04_GNN_RecommenderSystem_Final.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook I train and evaluate the final SAGE-model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from datetime import datetime\n",
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "from torch.nn import Linear, MSELoss\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch_geometric.data import HeteroData\n",
    "from torch_geometric.nn import SAGEConv, to_hetero\n",
    "from torch_geometric.transforms import RandomLinkSplit, ToUndirected, AddSelfLoops, NormalizeFeatures\n",
    "\n",
    "sns.set_theme(style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.0+cpu'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(671, 1)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId\n",
       "0       0\n",
       "1       1\n",
       "2       2\n",
       "3       3\n",
       "4       4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df = pd.read_csv('data/movieLens100k_users.csv')\n",
    "\n",
    "print(user_df.shape)\n",
    "user_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9125, 26)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>...</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>runtime</th>\n",
       "      <th>year</th>\n",
       "      <th>num_popular_actors</th>\n",
       "      <th>num_popular_directors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.528928</td>\n",
       "      <td>5.144229</td>\n",
       "      <td>-0.876122</td>\n",
       "      <td>0.158905</td>\n",
       "      <td>2.943417</td>\n",
       "      <td>4.502288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.174897</td>\n",
       "      <td>1.394602</td>\n",
       "      <td>-0.058951</td>\n",
       "      <td>0.158905</td>\n",
       "      <td>1.228413</td>\n",
       "      <td>-0.218854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.115253</td>\n",
       "      <td>-0.318207</td>\n",
       "      <td>-0.165539</td>\n",
       "      <td>0.158905</td>\n",
       "      <td>-0.486591</td>\n",
       "      <td>-0.218854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.082418</td>\n",
       "      <td>-0.426889</td>\n",
       "      <td>0.651632</td>\n",
       "      <td>0.158905</td>\n",
       "      <td>-0.486591</td>\n",
       "      <td>-0.218854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.792268</td>\n",
       "      <td>-0.248423</td>\n",
       "      <td>0.012107</td>\n",
       "      <td>0.158905</td>\n",
       "      <td>1.228413</td>\n",
       "      <td>-0.218854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  Action  Adventure  Animation  Children  Comedy  Crime  \\\n",
       "0        0       0          1          1         1       1      0   \n",
       "1        1       0          1          0         1       0      0   \n",
       "2        2       0          0          0         0       1      0   \n",
       "3        3       0          0          0         0       1      0   \n",
       "4        4       0          0          0         0       1      0   \n",
       "\n",
       "   Documentary  Drama  Fantasy  ...  Sci-Fi  Thriller  War  Western  \\\n",
       "0            0      0        1  ...       0         0    0        0   \n",
       "1            0      0        1  ...       0         0    0        0   \n",
       "2            0      0        0  ...       0         0    0        0   \n",
       "3            0      1        0  ...       0         0    0        0   \n",
       "4            0      0        0  ...       0         0    0        0   \n",
       "\n",
       "   imdbRating  imdbVotes   runtime      year  num_popular_actors  \\\n",
       "0    1.528928   5.144229 -0.876122  0.158905            2.943417   \n",
       "1    0.174897   1.394602 -0.058951  0.158905            1.228413   \n",
       "2   -0.115253  -0.318207 -0.165539  0.158905           -0.486591   \n",
       "3   -1.082418  -0.426889  0.651632  0.158905           -0.486591   \n",
       "4   -0.792268  -0.248423  0.012107  0.158905            1.228413   \n",
       "\n",
       "   num_popular_directors  \n",
       "0               4.502288  \n",
       "1              -0.218854  \n",
       "2              -0.218854  \n",
       "3              -0.218854  \n",
       "4              -0.218854  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df = pd.read_csv('data/movieLens100k_movies.csv')\n",
    "movie_df.drop(columns=['plot', 'title'], axis=1, inplace=True)\n",
    "\n",
    "print(movie_df.shape)\n",
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9125 entries, 0 to 9124\n",
      "Data columns (total 26 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   movieId                9125 non-null   int64  \n",
      " 1   Action                 9125 non-null   int64  \n",
