09. RecSys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf               # TesorFlow\n",
    "import numpy as np                    # NumPy\n",
    "import matplotlib.pyplot as plt       # Графики-то строить будем\n",
    "import pandas as pd                   # Для работы с таблицами и CSV-файлами\n",
    "import sklearn.metrics                # Для построения confusion matrix\n",
    "import seaborn as sns                 # Для отображения confusion matrix\n",
    "import os                             # Для возможности выбора GPU\n",
    "\n",
    "# Библиотека для упрощения работы с SVD\n",
    "# В колабе ее можно поставить командой !pip install git+https://github.com/mayukh18/reco.git\n",
    "import reco.recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Используем GPU 0\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "# Отключаем отладочные сообщения в TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_DIR = '/opt/datasets/movielens'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1493850091</td>\n",
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       "    <tr>\n",
       "      <th>100833</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1494273047</td>\n",
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       "      <th>100834</th>\n",
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       "  </tbody>\n",
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       "<p>100836 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(DATASET_DIR + '/ratings.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 9724 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           4.0     NaN     4.0     NaN     NaN     4.0     NaN     NaN   \n",
       "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...         ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "606         2.5     NaN     NaN     NaN     NaN     NaN     2.5     NaN   \n",
       "607         4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "608         2.5     2.0     2.0     NaN     NaN     NaN     NaN     NaN   \n",
       "609         3.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "610         5.0     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
       "\n",
       "movieId  9       10      ...  193565  193567  193571  193573  193579  193581  \\\n",
       "userId                   ...                                                   \n",
       "1           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...         ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
       "606         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "607         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "608         NaN     4.0  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "609         NaN     4.0  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "610         NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  193583  193585  193587  193609  \n",
       "userId                                   \n",
       "1           NaN     NaN     NaN     NaN  \n",
       "2           NaN     NaN     NaN     NaN  \n",
       "3           NaN     NaN     NaN     NaN  \n",
       "4           NaN     NaN     NaN     NaN  \n",
       "5           NaN     NaN     NaN     NaN  \n",
       "...         ...     ...     ...     ...  \n",
       "606         NaN     NaN     NaN     NaN  \n",
       "607         NaN     NaN     NaN     NaN  \n",
       "608         NaN     NaN     NaN     NaN  \n",
       "609         NaN     NaN     NaN     NaN  \n",
       "610         NaN     NaN     NaN     NaN  \n",
       "\n",
       "[610 rows x 9724 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_matrix = df.pivot(index=\"userId\", columns=\"movieId\", values='rating') #.fillna(0)\n",
    "utility_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "u, s, v = np.linalg.svd(utility_matrix.values, full_matrices=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да, можно так...\n",
    "\n",
    "Но, можно сделать проще - использовать библиотеку [reco](https://github.com/mayukh18/reco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаем объект SDV-рекомендатор, указывая количество фич в разложении"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd = reco.recommender.SVDRecommender(no_of_features=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаем из DataFrame *Utility Matrix*, указывая в formatizer какой столбец содержит пользователей, какой item-ы, а какой рейтинги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_item_matrix, users, items = svd.create_utility_matrix(df, formatizer={'user':'userId', 'item':'movieId', 'value':'rating'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот такая *Utility Matrix* получается:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4. , nan, 4. , ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [2.5, 2. , 2. , ..., nan, nan, nan],\n",
       "       [3. , nan, nan, ..., nan, nan, nan],\n",
       "       [5. , nan, nan, ..., nan, 4. , nan]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Делаем SVD-разложение *Utility Matrix* на u, s, v. Так же необходимо передать массивы users и items.\n",
