KNN-Exercise.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Авторские права принадлежат Pierian Data Inc.</em></center>\n",
    "<center><em>Для дополнительной информации посетите наш сайт <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Упражнения по методу KNN\n",
    "\n",
    "Метод KNN для классификации достаточно простой, поэтому давайте применим наши знания для создания пайплайна и применения GridSearchCV - их можно будет использовать и для других моделей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Сонарные данные\n",
    "\n",
    "### Определение камня или мины\n",
    "\n",
    "Сонар (ультразвуковой локатор) - это техника использования звука для навигации, коммуникации с объектами и определения объектов на воде или под водой, например морских судов.\n",
    "\n",
    "<img src=\"sonar.jpg\"  style=\"max-height: 500px; max-width: 500px;\">\n",
    "\n",
    "Данный набор данных содержит ответные метрики для 60 различных частот сонара, отправленных на известные объекты - мины и камни. Для каждого отправленного и полученного сигнала были записаны данные об объекте, на который направлялся сонар (мина или камень). \n",
    "\n",
    "<img src=\"mine.jpg\"  style=\"max-height: 500px; max-width: 500px;\">\n",
    "\n",
    "Наша цель - создать модель машинного обучения, которая может определить тип объекта - мина или камень - на основе ответа от сонаров на 60 различных частотах.\n",
    "\n",
    "\n",
    "Источник данных: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)\n",
    "\n",
    "### Выполните задания, написанные жирным шрифтом\n",
    "\n",
    "**ЗАДАНИЕ: Выполните ячейки ниже, чтобы загрузить данные.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/sonar.all-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freq_1</th>\n",
       "      <th>Freq_2</th>\n",
       "      <th>Freq_3</th>\n",
       "      <th>Freq_4</th>\n",
       "      <th>Freq_5</th>\n",
       "      <th>Freq_6</th>\n",
       "      <th>Freq_7</th>\n",
       "      <th>Freq_8</th>\n",
       "      <th>Freq_9</th>\n",
       "      <th>Freq_10</th>\n",
       "      <th>...</th>\n",
       "      <th>Freq_52</th>\n",
       "      <th>Freq_53</th>\n",
       "      <th>Freq_54</th>\n",
       "      <th>Freq_55</th>\n",
       "      <th>Freq_56</th>\n",
       "      <th>Freq_57</th>\n",
       "      <th>Freq_58</th>\n",
       "      <th>Freq_59</th>\n",
       "      <th>Freq_60</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
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       "   Freq_1  Freq_2  Freq_3  Freq_4  Freq_5  Freq_6  Freq_7  Freq_8  Freq_9  \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "   Freq_10  ...  Freq_52  Freq_53  Freq_54  Freq_55  Freq_56  Freq_57  \\\n",
       "0   0.2111  ...   0.0027   0.0065   0.0159   0.0072   0.0167   0.0180   \n",
       "1   0.2872  ...   0.0084   0.0089   0.0048   0.0094   0.0191   0.0140   \n",
       "2   0.6194  ...   0.0232   0.0166   0.0095   0.0180   0.0244   0.0316   \n",
       "3   0.1264  ...   0.0121   0.0036   0.0150   0.0085   0.0073   0.0050   \n",
       "4   0.4459  ...   0.0031   0.0054   0.0105   0.0110   0.0015   0.0072   \n",
       "\n",
       "   Freq_58  Freq_59  Freq_60  Label  \n",
       "0   0.0084   0.0090   0.0032      R  \n",
       "1   0.0049   0.0052   0.0044      R  \n",
       "2   0.0164   0.0095   0.0078      R  \n",
       "3   0.0044   0.0040   0.0117      R  \n",
       "4   0.0048   0.0107   0.0094      R  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исследование данных\n",
    "\n",
    "**ЗАДАНИЕ: Нарисуйте тепловую карту heatmap, показывающую корреляцию между откликами на различных частотах.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_11020\\1121667969.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Напишите Ваш код здесь\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Какие 5 частот лучше всего коррелируют с целевой переменной?**\n",
    "\n",
    "*Замечание: Вы можете пометить целевую переменную с помощью значений 0 и 1.*\n",
    "\n",
    "*Замечание 2: мы ищем **абсолютные** значения корреляции.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_11020\\4241013607.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  np.abs(df.corr()['Target']).sort_values().tail(6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Freq_45    0.339406\n",
       "Freq_10    0.341142\n",
       "Freq_49    0.351312\n",
       "Freq_12    0.392245\n",
       "Freq_11    0.432855\n",
       "Target     1.000000\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Напишите Ваш код здесь\n",
    "df['Target'] = df['Label'].map({\"R\": 0, \"M\": 1})\n",
    "np.abs(df.corr()['Target']).sort_values().tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разбиение на обучающий и тестовый наборы данных\n",
