k-medoids-algorithm-discover-the-menu-dataset.ipynb

{
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/discover-the-menu/menupage.csv\n",
      "/kaggle/input/discover-the-menu/dish.csv\n",
      "/kaggle/input/discover-the-menu/menu.csv\n",
      "/kaggle/input/discover-the-menu/menuitem.csv\n",
      "/kaggle/input/discover-the-menu/datapackage.json\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn-extra\r\n",
      "  Downloading scikit_learn_extra-0.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.10/site-packages (from scikit-learn-extra) (1.23.5)\r\n",
      "Requirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn-extra) (1.11.2)\r\n",
      "Requirement already satisfied: scikit-learn>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn-extra) (1.2.2)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (1.3.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=0.23.0->scikit-learn-extra) (3.1.0)\r\n",
      "Installing collected packages: scikit-learn-extra\r\n",
      "Successfully installed scikit-learn-extra-0.3.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn-extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7598884d",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn_extra.cluster import KMedoids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51a63a6d",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "dish = pd.read_csv('/kaggle/input/discover-the-menu/dish.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48a04a00",
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       "3              41            1900           1971          0.25            1.0  \n",
       "4              68            1881           1981          0.00           18.0  "
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    "dish.head()"
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       "      <td>519144</td>\n",
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       "            id                                               name  \\\n",
       "426735  519144                    Vanilla and chocolate ice cream   \n",
       "426736  519169  Mutton soup with Porto wine Cream soup Chicago...   \n",
       "426737  519170  Moussaka Oriental style Montana roebucks saddl...   \n",
       "426738  519171                                       Prima Classe   \n",
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       "\n",
       "        description  menus_appeared  times_appeared  first_appeared  \\\n",
       "426735          NaN               1               1               0   \n",
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       "           id   name  description  menus_appeared  times_appeared  \\\n",
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       "      <td>0.20</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1895</td>\n",
       "      <td>1960</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1893</td>\n",
       "      <td>1917</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900</td>\n",
       "      <td>1971</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1881</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426735</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426736</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426737</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426738</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426739</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>397639 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        first_appeared  last_appeared  lowest_price  highest_price\n",
       "0                 1897           1927          0.20            0.4\n",
       "1                 1895           1960          0.10            0.8\n",
       "2                 1893           1917          0.25            0.4\n",
       "3                 1900           1971          0.25            1.0\n",
       "4                 1881           1981          0.00           18.0\n",
       "...                ...            ...           ...            ...\n",
       "426735               0              0          0.00            0.0\n",
       "426736               0              0          0.00            0.0\n",
       "426737               0              0          0.00            0.0\n",
       "426738               0              0          0.00            0.0\n",
       "426739               0              0          0.00            0.0\n",
       "\n",
       "[397639 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dish.dropna(axis=0, inplace=True)\n",
    "dish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61cfceb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:45:51.172446Z",
     "iopub.status.busy": "2023-11-02T20:45:51.171218Z",
     "iopub.status.idle": "2023-11-02T20:45:51.184338Z",
     "shell.execute_reply": "2023-11-02T20:45:51.183004Z"
    },
    "papermill": {
     "duration": 0.028885,
     "end_time": "2023-11-02T20:45:51.187307",
     "exception": false,
     "start_time": "2023-11-02T20:45:51.158422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_appeared    0\n",
       "last_appeared     0\n",
       "lowest_price      0\n",
       "highest_price     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dish.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2caac1dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:45:51.211953Z",
     "iopub.status.busy": "2023-11-02T20:45:51.211491Z",
     "iopub.status.idle": "2023-11-02T20:45:51.217197Z",
     "shell.execute_reply": "2023-11-02T20:45:51.215957Z"
    },
    "papermill": {
     "duration": 0.021363,
     "end_time": "2023-11-02T20:45:51.219761",
     "exception": false,
     "start_time": "2023-11-02T20:45:51.198398",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dish = dish[:50000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b7e4b4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:45:51.244427Z",
     "iopub.status.busy": "2023-11-02T20:45:51.243101Z",
     "iopub.status.idle": "2023-11-02T20:45:51.261592Z",
     "shell.execute_reply": "2023-11-02T20:45:51.260417Z"
    },
    "papermill": {
     "duration": 0.033282,
     "end_time": "2023-11-02T20:45:51.264021",
     "exception": false,
     "start_time": "2023-11-02T20:45:51.230739",
     "status": "completed"
    },
    "tags": []
   },
   "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>first_appeared</th>\n",
       "      <th>last_appeared</th>\n",
       "      <th>lowest_price</th>\n",
       "      <th>highest_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1897</td>\n",
       "      <td>1927</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1895</td>\n",
       "      <td>1960</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1893</td>\n",
       "      <td>1917</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900</td>\n",
       "      <td>1971</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1881</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51569</th>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51570</th>\n",
       "      <td>1900</td>\n",
       "      <td>1958</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51572</th>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51573</th>\n",
       "      <td>1900</td>\n",
       "      <td>1938</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51574</th>\n",
       "      <td>1900</td>\n",
