIAwMzMDCtXrsSAAQNynU+v12Px4sWYP3++NOz69evYvn17vmOg8i0+Ph52dnb8niEqJXiskjEFqYvIqWbNmmjXrh2cnZ1x4sQJbN68WRr34Ycfolu3bqhevbqMERIRFRy7ziCiIvP3339Lr6tUqYLjx49j1qxZmDJlCubPn499+/YhJiYGkydPNpivsG6F3LBhA5o2bQorKys4Ojqid+/euHv3rtFpT506hcGDB8PPzw8WFhawsbFBrVq1MGnSJNy7dy/b9L6+vlJ806dPNxiXV3958fHxmDNnDho3bgx7e3uYm5ujUqVKGDJkCC5dupStHD8/P4NhrVu3Nuj7N6O8/fv3S9P8/PPPOd4mm5qaisWLF6NVq1ZwcnKCubk53N3d0bt3b6OJ/9ycPXsWo0aNQuPGjeHp6QlLS0tYWFjAx8cHffv2xaFDh7LNk3X9xMXFYfLkyfDx8YG5uTkqV66M2bNnQwhR4LKMiY+Ph62trRTD999/n22a3r17S+M7duwoDT948CB69OgBT09PmJubw8bGBr6+vujYsSOmT5+OuLg4adrc+mi+cOECBg4cCF9fX2g0GlhaWqJSpUpo06YNpkyZgujo6Dw/h0KhQEhIiMGwKlWqGD1u8rt/Z+2G5eLFi+jevTucnZ2h0Whw5cqVPOPL6t69ewgJCZGSzGq1GmvWrMkzyaxUvqyq/PTTT0hMTJSGL1q0SNpHzMzMcl3Go0eP8OGHHyIoKAi2trawsLBAlSpV8O677+LOnTvZpi/O/ToxMREzZ85E/fr1YWtrC7VaDTc3NwQFBeGtt97KdzL9xIkTiIiIQJUqVWBlZQUbGxtUq1YNERER+Pfff/OcP69b7HM79/3555/o0KGD1E2SnZ0d/P390b17d8yZMwd6vV66zTprC/bMZWZd7sGDB9GvXz9UqlQJGo0GdnZ2aNq0Kb799lukp6dn+wxZu23ZtGkTmjVrBhsbG1SqVAlA/p4FcOPGDfTv3x8uLi6wsLBA/fr1sWnTJqPr7+DBgwgNDYW1tTWcnJzQp08fREZGFqjP9oIev/mJPyf5OfcVxvHz4MEDhIeHw8XFBXZ2dujSpQuuX78OADh9+jQ6dOgAW1vbHL/bs+7Ht27dwoIFCxAYGAgLCwt4enpi4sSJePHiRb7WQ2F+fwIvfyzs378/nJycYG1tjVatWmHXrl25zrNv3z4MGzYM9evXh7u7OzQaDaysrFClShVERETgwoULBtMrFAq0bt3aYJifn1+27oa0Wi0++eQTdOrUCf7+/nBwcIBarYazszNatmyJRYsWGT3ejDl37pzB+s98nv3www+l4RMnTpSGP3r0yGCe48ePAzB+rL7KeSRDeno65s6dixo1akCj0cDLywvvvfceUlNTTfpsQNEcY1ndu3cP77//PurVqwc7OztYWFigUqVK6N69O3bu3GnSMnLrwqow6iim1FEzu3XrFsaOHYuAgABYW1vD0tISgYGB+OCDD/DkyZNs8ZtaF8nP+UkuzZs3x4EDB3Dx4kUsWLAAn3zyCf78809MnTpVmkan0xlcOxERlVqCiKiIjBkzRgAQAISLi4u4efOmSfMtX75cmi/raSokJEQaHh4eLg2PjIw0mCcsLMzgfcZf1apVRXJyssEyv/76a6FUKo1OD0DY29uLvXv3Gszj4+MjjZ82bZrBuGnTpknjfHx8DMZdv35d+Pr65liWRqMRa9euNVqOsb+QkBCD8nL6i4yMFEII8fjxYxEUFJTjdEqlUixYsMCk7SSEEIsWLcq1XIVCIZYvX57j+nF2dhYBAQFG5/3kk08KtazM2+Ldd9+Vhjds2NBgnoSEBGFlZSWNz9geu3btEmZmZrnGcOXKFWk54eHhBtspw6VLlwyWb+xv27Ztea77vLZ5hlfZvzMfZ/Xq1RPW1tYG85w5cybX2Pbu3Wsw/bRp04Sfn5/Bfv7nn3+aNG/37t2l199++60QQoi4uDhha2srxZf5OMl8XhBCiCNHjggXF5dcP/+BAwcM5inO/To0NDTXsvr27Zvrus5sxowZQqFQ5LisDRs2SNPmdC7Nuv4zzh0Zcjr3ZT1vG/tLTk7Odq429pd5uR9++GGu07Zs2VIkJCQYxJh1fNbtbSzezDKvmzp16kj7WtZ9YNeuXQbzbd68WahUqmzTOjs7i2bNmhk9H+SloMdvfuLPSX7PfQU9fpycnIx+T7q6uooNGzYIjUaTbVzW7/as+3GbNm2MxtKwYUOD+XLbLwr7+zMyMlJUrFjR6Prp1KlTjnFMmjQp1/Vrbm4udu7cKU2f1/GWcfy/ePEiz2nbtWsntFptnp9Nr9cLZ2dnab5ffvlFGteiRQtpeIMGDaTh69atk4bb2dlJ5RjbJvk5j2SdP6f64aBBg0zedoV9jGW1ZcsWo8vM+Bs3bpw0bW51zszzZD3mCqOOYkodNcPGjRtzXa6np6e4fPlyjus5p7pIfs9PuTHleyzzX9b1/SouXLhgsMwvv/yywMskIpIbu84goiJTv3596fWTJ09QrVo1BAUFoWHDhggODkbr1q1RpUqVIil7x44daNiwIcLCwrB3716pH9sbN25g48aN6NevHwDgwIEDmDhxotTKsFKlSujfvz8SEhKwfPlyJCUlIS4uDr169cLNmzfh6Oj4yjHpdDr06NFDaiHo6uqKAQMGwMnJCTt27MCRI0eQmpqKwYMHIzg4GJUrV8ZHH32E27dvY/bs2dJy3n77bal7BG9vb3h7e8PGxgZLly7FrVu3AAANGjRA3759pXmcnJwAAIMGDcLZs2cBALa2thgwYAC8vLxw+PBhbN++HXq9HhM
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZoAAAKgCAYAAAAS1si3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/GU6VOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVdsG8HtmWzab3iAJEEJCDSVUpQYEBESQIlVaEEFBEUReu4AFPsECgoBYQAGVIqCIgHSQJr1KkwDSOyG97Pn+2Oy4LcmGlE3I/cu1V2anPjs7Mznz5Mw5khBCgIiIiIiIiIiIiIjoAcmuDoCIiIiIiIiIiIiISjYmmomIiIiIiIiIiIgoX5hoJiIiIiIiIiIiIqJ8YaKZiIiIiIiIiIiIiPKFiWYiIiIiIiIiIiIiyhcmmomIiIiIiIiIiIgoX5hoJiIiIiIiIiIiIqJ8YaKZiIiIiIiIiIiIiPKFiWYiIiIiIiIiIiIiyhcmmomo2Jk3bx4kSVJellq2bKmMHzRokDL+3LlzVsts3ry5aIOmYmn8+PHKMVGxYsUi3fagQYOUbbds2bLQtnP16lXExsYiNDQUarVa2eaKFSsKbZtU8mV3LS0tcvo7Q6VXcTkuikscxUF+98XDvi9dWc4hIiJyhIlmIip0P/30E9q1a4cyZcpAo9HA29sb4eHhaNmyJV5++WWsXbvW1SEWa84m0Ut74qg0EkLg6aefxrx583D58mVkZma6OiQAwObNm62O2Xnz5llNv3PnDho1aqRMl2UZs2bNcrisJEno3Lmzw+2sXbvWbl4e+yXDw578ISpObK+r586dc3VIBYLXEddjRQ/nJSYmYtKkSWjQoAE8PT3h5uaGyMhIjBgxAv/++6+rwyMiKjBqVwdARA+3AQMGYP78+Vbj4uPjER8fj3PnzmHLli04f/482rVrp0xv2LAhpkyZUtShEhWo3r17o2bNmgCA8uXLF8o2Lly4gO3btyvvn3zySTRv3hyyLCvbLm5u3LiBtm3b4tChQwAAWZbx9ddfIzY2NttlVq1ahbNnz6JSpUpW46dNm1aosdLDi39niEoGnqv0MLh8+TJatWqFU6dOWY3/559/MHPmTCxcuBCrVq1C06ZNXRQhEVHBYaKZiArNmjVrrJLM9evXR7t27eDh4YEbN25g//792Llzp91yUVFRiIqKKspQiQpc+/bt0b59+0Ldxvnz563eT506FREREYW6zbS0NAghoNPp8rzs1atX0bp1axw/fhwAoFKp8P3336Nv3745Lmc0GjFjxgx8+umnyrhTp05hzZo1eY6BSrf4+Hh4eXnx7wxRCcFzlRzJT1nEFYYMGaIkmTUaDWJjY1G2bFksWrQIJ0+exL1799CzZ08cP34c3t7eLo6WiCh/2HQGERWaP/74QxmOjIzE7t278eGHH+KNN97Ap59+is2bN+PGjRsYO3as1XIF9Sjk8uXL0bhxY7i7u8PX1xc9evTI9tG0ffv2YcCAAQgPD4ebmxs8PDxQs2ZNjBkzBhcvXrSbv2LFikp848ePt5qWW3t58fHxmDRpEh555BF4e3tDq9WiQoUKGDRoEI4dO2a3nfDwcKtxrVq1smr717y9LVu2KPN899132T4mm5qaihkzZqBFixbw8/ODVqtFcHAwevTo4TDxn5ODBw9i+PDheOSRRxAaGgq9Xg83NzeEhYWhV69e+PPPP+2Wsd0/9+7dw9ixYxEWFgatVotKlSph4sSJEELke1uOxMfHw9PTU4lhzpw5dvP06NFDmd6hQwdl/LZt29C1a1eEhoZCq9XCw8MDFStWRIcOHTB+/Hjcu3dPmTenNpqPHDmCfv36oWLFitDpdNDr9ahQoQIee+wxvPHGG7h06VKun0OSJMTExFiNi4yMdHje5PX4tm2G5ejRo+jSpQv8/f2h0+nw999/5xqfrYsXLyImJkZJMms0GixatCjXJLMsm4oq3377LRITE5Xx06dPV44RlUqV4zquXbuGN998E9HR0XaPq164cMFu/qI8rhMTE/Hee++hXr168PT0hEajQVBQEKKjo/Hcc8/lOZm+Z88exMbGIjIyEu7u7vDw8ECVKlUQGxuLf/75J9flc3vEPqdr36+//or27dsrzSR5eXkhIiICXbp0waRJk2A0GpXHrG1rsFtu03a927ZtQ+/evVGhQgXodDp4eXmhcePG+OKLL5Cenm73GWybbfnll1/QpEkTeHh4oEKFCgDy1hfA6dOn0adPHwQEBMDNzQ316tXDL7/84nD/bdu2DS1btoTBYICfnx969uyJuLi4fLXZnt/zNy/xZycv176COH+uXLmCgQMHIiAgAF5eXujUqZOSpNm/fz/at28PT0/PbP+22x7HZ8+exdSpU1GjRg24ubkhNDQUr7zyCu7fv5+n/VCQfz8B0z8L+/TpAz8/PxgMBrRo0QLr16/PcZnNmzfj2WefRb169RAcHAydTgd3d3dERkYiNjYWR44csZpfkiS0atXKalx4eLhdc0MZGRl455138MQTTyAiIgI+Pj7QaDTw9/dH8+bNMX36dIfnmyOHDh2y2v+W19k333xTGf/KK68o469du2a1zO7duwE4Plcf5Dpilp6