ngcf_pretrained_movielens.ipynb

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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",
       "    </tr>\n",
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       "      <td>2009-10-17 20:25:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating            timestamp\n",
       "0              1        2     3.5  2005-04-02 23:53:47\n",
       "1              1       29     3.5  2005-04-02 23:31:16\n",
       "2              1       32     3.5  2005-04-02 23:33:39\n",
       "3              1       47     3.5  2005-04-02 23:32:07\n",
       "4              1       50     3.5  2005-04-02 23:29:40\n",
       "...          ...      ...     ...                  ...\n",
       "20000258  138493    68954     4.5  2009-11-13 15:42:00\n",
       "20000259  138493    69526     4.5  2009-12-03 18:31:48\n",
       "20000260  138493    69644     3.0  2009-12-07 18:10:57\n",
       "20000261  138493    70286     5.0  2009-11-13 15:42:24\n",
       "20000262  138493    71619     2.5  2009-10-17 20:25:36\n",
       "\n",
       "[20000263 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('/kaggle/input/movielens-20m-dataset/rating.csv')\n",
    "df"
   ]
  },
  {
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   "execution_count": 2,
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   "source": [
    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df = df.sort_values('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "df = df.rename(columns={\"userId\": \"userId:token\", \"movieId\": \"movieId:token\", \"timestamp_int\": \"timestamp:float\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "test_part = 0.2\n",
    "train_len = int(df.shape[0] * (1 - test_part))\n",
    "test_len = df.shape[0] - train_len \n",
    "df_train, df_test = df.drop(['timestamp'], axis=1).head(train_len), df.drop(['timestamp'], axis=1).tail(test_len)"
   ]
  },
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    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId:token</th>\n",
       "      <th>movieId:token</th>\n",
       "      <th>rating</th>\n",
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       "          userId:token  movieId:token  rating\n",
       "4182421          28507           1176     4.0\n",
       "18950979        131160           1079     3.0\n",
       "18950936        131160             47     5.0\n",
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   ],
   "source": [
    "df_train.head()"
   ]
  },
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   "outputs": [],
   "source": [
    "N_USERS = df['userId:token'].max() + 1\n",
    "N_ITEMS = df['movieId:token'].max() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5001a5f4",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:0'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "711463e5",
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   "outputs": [],
   "source": [
    "from math import log2\n",
    "\n",
    "def apk(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred) \n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            cnt += 1\n",
    "            ans += cnt / (i + 1)\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def mapk(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += apk(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def mr(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (i + 1)\n",
    "            s.add(pred[i])\n",
    "            break\n",
    "    return ans\n",
    "\n",
    "def mrr(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += mr(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def ndcgunique(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (log2(i + 2))\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "def ndcg(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += ndcgunique(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def precisionunique(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def precision(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += precisionunique(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) "
   ]
  },
  {
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   "source": [
    "import queue\n",
    "from tqdm.auto import trange\n",
    "\n",
    "\n",
    "class GraphDataset(object):\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "        self.graph = [[] for _ in range(N_USERS + N_ITEMS)]\n",
    "        for i in trange(self.df.shape[0]):\n",
    "            usr, item = self.df['userId:token'].iloc[i], self.df['movieId:token'].iloc[i] + N_USERS\n",
    "            self.graph[usr].append(item)\n",
    "            self.graph[item].append(usr)\n",
    "\n",
    "    \n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        vertexes = set()\n",
    "        edges = []\n",
    "        q = queue.Queue()\n",
    "        q.put((index, 0))\n",
    "        while q.qsize() > 0:\n",
    "            u, d = q.get()\n",
    "            vertexes.add(u)\n",
    "            if d >= 1:\n",
    "                continue\n",
    "            for v0 in self.graph[u]:\n",
    "                if v0 not in vertexes:\n",
    "                    edges.append((u, v0))\n",
    "                    q.put((v0, d + 1))\n",