       "      <td>1900</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_appeared  last_appeared  lowest_price  highest_price\n",
       "0                1897           1927          0.20            0.4\n",
       "1                1895           1960          0.10            0.8\n",
       "2                1893           1917          0.25            0.4\n",
       "3                1900           1971          0.25            1.0\n",
       "4                1881           1981          0.00           18.0\n",
       "...               ...            ...           ...            ...\n",
       "51569            1900           1900          0.00            0.0\n",
       "51570            1900           1958          0.00            0.5\n",
       "51572            1900           1900          0.00            0.0\n",
       "51573            1900           1938          0.00            0.5\n",
       "51574            1900           1900          0.00            0.0\n",
       "\n",
       "[50000 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = dish[[\"first_appeared\", \"last_appeared\", \"lowest_price\", \"highest_price\"]]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba276439",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:45:51.288751Z",
     "iopub.status.busy": "2023-11-02T20:45:51.288298Z",
     "iopub.status.idle": "2023-11-02T20:45:51.294395Z",
     "shell.execute_reply": "2023-11-02T20:45:51.293185Z"
    },
    "papermill": {
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     "end_time": "2023-11-02T20:45:51.296974",
     "exception": false,
     "start_time": "2023-11-02T20:45:51.275074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kmedoids = KMedoids(n_clusters=25, metric='euclidean', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7876d016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:45:51.323792Z",
     "iopub.status.busy": "2023-11-02T20:45:51.322031Z",
     "iopub.status.idle": "2023-11-02T20:53:59.004839Z",
     "shell.execute_reply": "2023-11-02T20:53:59.003115Z"
    },
    "papermill": {
     "duration": 487.710054,
     "end_time": "2023-11-02T20:53:59.018648",
     "exception": false,
     "start_time": "2023-11-02T20:45:51.308594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 1 is empty! self.labels_[self.medoid_indices_[1]] may not be labeled with its corresponding cluster (1).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 2 is empty! self.labels_[self.medoid_indices_[2]] may not be labeled with its corresponding cluster (2).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 3 is empty! self.labels_[self.medoid_indices_[3]] may not be labeled with its corresponding cluster (3).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 4 is empty! self.labels_[self.medoid_indices_[4]] may not be labeled with its corresponding cluster (4).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 5 is empty! self.labels_[self.medoid_indices_[5]] may not be labeled with its corresponding cluster (5).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 7 is empty! self.labels_[self.medoid_indices_[7]] may not be labeled with its corresponding cluster (7).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 8 is empty! self.labels_[self.medoid_indices_[8]] may not be labeled with its corresponding cluster (8).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 9 is empty! self.labels_[self.medoid_indices_[9]] may not be labeled with its corresponding cluster (9).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 10 is empty! self.labels_[self.medoid_indices_[10]] may not be labeled with its corresponding cluster (10).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 11 is empty! self.labels_[self.medoid_indices_[11]] may not be labeled with its corresponding cluster (11).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 12 is empty! self.labels_[self.medoid_indices_[12]] may not be labeled with its corresponding cluster (12).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 13 is empty! self.labels_[self.medoid_indices_[13]] may not be labeled with its corresponding cluster (13).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 14 is empty! self.labels_[self.medoid_indices_[14]] may not be labeled with its corresponding cluster (14).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 15 is empty! self.labels_[self.medoid_indices_[15]] may not be labeled with its corresponding cluster (15).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 17 is empty! self.labels_[self.medoid_indices_[17]] may not be labeled with its corresponding cluster (17).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 18 is empty! self.labels_[self.medoid_indices_[18]] may not be labeled with its corresponding cluster (18).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 19 is empty! self.labels_[self.medoid_indices_[19]] may not be labeled with its corresponding cluster (19).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 20 is empty! self.labels_[self.medoid_indices_[20]] may not be labeled with its corresponding cluster (20).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 21 is empty! self.labels_[self.medoid_indices_[21]] may not be labeled with its corresponding cluster (21).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 22 is empty! self.labels_[self.medoid_indices_[22]] may not be labeled with its corresponding cluster (22).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 23 is empty! self.labels_[self.medoid_indices_[23]] may not be labeled with its corresponding cluster (23).\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn_extra/cluster/_k_medoids.py:329: UserWarning: Cluster 24 is empty! self.labels_[self.medoid_indices_[24]] may not be labeled with its corresponding cluster (24).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "<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>KMedoids(n_clusters=25, random_state=42)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMedoids</label><div class=\"sk-toggleable__content\"><pre>KMedoids(n_clusters=25, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMedoids(n_clusters=25, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmedoids.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "535f05b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:53:59.050346Z",
     "iopub.status.busy": "2023-11-02T20:53:59.048555Z",
     "iopub.status.idle": "2023-11-02T20:53:59.059989Z",
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     "exception": false,
     "start_time": "2023-11-02T20:53:59.031236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6,  2, ..., 16,  2, 16])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmedoids.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c896e008",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-02T20:53:59.091877Z",
     "iopub.status.busy": "2023-11-02T20:53:59.090996Z",
     "iopub.status.idle": "2023-11-02T20:53:59.096772Z",
     "shell.execute_reply": "2023-11-02T20:53:59.095987Z"
    },
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     "duration": 0.022531,
     "end_time": "2023-11-02T20:53:59.099411",
     "exception": false,
     "start_time": "2023-11-02T20:53:59.076880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  6  2 ... 16  2 16]\n"
     ]
    }
   ],
   "source": [
    "labels = kmedoids.labels_\n",
    "print(labels)"
   ]
  }
 ],
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