      " 2   Adventure              9125 non-null   int64  \n",
      " 3   Animation              9125 non-null   int64  \n",
      " 4   Children               9125 non-null   int64  \n",
      " 5   Comedy                 9125 non-null   int64  \n",
      " 6   Crime                  9125 non-null   int64  \n",
      " 7   Documentary            9125 non-null   int64  \n",
      " 8   Drama                  9125 non-null   int64  \n",
      " 9   Fantasy                9125 non-null   int64  \n",
      " 10  Film-Noir              9125 non-null   int64  \n",
      " 11  Horror                 9125 non-null   int64  \n",
      " 12  IMAX                   9125 non-null   int64  \n",
      " 13  Musical                9125 non-null   int64  \n",
      " 14  Mystery                9125 non-null   int64  \n",
      " 15  Romance                9125 non-null   int64  \n",
      " 16  Sci-Fi                 9125 non-null   int64  \n",
      " 17  Thriller               9125 non-null   int64  \n",
      " 18  War                    9125 non-null   int64  \n",
      " 19  Western                9125 non-null   int64  \n",
      " 20  imdbRating             9125 non-null   float64\n",
      " 21  imdbVotes              9125 non-null   float64\n",
      " 22  runtime                9125 non-null   float64\n",
      " 23  year                   9125 non-null   float64\n",
      " 24  num_popular_actors     9125 non-null   float64\n",
      " 25  num_popular_directors  9125 non-null   float64\n",
      "dtypes: float64(6), int64(20)\n",
      "memory usage: 1.8 MB\n"
     ]
    }
   ],
   "source": [
    "movie_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100004, 3)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1815</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1962</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2380</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2925</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1515</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       0     1815     2.0\n",
       "1       0     1962     2.5\n",
       "2       0     2380     1.0\n",
       "3       0     2925     3.0\n",
       "4       0     1515     4.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df = pd.read_csv('data/movieLens100k_ratings.csv')\n",
    "\n",
    "print(rating_df.shape)\n",
    "rating_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert not movie_df.isnull().values.any(), 'Null values in dataset!'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pretrained user-embeddings\n",
    "\n",
    "The user embeddings are calculated in the notebook `03_LinkPrediction_userEmbeddings.ipynb`. The final embeddings are stored as `.npy` and just loaded here. Also the plot embeddings and their reduced forms are loaded here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User embedding shape: (671, 8)\n",
      "Plot embedding shape (UMAP-4): (9125, 4)\n",
      "Plot embedding shape (t-SENE-2): (9125, 2)\n"
     ]
    }
   ],
   "source": [
    "user_embeddings = np.load('./data/np_user_embeddings_20221125.npy')\n",
    "# user_embeddings = np.load('./data/np_user_embeddings_20221223.npy')\n",
    "\n",
    "plot_embeddings_umap4 = np.load('./data/np_plot_embeddings_umap_4d.npy')\n",
    "plot_embeddings_tsne2 = np.load('./data/np_plot_embeddings_tsne_2d.npy')\n",
    "\n",
    "print('User embedding shape:', user_embeddings.shape)\n",
    "print('Plot embedding shape (UMAP-4):', plot_embeddings_umap4.shape)\n",
    "print('Plot embedding shape (t-SENE-2):', plot_embeddings_tsne2.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Users: 671\n",
      "# Movies: 9125\n"
     ]
    }
   ],
   "source": [
    "assert len(user_embeddings) == 671, 'User shape not as expected'\n",
    "assert len(user_df) == 671, 'User shape not as expected'\n",
    "assert len(movie_df) == 9125, 'Movie shape not as expected'\n",
    "assert len(rating_df) == 100_004, 'Rating shape not as expected'\n",
    "\n",
    "n_users = len(user_df)\n",
    "n_movies = len(movie_df)\n",
    "\n",
    "print(f'# Users: {n_users}\\n# Movies: {n_movies}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9125, 26)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Pytorch Geometric (heterogenious) Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_no_plot: (9125, 26)\n",
      "dataset_tsne2: (9125, 28)\n",
      "dataset_umap4: (9125, 30)\n"
     ]
    }
   ],
   "source": [
    "def create_movie_df(df:pd.DataFrame, kind:str) -> pd.DataFrame:\n",
    "    \"\"\" \"\"\"\n",
    "    if kind == 'no_plot_embedding':\n",
    "        movie_df = df\n",
    "    elif kind == 'umap4':\n",
    "        movie_df = pd.concat([df, pd.DataFrame(plot_embeddings_umap4)], axis=1)\n",
    "    elif kind == 'tsne2':\n",