    "        return list(vertexes), edges\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.graph)\n"
   ]
  },
  {
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     "shell.execute_reply": "2023-01-10T12:19:08.335387Z"
    },
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    "outputId": "e20a60c0-c1fc-4acc-f4cb-bb4f27de53f0",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d934f3b8fb8f4150b4efd33f506a1369",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = GraphDataset(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dca70fc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T12:19:08.357995Z",
     "iopub.status.busy": "2023-01-10T12:19:08.357098Z",
     "iopub.status.idle": "2023-01-10T12:19:08.362729Z",
     "shell.execute_reply": "2023-01-10T12:19:08.361915Z"
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     "start_time": "2023-01-10T12:19:08.348739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sample_graph = dataset[1]\n",
    "#sample_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcc4a005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T12:19:08.380742Z",
     "iopub.status.busy": "2023-01-10T12:19:08.380479Z",
     "iopub.status.idle": "2023-01-10T13:09:17.175668Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in links: https://data.pyg.org/whl/torch-1.11.0.html\r\n",
      "Collecting torch-scatter\r\n",
      "  Downloading torch_scatter-2.1.0.tar.gz (106 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.8/106.8 kB\u001b[0m \u001b[31m828.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: torch-scatter\r\n",
      "  Building wheel for torch-scatter (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-scatter: filename=torch_scatter-2.1.0-cp37-cp37m-linux_x86_64.whl size=4187464 sha256=49aa770ab1ccc213beb37a8ea811e4834989873ea5cfe3050c753afb01cb4d01\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/2d/d1/15/8a2f0086896d156654a843fff4bdbeaf621cdd10310a0daad2\r\n",
      "Successfully built torch-scatter\r\n",
      "Installing collected packages: torch-scatter\r\n",
      "Successfully installed torch-scatter-2.1.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0mLooking in links: https://data.pyg.org/whl/torch-1.11.0.html\r\n",
      "Collecting torch-sparse\r\n",
      "  Downloading torch_sparse-0.6.16.tar.gz (208 kB)\r\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from torch-sparse) (1.7.3)\r\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /opt/conda/lib/python3.7/site-packages (from scipy->torch-sparse) (1.21.6)\r\n",
      "Building wheels for collected packages: torch-sparse\r\n",
      "  Building wheel for torch-sparse (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-sparse: filename=torch_sparse-0.6.16-cp37-cp37m-linux_x86_64.whl size=1743105 sha256=944703ba77125ff1f4003d9e36e4bd7d4f567357c12047c3028f8af13180f7ef\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/92/8d/17/1517bf0318b8343b665b7aadf906dff4bc8967d82cacc17992\r\n",
      "Successfully built torch-sparse\r\n",
      "Installing collected packages: torch-sparse\r\n",
      "Successfully installed torch-sparse-0.6.16\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0mLooking in links: https://data.pyg.org/whl/torch-1.11.0.html\r\n",
      "Collecting torch-cluster\r\n",
      "  Downloading torch_cluster-1.6.0.tar.gz (43 kB)\r\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hBuilding wheels for collected packages: torch-cluster\r\n",
      "  Building wheel for torch-cluster (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-cluster: filename=torch_cluster-1.6.0-cp37-cp37m-linux_x86_64.whl size=1013768 sha256=70bcc28c8d7b96d58bc7df26b7bd35a800f4567f77a4e145aa36df293fc53376\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/bc/c7/3e/258dd72b35d7a459264143ad5bfe97b9dc5eef90069ca2f13f\r\n",
      "Successfully built torch-cluster\r\n",
      "Installing collected packages: torch-cluster\r\n",
      "Successfully installed torch-cluster-1.6.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0mCollecting torch-geometric\r\n",
      "  Downloading torch_geometric-2.2.0.tar.gz (564 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m565.0/565.0 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (4.64.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (1.21.6)\r\n",
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      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (2.28.1)\r\n",
      "Requirement already satisfied: pyparsing in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (3.0.9)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (1.0.2)\r\n",
      "Requirement already satisfied: psutil>=5.8.0 in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (5.9.1)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.7/site-packages (from jinja2->torch-geometric) (2.1.1)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torch-geometric) (3.3)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->torch-geometric) (1.26.13)\r\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->torch-geometric) (2.1.0)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torch-geometric) (2022.12.7)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->torch-geometric) (1.0.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->torch-geometric) (3.1.0)\r\n",
      "Building wheels for collected packages: torch-geometric\r\n",