ejsmTJ6NatWrQ6XQoV64cXn31VaSmpjr12YDCOcdsXbx4Ea+99hrq1q0LLy8vuLm5oUKFCujSpQvWrVvn1DpyasKqIMoozpRRLZ09exYjR45E9erVYTAYoNfrUaNGDbz++uu4efOmXfzOlkXycn1yhatXr1r9LX/ttdfw5ZdfYsKECdi4caNSzrl8+TK+/fZbV4VJRFRwBBFRIXnppZcEAAFABAQEiDNnzji13Ny5c5XlbC9TMTExyviBAwcq4+Pi4qyWadeundV786ty5coiOTnZap2fffaZkGXZ4fwAhLe3t9i0aZPVMmFhYcr0cePGWU0bN26cMi0sLMxq2qlTp0TFihWz3ZZOpxOLFy92uB1Hr5iYGKvtZfeKi4sTQghx/fp1ER0dne18siyLqVOnOvU9CSHE9OnTc9yuJEli7ty52e4ff39/Ub16dYfLvvPOOwW6LcvvYsSIEcr4hg0bWi2TkJAg3N3dlenm72P9+vVCpVLlGMPff/+trGfgwIFW35PZsWPHrNbv6LV69epc931u37nZgxzfludZ3bp1hcFgsFrmwIEDOca2adMmq/nHjRsnwsPDrY7zX3/91allu3Tpogx/8cUXQggh7t27Jzw9PZX4LM8Ty+uCEELs2LFDBAQE5Pj5t27darVMUR7XLVu2zHFbvXr1ynFfW5owYYKQJCnbdS1fvlyZN7trqe3+N187zLK79tletx29kpOT7a7Vjl6W633zzTdznLd58+YiISHBKkbb6bbft6N4LVnum9q1ayvHmu0xsH79eqvlVq5cKdRqtd28/v7+okmTJg6vB7nJ7/mbl/izk9drX37PHz8/P4d/JwMDA8Xy5cuFTqezm2b7t932OH7sscccxtKwYUOr5XI6Lgr672dcXJwoW7asw/3zxBNPZBvHmDFjcty/Wq1WrFu3Tpk/t/PNfP7fv38/13nbtGkjMjIycv1sRqNR+Pv7K8stXLhQmdasWTNlfIMGDZTxS5YsUcZ7eXkp23H0neTlOmK7fHblw/79+zv93RX0OWZr1apVDtdpfr388svKvDmVOS2XsT3nCqKM4kwZ1WzFihU5rjc0NFQcP3482/2cXVkkr9ennDjzd8zyZbu/s7Njxw6r5ZYsWWI1PSgoSJnWunVrp9ZJRFScsekMIio09erVU4Zv3ryJKlWqIDo6Gg0bNkT9+vXRqlUrREZGFsq2165di4YNG6Jdu3bYtGmT0o7t6dOnsWLFCvTu3RsAsHXrVrzyyitKLcMKFSqgT58+SEhIwNy5c5GUlIR79+6he/fuOHPmDHx9fR84pszMTHTt2lWpIRgYGIi+ffvCz88Pa9euxY4dO5CamooBAwagfv36qFSpEt566y2cO3cOEydOVNbz/PPPK80jlC9fHuXLl4eHhwdmzZqFs2fPAgAaNGiAXr16Kcv4+fkBAPr374+DBw8CADw9PdG3b1+UK1cO27d
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.clusters import get_clusters_silhouettes\n",
    "from src.visual import draw_silhouettes\n",
    "\n",
    "\n",
    "silhouettes = get_clusters_silhouettes(reduced_data, random_state)\n",
    "draw_silhouettes(reduced_data, silhouettes)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}