    "        movie_df = pd.concat([df, pd.DataFrame(plot_embeddings_tsne2)], axis=1)\n",
    "    else:\n",
    "        raise ValueError('kind must be either \"no_plot_embedding\", \"umap4\", or \"tsne2\"')\n",
    "\n",
    "    return movie_df\n",
    "\n",
    "movie_df_no_plot = create_movie_df(movie_df, kind='no_plot_embedding')\n",
    "movie_df_tsne2 = create_movie_df(movie_df, kind='tsne2')\n",
    "movie_df_umap4 = create_movie_df(movie_df, kind='umap4')\n",
    "\n",
    "print('dataset_no_plot:', movie_df_no_plot.shape)\n",
    "print('dataset_tsne2:', movie_df_tsne2.shape)\n",
    "print('dataset_umap4:', movie_df_umap4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_no_plot: HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 25] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n",
      "dataset_tsne2: HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 27] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n",
      "dataset_umap4: HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 29] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "def create_dataset(movie_df:pd.DataFrame) -> dict:\n",
    "    \"\"\" Creates a PyG HeteroData object.\"\"\"\n",
    "\n",
    "    x_user = torch.Tensor(user_embeddings)\n",
    "\n",
    "    x_movie = movie_df.drop(columns=['movieId'], axis=1)\n",
    "    x_movie = torch.Tensor(x_movie.values)\n",
    "\n",
    "    edge_index = torch.Tensor(rating_df[['userId', 'movieId']].values).swapaxes(1,0).to(torch.long)\n",
    "    edge_label = torch.Tensor(rating_df['rating'].values)\n",
    "\n",
    "    data = HeteroData()\n",
    "    data['user'].x = x_user\n",
    "    data['movie'].x = x_movie\n",
    "\n",
    "    data[('user', 'rates', 'movie')].edge_index = edge_index\n",
    "    data[('user', 'rates', 'movie')].edge_label = edge_label\n",
    "\n",
    "    data = ToUndirected()(data)\n",
    "    del data[('movie', 'rev_rates', 'user')].edge_label\n",
    "\n",
    "    return data\n",
    "\n",
    "dataset_no_plot = create_dataset(movie_df_no_plot)\n",
    "dataset_tsne2 = create_dataset(movie_df_tsne2)\n",
    "dataset_umap4 = create_dataset(movie_df_umap4)\n",
    "\n",
    "\n",
    "print('dataset_no_plot:', dataset_no_plot)\n",
    "print('dataset_tsne2:', dataset_tsne2)\n",
    "print('dataset_umap4:', dataset_umap4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train / Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(data:dict, num_val:float=0.1, num_test:float=0.1):\n",
    "    train_data, val_data, test_data = RandomLinkSplit(\n",
    "        num_val=num_val,\n",
    "        num_test=num_test,\n",
    "        neg_sampling_ratio=0.0,\n",
    "        edge_types=[('user', 'rates', 'movie')],\n",
    "        rev_edge_types=[('movie', 'rev_rates', 'user')],\n",
    "    )(data)\n",
    "    return train_data, val_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNNEncoder(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels, out_channels):\n",
    "        super().__init__()\n",
    "        # these convolutions have been replicated to match the number of edge types\n",
    "        self.conv1 = SAGEConv((-1, -1), hidden_channels)\n",
    "        self.conv2 = SAGEConv((-1, -1), out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = self.conv1(x, edge_index).relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        # x = self.norm2(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class EdgeDecoder(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super().__init__()\n",
    "        self.lin1 = Linear(2 * hidden_channels, hidden_channels)\n",
    "        self.dropout = torch.nn.Dropout(0.00)\n",
    "        self.lin_out = Linear(hidden_channels, 1)\n",
    "        \n",
    "    def forward(self, z_dict, edge_label_index):\n",
    "        row, col = edge_label_index\n",
    "        # concat user and movie embeddings\n",
    "        z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1)\n",
    "        # concatenated embeddings passed to linear layer\n",
    "        z = self.lin1(z).relu()\n",
    "        z = self.dropout(z)\n",
    "        z = self.lin_out(z)\n",
    "        return z.view(-1)\n",
    "\n",
    "\n",
    "class Model(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels, out_channels):\n",
    "        super().__init__()\n",
    "        self.encoder = GNNEncoder(hidden_channels, hidden_channels)\n",
    "        self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum')\n",
    "        self.decoder = EdgeDecoder(hidden_channels)\n",
    "\n",
    "    def forward(self, x_dict, edge_index_dict, edge_label_index):\n",
    "        # z_dict contains dictionary of movie and user embeddings returned from GraphSage\n",
    "        z_dict = self.encoder(x_dict, edge_index_dict)\n",