      "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-geometric: filename=torch_geometric-2.2.0-py3-none-any.whl size=773302 sha256=628989a0a63503d1929ed4424b4da27d8b3438bd7c0627149f293ce78af06d7a\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/10/ac/4c/22ba1900c19bbc0e47b7eaa1e29e03f87fcef772f31aabe680\r\n",
      "Successfully built torch-geometric\r\n",
      "Installing collected packages: torch-geometric\r\n",
      "Successfully installed torch-geometric-2.2.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "!pip install torch-scatter -f https://data.pyg.org/whl/torch-{torch.__version__}.html\n",
    "!pip install torch-sparse -f https://data.pyg.org/whl/torch-{torch.__version__}.html\n",
    "!pip install torch-cluster -f https://data.pyg.org/whl/torch-{torch.__version__}.html\n",
    "!pip install torch-geometric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "deedbe71",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "def renumber_graph(vertexes, edges):\n",
    "    d = {v: i for i, v in enumerate(vertexes)}\n",
    "    new_edges = [(d[u1], d[u2]) for u1, u2 in edges]\n",
    "    new_vertexes = list(range(len(vertexes)))\n",
    "    return new_vertexes, new_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b834132",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T13:09:17.264772Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:0'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13ad3c6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T13:09:17.313812Z",
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   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch_geometric as tg\n",
    "\n",
    "class SHAN(nn.Module):\n",
    "    def __init__(self, n_users, n_items, hidden_dim):\n",
    "        super().__init__()\n",
    "        self.embedding_items = nn.Embedding(n_items, hidden_dim)\n",
    "        self.embedding_users = nn.Embedding(n_users, hidden_dim)\n",
    "        self.additional_info_encoding = nn.Linear(2, hidden_dim)\n",
    "\n",
    "        self.attention_1 = nn.Linear(2 * hidden_dim, 1)\n",
    "        self.attention_2 = nn.Linear(2 * hidden_dim, 1)\n",
    "        self.last_layer = nn.Linear(hidden_dim, n_items)\n",
    "        self.hidden_dim = hidden_dim\n",
    "\n",
    "    def forward(self, history_input, metadata, user_id):\n",
    "        len_long_history = int(history_input.shape[1] * (2/3))\n",
    "        \n",
    "        long_history, short_history = history_input[:, :len_long_history], history_input[:, len_long_history:]\n",
    "        encoded_metadata = self.additional_info_encoding(metadata)\n",
    "        encoded_long_history = self.embedding_items(long_history) + encoded_metadata[:, :len_long_history, :]\n",
    "        encoded_short_history = self.embedding_items(short_history) + encoded_metadata[:, len_long_history:, :]\n",
    "        encoded_user = self.embedding_users(user_id)\n",
    "\n",
    "        encoded_long_user = torch.cat([encoded_user.unsqueeze(1)] * len_long_history, 1)\n",
    "        weights_long = self.attention_1(torch.cat([encoded_long_history, encoded_long_user], 2)).softmax(1)\n",
    "        weights_long = torch.cat([weights_long] * self.hidden_dim, 2)\n",
    "        output_long = torch.sum(weights_long * encoded_long_history, 1).unsqueeze(1)\n",
    "\n",
    "        encoded_short_history = torch.cat([encoded_short_history, output_long], 1)\n",
    "        encoded_short_user = torch.cat([encoded_user.unsqueeze(1)] * encoded_short_history.shape[1], 1)\n",
    "        #print(encoded_short_history.shape, encoded_short_user.shape)\n",
    "        weights_short = self.attention_2(torch.cat([encoded_short_history, encoded_short_user], 2)).softmax(1)\n",
    "        weights_short = torch.cat([weights_short] * self.hidden_dim, 2)\n",
    "        output_short = torch.sum(weights_short * encoded_short_history, 1)\n",
    "    \n",
    "        return self.last_layer(output_short)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42f8b761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T13:09:22.230174Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_pizdim_embedding = torch.load('/kaggle/input/shan-model/model.pt').to(device)\n",
    "for param in model_pizdim_embedding.parameters():\n",
    "    param.requires_grad=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b42cb4b",
   "metadata": {
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    "id": "hfDLntPz4QhI",
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     "duration": 0.033705,
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     "status": "completed"
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    "tags": []
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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch_geometric as tg\n",
    "\n",
    "\n",
    "class NGCF(nn.Module):\n",
    "    def __init__(self, n_vertexes, num_layers, hidden_dim):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(n_vertexes, hidden_dim)\n",
    "        for param in self.embedding.parameters():\n",
    "            param.requires_grad=False\n",
    "\n",
    "        for i in range(model_pizdim_embedding.embedding_users.weight.shape[0]):\n",
    "            self.embedding.weight[i] = model_pizdim_embedding.embedding_users.weight[i]\n",