    "И необязательно передавать данные, полученные на предыдущем шаге, можно сделать их самому (если условия задачи более сложные)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd.fit(user_item_matrix, users, items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "или используем для разложения уже созданную в pandas utility_matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd.fit(utility_matrix.to_numpy(), utility_matrix.index.values.tolist(), list(utility_matrix.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь нам доступны все возможности SVD-разложения!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Например, можно получить 5 пользователей похожих по item-ам (оценкам/покапкам/ и т.п.) на пользовтеля с userID=1 (отобразятся id похожих пользавтелей и их векторная близость)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(555, 0.4449888226854942),\n",
       " (201, 0.6172590367318571),\n",
       " (453, 0.6305270109229381),\n",
       " (186, 0.7169010737448965),\n",
       " (93, 0.7285443728759976)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.topN_similar(x=1, N=5, column='user')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Или можно получить 7-ми item-ов, похожих на itemID=3 по их использованию/оценкам пользователями:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2012, 0.27226832882353874),\n",
       " (1028, 0.3011053980250963),\n",
       " (5103, 0.34012564092614406),\n",
       " (785, 0.3741251663504187),\n",
       " (2916, 0.3958923146917345),\n",
       " (2804, 0.4001426062572117),\n",
       " (1259, 0.40057537507935215)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.topN_similar(x=3, N=7, column='item')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ну, и конечно же можно получить рекомендации (например 4 рекомендации) по item-ам, для пользователя UserId = 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2972, 6983, 126088, 124851]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.recommend([5], N=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно даже сразу для нескольких юзеров, например, для 5,6,7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2972, 6983, 126088, 124851],\n",
       " [91355, 162344, 6611, 120138],\n",
       " [4495, 8804, 95311, 5416]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.recommend([5, 6, 7], N=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нужно оперировать не id-шниками, а прям названиями:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193581</th>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193583</th>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193585</th>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193587</th>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193609</th>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             title  \\\n",
       "movieId                                              \n",
       "1                                 Toy Story (1995)   \n",
       "2                                   Jumanji (1995)   \n",
       "3                          Grumpier Old Men (1995)   \n",
       "4                         Waiting to Exhale (1995)   \n",
       "5               Father of the Bride Part II (1995)   \n",
       "...                                            ...   \n",
       "193581   Black Butler: Book of the Atlantic (2017)   \n",
       "193583                No Game No Life: Zero (2017)   \n",
       "193585                                Flint (2017)   \n",
       "193587         Bungo Stray Dogs: Dead Apple (2018)   \n",
       "193609         Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                              genres  \n",
       "movieId                                               \n",
       "1        Adventure|Animation|Children|Comedy|Fantasy  \n",
       "2                         Adventure|Children|Fantasy  \n",
       "3                                     Comedy|Romance  \n",
       "4                               Comedy|Drama|Romance  \n",
       "5                                             Comedy  \n",
       "...                                              ...  \n",
       "193581               Action|Animation|Comedy|Fantasy  \n",
       "193583                      Animation|Comedy|Fantasy  \n",
       "193585                                         Drama  \n",
       "193587                              Action|Animation  \n",
       "193609                                        Comedy  \n",
       "\n",
       "[9742 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies = pd.read_csv(DATASET_DIR + '/movies.csv', index_col = \"movieId\")\n",
    "df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "На фильм 'Jumanji (1995)' по оценкам пользователей похожи следующие фильмы:\n",
      "1 - Toy Story (1995) - Adventure|Animation|Children|Comedy|Fantasy\n",
      "2671 - Notting Hill (1999) - Comedy|Romance\n",
      "4306 - Shrek (2001) - Adventure|Animation|Children|Comedy|Fantasy|Romance\n",
      "2424 - You've Got Mail (1998) - Comedy|Romance\n",
      "2572 - 10 Things I Hate About You (1999) - Comedy|Romance\n"
     ]
    }
   ],
   "source": [
    "# Найдем фильмы похожие на movieId=2\n",
    "movie_id = 2\n",
    "movie_name = df_movies.loc[movie_id]['title']\n",
    "print(\"На фильм '{}' по оценкам пользователей похожи следующие фильмы:\".format(movie_name))\n",
    "sims = svd.topN_similar(x=movie_id, N=5, column='item')\n",
    "for sim in sims:\n",
    "    print('{} - {} - {}'.format(sim[0], df_movies.loc[sim[0]]['title'], df_movies.loc[sim[0]]['genres']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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