    "        return self.decoder(z_dict, edge_label_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Due to lazy initialization, we need to run one model step so the number\n",
    "# of parameters can be inferred:\n",
    "def lazy_init(model, data):\n",
    "    \"\"\" Due to lazy initialization, we need to run one model step \n",
    "        so the number of parameters can be inferred.\"\"\"\n",
    "    with torch.no_grad():\n",
    "        model.encoder(data.x_dict, data.edge_index_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, data, optimizer):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    pred = model(data.x_dict, data.edge_index_dict,\n",
    "                 data['user', 'movie'].edge_label_index)\n",
    "    target = data['user', 'movie'].edge_label\n",
    "    loss = F.mse_loss(pred, target).sqrt()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return float(loss)\n",
    "\n",
    "\n",
    "@torch.no_grad()\n",
    "def test(model, data):\n",
    "    model.eval()\n",
    "    pred = model(data.x_dict, data.edge_index_dict,\n",
    "                 data['user', 'movie'].edge_label_index)\n",
    "    pred = pred.clamp(min=0, max=5)\n",
    "    target = data['user', 'movie'].edge_label.float()\n",
    "    rmse = F.mse_loss(pred, target).sqrt()\n",
    "    return float(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 25] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n",
      "\tTraining CV #1\tepoch # 100\ttrain_loss: 0.969\tval_loss: 0.994\ttest_loss: 0.995\n",
      "\tTraining CV #1\tepoch # 200\ttrain_loss: 0.916\tval_loss: 0.947\ttest_loss: 0.949\n",
      "\tTraining CV #1\tepoch # 300\ttrain_loss: 0.869\tval_loss: 0.890\ttest_loss: 0.899\n",
      "\tTraining CV #1\tepoch # 400\ttrain_loss: 0.863\tval_loss: 0.879\ttest_loss: 0.890\n",
      "\tTraining CV #1\tepoch # 500\ttrain_loss: 0.860\tval_loss: 0.876\ttest_loss: 0.886\n",
      "\tTraining CV #1\tepoch # 600\ttrain_loss: 0.859\tval_loss: 0.875\ttest_loss: 0.884\n",
      "\tTraining CV #1\tepoch # 700\ttrain_loss: 0.858\tval_loss: 0.874\ttest_loss: 0.882\n",
      "\tTraining CV #1\tepoch # 800\ttrain_loss: 0.858\tval_loss: 0.873\ttest_loss: 0.882\n",
      "\tTraining CV #1\tepoch # 900\ttrain_loss: 0.857\tval_loss: 0.872\ttest_loss: 0.881\n",
      "\tTraining CV #1\tepoch # 1000\ttrain_loss: 0.856\tval_loss: 0.872\ttest_loss: 0.881\n",
      "\tTraining CV #2\tepoch # 100\ttrain_loss: 0.960\tval_loss: 0.996\ttest_loss: 1.003\n",
      "\tTraining CV #2\tepoch # 200\ttrain_loss: 0.895\tval_loss: 0.943\ttest_loss: 0.944\n",
      "\tTraining CV #2\tepoch # 300\ttrain_loss: 0.861\tval_loss: 0.895\ttest_loss: 0.893\n",
      "\tTraining CV #2\tepoch # 400\ttrain_loss: 0.858\tval_loss: 0.895\ttest_loss: 0.891\n",
      "\tTraining CV #2\tepoch # 500\ttrain_loss: 0.856\tval_loss: 0.898\ttest_loss: 0.894\n",
      "\tTraining CV #2\tepoch # 600\ttrain_loss: 0.852\tval_loss: 0.897\ttest_loss: 0.892\n",
      "\tTraining CV #2\tepoch # 700\ttrain_loss: 0.850\tval_loss: 0.894\ttest_loss: 0.888\n",
      "\tTraining CV #2\tepoch # 800\ttrain_loss: 0.847\tval_loss: 0.893\ttest_loss: 0.887\n",
      "\tTraining CV #2\tepoch # 900\ttrain_loss: 0.844\tval_loss: 0.896\ttest_loss: 0.888\n",
      "\tTraining CV #2\tepoch # 1000\ttrain_loss: 0.842\tval_loss: 0.897\ttest_loss: 0.889\n",
      "\tTraining CV #3\tepoch # 100\ttrain_loss: 0.966\tval_loss: 0.987\ttest_loss: 0.974\n",
      "\tTraining CV #3\tepoch # 200\ttrain_loss: 0.903\tval_loss: 0.929\ttest_loss: 0.914\n",
      "\tTraining CV #3\tepoch # 300\ttrain_loss: 0.863\tval_loss: 0.874\ttest_loss: 0.862\n",
      "\tTraining CV #3\tepoch # 400\ttrain_loss: 0.858\tval_loss: 0.869\ttest_loss: 0.856\n",
      "\tTraining CV #3\tepoch # 500\ttrain_loss: 0.854\tval_loss: 0.866\ttest_loss: 0.852\n",
      "\tTraining CV #3\tepoch # 600\ttrain_loss: 0.851\tval_loss: 0.865\ttest_loss: 0.851\n",
      "\tTraining CV #3\tepoch # 700\ttrain_loss: 0.848\tval_loss: 0.864\ttest_loss: 0.850\n",
      "\tTraining CV #3\tepoch # 800\ttrain_loss: 0.847\tval_loss: 0.865\ttest_loss: 0.851\n",
      "\tTraining CV #3\tepoch # 900\ttrain_loss: 0.845\tval_loss: 0.866\ttest_loss: 0.852\n",
      "\tTraining CV #3\tepoch # 1000\ttrain_loss: 0.843\tval_loss: 0.866\ttest_loss: 0.852\n",
      "HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 27] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n",
      "\tTraining CV #1\tepoch # 100\ttrain_loss: 0.997\tval_loss: 0.999\ttest_loss: 1.004\n",
      "\tTraining CV #1\tepoch # 200\ttrain_loss: 0.915\tval_loss: 0.924\ttest_loss: 0.933\n",
      "\tTraining CV #1\tepoch # 300\ttrain_loss: 0.865\tval_loss: 0.877\ttest_loss: 0.889\n",
      "\tTraining CV #1\tepoch # 400\ttrain_loss: 0.859\tval_loss: 0.873\ttest_loss: 0.884\n",
      "\tTraining CV #1\tepoch # 500\ttrain_loss: 0.857\tval_loss: 0.869\ttest_loss: 0.881\n",