    "            \n",
    "        for i in range(model_pizdim_embedding.embedding_items.weight.shape[0]):\n",
    "            self.embedding.weight[i + N_USERS] = model_pizdim_embedding.embedding_items.weight[i]\n",
    "            \n",
    "        for param in self.embedding.parameters():\n",
    "            param.requires_grad=False\n",
    "        self.graph_layer = nn.ModuleList([tg.nn.GCNConv(hidden_dim, hidden_dim) for _ in range(num_layers)])\n",
    "        self.relu = nn.ReLU()\n",
    "        self.last = nn.Sequential(\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, vertexes, edges):\n",
    "        reordered_v, reordered_edges = renumber_graph(vertexes, edges)\n",
    "        embeddings = self.embedding(torch.tensor(vertexes).unsqueeze(0).to(device))\n",
    "        edges = torch.LongTensor(reordered_edges).to(device).T\n",
    "        hidden = embeddings\n",
    "        for i in range(len(self.graph_layer)):\n",
    "            hidden = self.graph_layer[i](hidden, edges)\n",
    "            hidden = self.relu(hidden)\n",
    "        return self.last(hidden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8d2e9d6",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = NGCF(N_ITEMS + N_USERS, 2, 64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "criterion = nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d907a21",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45f94fd91a134a8c956053753e0d922f",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vertex2interations = [[] for _ in range(N_USERS + N_ITEMS)]\n",
    "\n",
    "for i in trange(df_train.shape[0]):\n",
    "    usr, item = df_train['userId:token'].iloc[i], df_train['movieId:token'].iloc[i] + N_USERS\n",
    "    rating = df['rating'].iloc[i]\n",
    "    vertex2interations[usr].append((item, rating))\n",
    "    vertex2interations[item].append((usr, rating))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96f20acc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T13:17:12.851265Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c91dff76e7dc4353aa5bba81026988d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c9812167bbd4194b81ae8b43078b875",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/269756 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "\n",
    "epochs = 1\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    for i in trange(1, len(dataset)):\n",
    "        optimizer.zero_grad()\n",
    "        batch = dataset[i]\n",
    "        if len(batch[1]) == 0:\n",
    "            continue\n",
    "        output = model(*batch).squeeze(0)\n",
    "        loss = 0\n",
    "        d = {v: i for i, v in enumerate(batch[0])}\n",
    "        for v, rating in vertex2interations[i]:\n",
    "            inv_rating = 5 - rating\n",
    "            cur_rating = torch.sum((output[d[i]] - output[d[v]]) * (output[d[i]] - output[d[v]]), -1)\n",
    "            loss += (cur_rating - inv_rating) * (cur_rating - inv_rating)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12126d89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:14:48.426472Z",
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     "exception": false,
     "start_time": "2023-01-10T16:14:48.405055",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e0cb1d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:14:48.682306Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from math import log2\n",
    "\n",
    "def apk(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred) \n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            cnt += 1\n",
    "            ans += cnt / (i + 1)\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def mapk(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += apk(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def mr(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (i + 1)\n",
    "            s.add(pred[i])\n",
    "            break\n",
    "    return ans\n",
    "\n",
    "def mrr(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += mr(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def ndcgunique(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (log2(i + 2))\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "def ndcg(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += ndcgunique(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def precisionunique(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def precision(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += precisionunique(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b885defe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:14:48.740263Z",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class FastKNN:\n",
    "    def __init__(self, num_projections = 10):\n",
    "        self.num_projections = num_projections\n",
    "        self.distributions = []\n",
    "        self.projection_matrixes = []\n",
    "        self.embeddings = None\n",
    "\n",
    "    def binary_search(self, index_projection, find_value):\n",
    "        left_index, right_index = 0, len(self.distributions[index_projection]) - 1\n",
    "        while left_index < right_index - 1:\n",
    "            middle_index = (left_index + right_index) // 2\n",