    "\n",
    "Здесь мы будем использовать кросс-валидацию на 90% данных, и затем оценим результаты работы нашей модели на тестовом наборе данных в 10%.\n",
    "\n",
    "**ЗАДАНИЕ: Разделите данные на признаки и целевую переменную, а затем разбейте все данные на обучающий и тестовый наборы данных, в отношении 90% и 10%.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишите Ваш код здесь\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Target', 'Label'], axis=1)\n",
    "y = df['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Создайте объект PipeLine, в котором будет StandardScaler и модель KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "operations = [('scaler', scaler), ('knn', knn)]\n",
    "pipe = Pipeline(operations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: С помощью пайплайна выполните поиск по сетке для различных значений k, и выведите наилучшие параметры.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = list(range(1, 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'knn__n_neighbors': k}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(pipe, param_grid=param_grid, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;knn__n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
       "                                              12, 13, 14, 15, 16, 17, 18, 19,\n",
       "                                              20, 21, 22, 23, 24, 25, 26, 27,\n",
       "                                              28, 29]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;knn__n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
       "                                              12, 13, 14, 15, 16, 17, 18, 19,\n",
       "                                              20, 21, 22, 23, 24, 25, 26, 27,\n",
       "                                              28, 29]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                       ('knn', KNeighborsClassifier())]),\n",
       "             param_grid={'knn__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
       "                                              12, 13, 14, 15, 16, 17, 18, 19,\n",
       "                                              20, 21, 22, 23, 24, 25, 26, 27,\n",
       "                                              28, 29]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('scaler', StandardScaler()),\n",
       "  ('knn', KNeighborsClassifier(n_neighbors=1))],\n",
       " 'verbose': False,\n",
       " 'scaler': StandardScaler(),\n",
       " 'knn': KNeighborsClassifier(n_neighbors=1),\n",
       " 'scaler__copy': True,\n",
       " 'scaler__with_mean': True,\n",
       " 'scaler__with_std': True,\n",
       " 'knn__algorithm': 'auto',\n",
       " 'knn__leaf_size': 30,\n",
       " 'knn__metric': 'minkowski',\n",
       " 'knn__metric_params': None,\n",
       " 'knn__n_jobs': None,\n",
       " 'knn__n_neighbors': 1,\n",
       " 'knn__p': 2,\n",
       " 'knn__weights': 'uniform'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(СЛОЖНОЕ) ЗАДАНИЕ: Используя словарь .cv_results_, попробуйте нарисовать график средних тестовых оценок (mean_test_score) для каждого значения K.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88250356, 0.82930299, 0.85049787, 0.76486486, 0.78634424,\n",
       "       0.75931721, 0.78122333, 0.73285917, 0.7487909 , 0.7116643 ,\n",
       "       0.70625889, 0.70640114, 0.72759602, 0.70640114, 0.73271693,\n",
       "       0.72745377, 0.72759602, 0.70611664, 0.70611664, 0.69004267,\n",
       "       0.69004267, 0.69530583, 0.72716927, 0.70056899, 0.72702703,\n",
       "       0.71123755, 0.71664296, 0.7059744 , 0.72731152])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Напишите Ваш код здесь\n",
    "grid.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k, grid.cv_results_['mean_test_score'], marker='o')\n",
    "plt.xlabel('k_neighbors')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Финальная оценка модели\n",
    "\n",
    "**ЗАДАНИЕ: используя объект grid classifier с предыдущего шага, создайте финальный отчёт \"classification report\" и матрицу \"confusion matrix\".**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишите Ваш код здесь\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 2],\n",
       "       [1, 9]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           M       0.90      0.82      0.86        11\n",
      "           R       0.82      0.90      0.86        10\n",
      "\n",
      "    accuracy                           0.86        21\n",
      "   macro avg       0.86      0.86      0.86        21\n",
      "weighted avg       0.86      0.86      0.86        21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Отличная работа!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}