      "\tTraining CV #1\tepoch # 600\ttrain_loss: 0.855\tval_loss: 0.868\ttest_loss: 0.880\n",
      "\tTraining CV #1\tepoch # 700\ttrain_loss: 0.854\tval_loss: 0.867\ttest_loss: 0.878\n",
      "\tTraining CV #1\tepoch # 800\ttrain_loss: 0.852\tval_loss: 0.866\ttest_loss: 0.877\n",
      "\tTraining CV #1\tepoch # 900\ttrain_loss: 0.851\tval_loss: 0.865\ttest_loss: 0.876\n",
      "\tTraining CV #1\tepoch # 1000\ttrain_loss: 0.850\tval_loss: 0.865\ttest_loss: 0.876\n",
      "\tTraining CV #2\tepoch # 100\ttrain_loss: 1.002\tval_loss: 1.003\ttest_loss: 0.995\n",
      "\tTraining CV #2\tepoch # 200\ttrain_loss: 0.925\tval_loss: 0.930\ttest_loss: 0.923\n",
      "\tTraining CV #2\tepoch # 300\ttrain_loss: 0.875\tval_loss: 0.883\ttest_loss: 0.873\n",
      "\tTraining CV #2\tepoch # 400\ttrain_loss: 0.863\tval_loss: 0.872\ttest_loss: 0.863\n",
      "\tTraining CV #2\tepoch # 500\ttrain_loss: 0.859\tval_loss: 0.866\ttest_loss: 0.858\n",
      "\tTraining CV #2\tepoch # 600\ttrain_loss: 0.857\tval_loss: 0.864\ttest_loss: 0.856\n",
      "\tTraining CV #2\tepoch # 700\ttrain_loss: 0.856\tval_loss: 0.863\ttest_loss: 0.856\n",
      "\tTraining CV #2\tepoch # 800\ttrain_loss: 0.854\tval_loss: 0.862\ttest_loss: 0.854\n",
      "\tTraining CV #2\tepoch # 900\ttrain_loss: 0.853\tval_loss: 0.863\ttest_loss: 0.854\n",
      "\tTraining CV #2\tepoch # 1000\ttrain_loss: 0.851\tval_loss: 0.861\ttest_loss: 0.853\n",
      "\tTraining CV #3\tepoch # 100\ttrain_loss: 1.009\tval_loss: 1.014\ttest_loss: 1.020\n",
      "\tTraining CV #3\tepoch # 200\ttrain_loss: 0.935\tval_loss: 0.945\ttest_loss: 0.954\n",
      "\tTraining CV #3\tepoch # 300\ttrain_loss: 0.872\tval_loss: 0.879\ttest_loss: 0.887\n",
      "\tTraining CV #3\tepoch # 400\ttrain_loss: 0.862\tval_loss: 0.872\ttest_loss: 0.880\n",
      "\tTraining CV #3\tepoch # 500\ttrain_loss: 0.859\tval_loss: 0.869\ttest_loss: 0.877\n",
      "\tTraining CV #3\tepoch # 600\ttrain_loss: 0.857\tval_loss: 0.868\ttest_loss: 0.876\n",
      "\tTraining CV #3\tepoch # 700\ttrain_loss: 0.855\tval_loss: 0.867\ttest_loss: 0.875\n",
      "\tTraining CV #3\tepoch # 800\ttrain_loss: 0.854\tval_loss: 0.866\ttest_loss: 0.875\n",
      "\tTraining CV #3\tepoch # 900\ttrain_loss: 0.853\tval_loss: 0.865\ttest_loss: 0.874\n",
      "\tTraining CV #3\tepoch # 1000\ttrain_loss: 0.852\tval_loss: 0.865\ttest_loss: 0.874\n",
      "HeteroData(\n",
      "  \u001b[1muser\u001b[0m={ x=[671, 8] },\n",
      "  \u001b[1mmovie\u001b[0m={ x=[9125, 29] },\n",
      "  \u001b[1m(user, rates, movie)\u001b[0m={\n",
      "    edge_index=[2, 100004],\n",
      "    edge_label=[100004]\n",
      "  },\n",
      "  \u001b[1m(movie, rev_rates, user)\u001b[0m={ edge_index=[2, 100004] }\n",
      ")\n",
      "\tTraining CV #1\tepoch # 100\ttrain_loss: 0.973\tval_loss: 0.983\ttest_loss: 0.983\n",
      "\tTraining CV #1\tepoch # 200\ttrain_loss: 0.900\tval_loss: 0.919\ttest_loss: 0.920\n",
      "\tTraining CV #1\tepoch # 300\ttrain_loss: 0.863\tval_loss: 0.883\ttest_loss: 0.882\n",
      "\tTraining CV #1\tepoch # 400\ttrain_loss: 0.861\tval_loss: 0.881\ttest_loss: 0.880\n",
      "\tTraining CV #1\tepoch # 500\ttrain_loss: 0.860\tval_loss: 0.881\ttest_loss: 0.880\n",
      "\tTraining CV #1\tepoch # 600\ttrain_loss: 0.858\tval_loss: 0.881\ttest_loss: 0.880\n",
      "\tTraining CV #1\tepoch # 700\ttrain_loss: 0.856\tval_loss: 0.884\ttest_loss: 0.882\n",
      "\tTraining CV #1\tepoch # 800\ttrain_loss: 0.855\tval_loss: 0.888\ttest_loss: 0.885\n",
      "\tTraining CV #1\tepoch # 900\ttrain_loss: 0.853\tval_loss: 0.890\ttest_loss: 0.888\n",
      "\tTraining CV #1\tepoch # 1000\ttrain_loss: 0.850\tval_loss: 0.893\ttest_loss: 0.888\n",
      "\tTraining CV #2\tepoch # 100\ttrain_loss: 0.982\tval_loss: 0.999\ttest_loss: 0.992\n",
      "\tTraining CV #2\tepoch # 200\ttrain_loss: 0.905\tval_loss: 0.952\ttest_loss: 0.942\n",
      "\tTraining CV #2\tepoch # 300\ttrain_loss: 0.863\tval_loss: 0.877\ttest_loss: 0.875\n",
      "\tTraining CV #2\tepoch # 400\ttrain_loss: 0.860\tval_loss: 0.877\ttest_loss: 0.874\n",
      "\tTraining CV #2\tepoch # 500\ttrain_loss: 0.858\tval_loss: 0.882\ttest_loss: 0.878\n",
      "\tTraining CV #2\tepoch # 600\ttrain_loss: 0.857\tval_loss: 0.887\ttest_loss: 0.884\n",
      "\tTraining CV #2\tepoch # 700\ttrain_loss: 0.854\tval_loss: 0.891\ttest_loss: 0.887\n",
      "\tTraining CV #2\tepoch # 800\ttrain_loss: 0.852\tval_loss: 0.898\ttest_loss: 0.892\n",
      "\tTraining CV #2\tepoch # 900\ttrain_loss: 0.850\tval_loss: 0.904\ttest_loss: 0.896\n",
      "\tTraining CV #2\tepoch # 1000\ttrain_loss: 0.849\tval_loss: 0.908\ttest_loss: 0.899\n",
      "\tTraining CV #3\tepoch # 100\ttrain_loss: 0.974\tval_loss: 1.016\ttest_loss: 0.999\n",
      "\tTraining CV #3\tepoch # 200\ttrain_loss: 0.882\tval_loss: 0.938\ttest_loss: 0.923\n",