    "            middle_value = self.distributions[index_projection][middle_index][0]\n",
    "            if middle_value < find_value:\n",
    "                left_index = middle_index\n",
    "            else:\n",
    "                right_index = middle_index\n",
    "\n",
    "        return left_index\n",
    "\n",
    "    def find_k_nearest(self, index_projection, find_value, k):\n",
    "        i0 = self.binary_search(index_projection, find_value)\n",
    "        left_index = max(0, i0 - k // 2)\n",
    "        right_index = min(len(self.distributions[index_projection]), left_index + k )\n",
    "        if right_index - left_index != k:\n",
    "            left_index = right_index - k\n",
    "        return [self.distributions[index_projection][i][1] for i in range(left_index, right_index)]\n",
    "\n",
    "    def fit(self, vectors):\n",
    "        for i in trange(self.num_projections):\n",
    "            projection = torch.randn((vectors.shape[1], 1))\n",
    "            values = (vectors @ projection).cpu().numpy().reshape(-1)\n",
    "            self.distributions.append(sorted([(values[j], j) for j in range(len(values))]))\n",
    "            self.projection_matrixes.append(projection.cpu())\n",
    "\n",
    "    def predict(self, vector, k):\n",
    "        k0 = 1 + (k // self.num_projections)\n",
    "        values = [torch.sum(vector * proj.reshape(-1)).item() for proj in self.projection_matrixes]\n",
    "        neighb = []\n",
    "        for i, value in enumerate(values):\n",
    "            cur_n = self.find_k_nearest(i, value, k0)\n",
    "            neighb += cur_n\n",
    "        neighb = list(set(neighb))\n",
    "        return neighb[:k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8553696",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eae607d9798740e5a6622ff91a77048c",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vectors = model.embedding.weight.cpu().detach()[N_USERS:, :]\n",
    "knn = FastKNN()\n",
    "knn.fit(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01b530f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:14:50.590946Z",
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    "outputId": "3ef85ddf-e934-44d0-b638-1b858b1af372",
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     "duration": 78.944356,
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     "exception": false,
     "start_time": "2023-01-10T16:14:50.567576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84b381f782064a5bb2e935528a3d5746",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/4000053 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_test = [[] for i in range(df['userId:token'].max() + 1)]\n",
    "for i in trange(df_test.shape[0]):\n",
    "    usr, item = df_test['userId:token'].iloc[i], df_test['movieId:token'].iloc[i]\n",
    "    g_test[usr].append(item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8a0040b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:16:09.554431Z",
     "iopub.status.busy": "2023-01-10T16:16:09.553591Z",
     "iopub.status.idle": "2023-01-10T16:16:18.711425Z",
     "shell.execute_reply": "2023-01-10T16:16:18.710418Z"
    },
    "id": "t5o-f2fvHGlc",
    "papermill": {
     "duration": 9.181237,
     "end_time": "2023-01-10T16:16:18.713745",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del vertex2interations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a6f7e72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-10T16:16:18.763691Z",
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     "duration": 456.370871,
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     "exception": false,
     "start_time": "2023-01-10T16:16:18.733780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "194547bca83a4e7982d61b3f96584d71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_train = [[] for i in range(df_train['userId:token'].max() + 1)]\n",
    "for i in trange(df_train.shape[0]):\n",
    "    cur_user = df['userId:token'].iloc[i]\n",
    "    cur_item = df['movieId:token'].iloc[i]\n",
    "    cur_rating = df['rating'].iloc[i]\n",
    "    g_train[cur_user].append((cur_rating, cur_item))\n",
    "\n",
    "for i in range(len(g_train)):\n",
    "    g_train[i] = sorted(g_train[i])\n",
    "    g_train[i] = g_train[i][-5:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "991b108f",
   "metadata": {
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    "predictions = []\n",
    "target = []\n",
    "k = 40\n",
    "for i in trange(df_train['userId:token'].max() + 1):\n",
    "    if len(g_test[i]) == 0 or len(g_train[i]) == 0:\n",
    "        continue\n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.predict(vectors[cur_item], k)[:(k // 5)]\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    \n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n"
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       "          ... \n",
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    "for i in range(1, 20):\n",
    "    print(f\"precision@{i} : {precision(predictions, target, i)}\")\n",
    "print(f\"ndcg: {ndcg(predictions, target)}\")\n",
    "print(f\"mrr: {mrr(predictions, target)}\")\n",
    "for i in range(1, 20):\n",
    "    print(f\"mapk@{i} : {mapk(predictions, target, i)}\")\n"
   ]
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