      "\tTraining CV #3\tepoch # 300\ttrain_loss: 0.860\tval_loss: 0.903\ttest_loss: 0.888\n",
      "\tTraining CV #3\tepoch # 400\ttrain_loss: 0.858\tval_loss: 0.896\ttest_loss: 0.882\n",
      "\tTraining CV #3\tepoch # 500\ttrain_loss: 0.857\tval_loss: 0.893\ttest_loss: 0.880\n",
      "\tTraining CV #3\tepoch # 600\ttrain_loss: 0.856\tval_loss: 0.892\ttest_loss: 0.878\n",
      "\tTraining CV #3\tepoch # 700\ttrain_loss: 0.855\tval_loss: 0.891\ttest_loss: 0.877\n",
      "\tTraining CV #3\tepoch # 800\ttrain_loss: 0.854\tval_loss: 0.890\ttest_loss: 0.876\n",
      "\tTraining CV #3\tepoch # 900\ttrain_loss: 0.854\tval_loss: 0.889\ttest_loss: 0.875\n",
      "\tTraining CV #3\tepoch # 1000\ttrain_loss: 0.853\tval_loss: 0.889\ttest_loss: 0.875\n"
     ]
    }
   ],
   "source": [
    "do_cv = False\n",
    "\n",
    "if do_cv:\n",
    "    EPOCHS = 1000\n",
    "    best_test_loss = 100\n",
    "    save_date = datetime.today().strftime('%Y%m%d')\n",
    "    train_losses, val_losses, test_losses = [], [], []\n",
    "    results = {}\n",
    "    for dataset, name in zip([dataset_no_plot, dataset_tsne2, dataset_umap4],\n",
    "                            ['NoPlotEmbed', 'tSENE2', 'UMAP4']):\n",
    "        print(dataset)\n",
    "        data = dataset\n",
    "\n",
    "        results[name] = {}\n",
    "        results[name]['train_losses'] = []\n",
    "        results[name]['val_losses'] = []\n",
    "        results[name]['test_losses'] = []\n",
    "        results[name]['time_elapsed'] = []\n",
    "        \n",
    "        # Cross validation\n",
    "        for cv in range(1, 4):\n",
    "\n",
    "            train_data, val_data, test_data = train_test_split(data)\n",
    "            model = Model(hidden_channels=32, out_channels=32).to(device)\n",
    "            optimizer = torch.optim.Adam(model.parameters())\n",
    "            lazy_init(model=model, data=train_data)\n",
    "            \n",
    "            train_losses, val_losses, test_losses = [], [], []\n",
    "            for epoch in range(1, EPOCHS+1):\n",
    "                train_loss = train(model=model, data=train_data, optimizer=optimizer)\n",
    "                val_loss = test(model=model, data=val_data)\n",
    "                test_loss = test(model=model, data=test_data)\n",
    "\n",
    "                train_losses.append(train_loss), val_losses.append(val_loss), test_losses.append(test_loss)\n",
    "\n",
    "                if epoch % 100 == 0:\n",
    "                    print(f'\\tTraining CV #{cv}\\tepoch # {epoch}\\ttrain_loss: {train_loss:.3f}' \\\n",
    "                        f'\\tval_loss: {val_loss:.3f}\\ttest_loss: {test_loss:.3f}')\n",
    "\n",
    "            results[name]['train_losses'].append(train_losses)\n",
    "            results[name]['val_losses'].append(val_losses)\n",
    "            results[name]['test_losses'].append(test_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "OFFSET = 300\n",
    "_, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "color_list = ['royalblue', 'orchid', 'orange']\n",
    "assert len(results) == len(color_list)\n",
    "\n",
    "for model, color in zip(results.keys(), color_list):\n",
    "    # print(model)\n",
    "    train_losses = results[model]['train_losses']\n",
    "    test_losses = results[model]['test_losses']\n",
    "    for i, test_loss in enumerate(test_losses):\n",
    "        # print(i)\n",
    "        if i == 0:\n",
    "            ax0.plot(test_loss, label=model, c=color, alpha=0.5)\n",
    "            ax1.plot(range(OFFSET, len(test_loss)), test_loss[OFFSET:], label=model, c=color, alpha=0.5)\n",
    "        else:\n",
    "            ax0.plot(test_loss, c=color, alpha=0.5)\n",
    "            ax1.plot(range(OFFSET, len(test_loss)), test_loss[OFFSET:], c=color, alpha=0.5)\n",
    "\n",
    "ax0.legend()\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tEpoch # 100\ttrain_loss: 0.990\tval_loss: 1.004\ttest_loss: 0.991\n",
      "\tEpoch # 200\ttrain_loss: 0.909\tval_loss: 0.931\ttest_loss: 0.914\n",
      "\tEpoch # 300\ttrain_loss: 0.859\tval_loss: 0.886\ttest_loss: 0.863\n",
      "\tEpoch # 400\ttrain_loss: 0.855\tval_loss: 0.881\ttest_loss: 0.858\n",
      "\tEpoch # 500\ttrain_loss: 0.853\tval_loss: 0.879\ttest_loss: 0.858\n",
      "\tEpoch # 600\ttrain_loss: 0.851\tval_loss: 0.878\ttest_loss: 0.858\n"
     ]
    }
   ],
   "source": [
    "do_train = False\n",
    "\n",
    "if do_train:\n",
    "    EPOCHS = 600\n",
    "    best_test_loss = 100\n",
    "    save_date = datetime.today().strftime('%Y%m%d')\n",
    "    train_losses, val_losses, test_losses = [], [], []\n",
    "    cv_results = {}\n",
    "    cv_results['train_losses'] = []\n",
    "    cv_results['val_losses'] = []\n",
    "    cv_results['test_losses'] = []\n",
    "\n",
    "    train_data, val_data, test_data = train_test_split(dataset_no_plot)\n",
    "    model = Model(hidden_channels=24, out_channels=24).to(device)\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "    lazy_init(model=model, data=train_data)\n",
    "\n",
    "    train_losses, val_losses, test_losses = [], [], []\n",
    "    for epoch in range(1, EPOCHS+1):\n",
    "        train_loss = train(model=model, data=train_data, optimizer=optimizer)\n",
    "        val_loss = test(model=model, data=val_data)\n",
    "        test_loss = test(model=model, data=test_data)\n",
    "\n",
    "        train_losses.append(train_loss)\n",
    "        val_losses.append(val_loss)\n",
    "        test_losses.append(test_loss)\n",
    "\n",
    "        # print to console\n",
    "        if epoch % 100 == 0:\n",
    "            print(f'\\tEpoch # {epoch}\\ttrain_loss: {train_loss:.3f}\\tval_loss: {val_loss:.3f}\\ttest_loss: {test_loss:.3f}')\n",
    "\n",
    "            # safe best model\n",
    "            if test_loss < best_test_loss:\n",
    "                torch.save(model, f'./models/sage_model_{save_date}')\n",
    "                best_test_loss = test_loss\n",
    "\n",
    "    cv_results['train_losses'].append(train_losses)\n",
    "    cv_results['test_losses'].append(test_losses)\n",
    "\n",
    "    # save results as json\n",
    "    cv_results_json = json.dumps(cv_results, indent=4)\n",
    "    with open(f'models/sage_model_{save_date}_results.json', 'w') as f:\n",
    "        f.write(cv_results_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8576915264129639\n"
     ]
    }
   ],
   "source": [
    "# Load best model\n",
    "model = torch.load('models/sage_model_20230108')\n",
    "test_loss = test(model=model, data=test_data)\n",
    "print(test_loss)\n",
    "\n",
    "# Load model results\n",
    "with open('models/sage_model_20230108_results.json') as json_file:\n",
    "    model_results = json.load(json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SKIP_WARM_UP_STEPS = 200\n",
    "\n",
    "train_losses = model_results['train_losses'][0]\n",
    "test_losses = model_results['test_losses'][0]\n",
    "\n",
    "_, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "ax0.plot(train_losses, label='Train')\n",
    "ax0.plot(test_losses, label='Test')\n",
    "ax0.set_title('Learning curve')\n",
    "ax0.set_xlabel('Epochs (x10)')\n",
    "ax0.set_ylabel('Loss (RMSE)')\n",
    "ax0.legend()\n",
    "\n",
    "ax1.plot(range(SKIP_WARM_UP_STEPS, len(train_losses)), train_losses[SKIP_WARM_UP_STEPS:], label='Train')\n",
    "ax1.plot(range(SKIP_WARM_UP_STEPS, len(test_losses)), test_losses[SKIP_WARM_UP_STEPS:], label='Test')\n",
    "ax1.set_title(f'Learning curve (after {SKIP_WARM_UP_STEPS} warm up steps)')\n",
    "ax1.set_xlabel('Epochs (x10)')\n",
    "ax1.set_ylabel('Loss (RMSE)')\n",
    "ax1.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "pred = model(test_data.x_dict, test_data.edge_index_dict,\n",
    "                test_data['user', 'movie'].edge_label_index)\n",
    "pred = pred.clamp(min=0, max=5)\n",
    "target = test_data['user', 'movie'].edge_label.float()\n",
    "rmse = F.mse_loss(pred, target).sqrt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([2.9724, 3.9992, 3.6889, 3.5172, 3.2797, 3.5268, 4.4963, 3.5764, 3.3550,\n",
       "         4.0056, 4.0984, 3.8702, 4.0096, 2.5478, 3.1185, 2.8566, 2.9420, 3.0568,\n",
       "         3.4057, 2.8790], grad_fn=<SliceBackward0>),\n",
       " tensor([5.0000, 3.0000, 4.5000, 3.0000, 2.5000, 4.0000, 5.0000, 3.0000, 3.0000,\n",
       "         4.0000, 4.5000, 4.0000, 5.0000, 3.0000, 3.5000, 2.0000, 3.0000, 4.0000,\n",
       "         3.5000, 3.5000]))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[:20], target[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation\n",
    "\n",
    "Mean squared Error (MSE) can be used as loss function. However, it is not a well suited metric to evaluate the model's recommendations. `Recall@k` fits better for evaluating the recommendations done by the model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 651/651 [00:30<00:00, 21.09it/s]\n"
     ]
    }
   ],
   "source": [
    "def create_rec_dict_per_user() -> dict:\n",
    "    \"\"\" Return a dictionnairy, with link predictions for each user.\n",
    "    \n",
    "        The dict keys are userId's and values a list of tuples. The\n",
    "        first tuple value is the predicted rating and the second one\n",
    "        the ground truth (rating). \"\"\"\n",
    "\n",
    "    user_rec_dict = defaultdict(list)\n",
    "    test_users_unique = test_data['user', 'movie'].edge_label_index[0].unique()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for user_id in tqdm(test_users_unique):\n",
    "            user_id = user_id.item()\n",
    "\n",
    "            idxs = (test_data['user', 'movie'].edge_label_index[0] == user_id).nonzero().squeeze()\n",
    "            edge_idxs = test_data['user', 'movie'].edge_label_index[:,idxs]\n",
    "            \n",
    "            true_ratings = test_data['user', 'movie'].edge_label[idxs]\n",
    "            if true_ratings.ndimension() == 0:\n",
    "                true_ratings = test_data['user', 'movie'].edge_label[idxs].unsqueeze(dim=0)\n",
    "\n",
    "            pred_ratings = model(test_data.x_dict, test_data.edge_index_dict, edge_idxs)\n",
    "\n",
    "            ratings = [(pred.item(), gt.item()) for gt, pred in zip(true_ratings, pred_ratings)] \n",
    "            user_rec_dict[user_id] = ratings\n",
    "        \n",
    "        return user_rec_dict\n",
    "\n",
    "\n",
    "def recall_precision_at_k(user_rec_dict:dict, k:int, threshold:float) -> dict:\n",
    "    \"\"\"\" \"\"\"\n",
    "    with torch.no_grad():\n",
    "        precisions = dict()\n",
    "        recalls = dict()\n",
    "\n",
    "\n",
    "        for uid, user_ratings in user_rec_dict.items():\n",
    "            \n",
    "            user_ratings.sort(key=lambda x: x[0], reverse=True) \n",
    "             \n",
    "            n_rel = sum([(true_rating >= threshold) for (_, true_rating) in user_ratings])\n",
    "            n_rec_k = sum([(true_rating >= threshold) for (_, true_rating) in user_ratings[:k]])\n",
    "            n_rel_and_rec_k = sum([((true_r >= threshold) and (est >= threshold)) for (est, true_r) in user_ratings[:k]])\n",
    "            \n",
    "            precisions[uid] = n_rel_and_rec_k / n_rec_k if n_rec_k != 0 else 0\n",
    "            recalls[uid] = n_rel_and_rec_k / n_rel if n_rel != 0 else 0\n",
    "        \n",
    "        return recalls, precisions\n",
    "\n",
    "user_rec_dict = create_rec_dict_per_user()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall@20 (Threshold=4.0): 0.3486\n",
      "Precision@20 (Threshold=4.0): 0.3782\n",
      "\n",
      "Recall@20 (Threshold=3.5): 0.6644\n",
      "Precision@20 (Threshold=3.5): 0.7238\n"
     ]
    }
   ],
   "source": [
    "k = 20\n",
    "threshold = 4.0\n",
    "recalls, precisions = recall_precision_at_k(user_rec_dict, k, threshold)\n",
    "rcl_at_k = sum(recalls.values()) / len(recalls.values())\n",
    "prc_at_k = sum(precisions.values()) / len(precisions.values())\n",
    "\n",
    "print(f'Recall@{k} (Threshold={threshold}):', round(rcl_at_k, 4))\n",
    "print(f'Precision@{k} (Threshold={threshold}):', round(prc_at_k, 4))\n",
    "\n",
    "threshold = 3.5\n",
    "recalls, precisions = recall_precision_at_k(user_rec_dict, k=k, threshold=threshold)\n",
    "rcl_at_k = sum(recalls.values()) / len(recalls.values())\n",
    "prc_at_k = sum(precisions.values()) / len(precisions.values())\n",
    "\n",
    "print(f'\\nRecall@{k} (Threshold={threshold}):', round(rcl_at_k, 4))\n",
    "print(f'Precision@{k} (Threshold={threshold}):', round(prc_at_k, 4))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get new recommendations\n",
    "\n",
    "Get the top recommendended movies for each user and create new relationships `(m:Movie)-[:RECOMMENDED_TO]-> (u:User)` in Neo4j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 671/671 [00:30<00:00, 22.00it/s]\n"
     ]
    }
   ],
   "source": [
    "K = 20\n",
    "movie_recs = []\n",
    "with open('data/encoder_user.pkl', 'rb') as pkl:\n",
    "    user_encoder = pickle.load(pkl)\n",
    "\n",
    "with open('data/encoder_movie.pkl', 'rb') as pkl:  \n",
    "    movie_encoder = pickle.load(pkl)\n",
    "\n",
    "with torch.no_grad():\n",
    "    for user_id in tqdm(range(0, n_users)):\n",
    "        user_row = torch.tensor([user_id] * n_movies)\n",
    "        # break\n",
    "        all_movie_ids = torch.arange(n_movies)\n",
    "        edge_label_index = torch.stack([user_row, all_movie_ids], dim=0)\n",
    "        pred = model(data.x_dict, data.edge_index_dict,\n",
    "                edge_label_index)\n",
    "        pred = pred.clamp(min=0, max=5)\n",
    "        \n",
    "        pred_top_k, pred_top_k_idxs = torch.topk(pred, k=K)\n",
    "        \n",
    "        movie_recs.append({\n",
    "            'user': user_id,\n",
    "            'user_id_neo4j': user_encoder.inverse_transform([user_id])[0],\n",
    "            'rec_movie_ids': pred_top_k_idxs.tolist(), \n",
    "            'rec_movie_ids_neo4j': movie_encoder.inverse_transform(pred_top_k_idxs).tolist(),\n",
    "            'pred_rating': pred_top_k.tolist()})\n",
    "\n",
    "# safe recommendation dictionnariy\n",
    "with open(f'./data/movie_recommendations_{save_date}', 'wb') as fp:\n",
    "    pickle.dump(movie_recs, fp)"
   ]
  }
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