embeddings.ipynb

{
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    "tags": []
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   "source": [
    "# Получаем различные эмбеддинги для вершин"
   ]
  },
  {
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   "id": "892875c5",
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     "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[31m542.7 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=4187466 sha256=a15de06e7899795a4dc27d338275f1a9175b116405d2665c25b610d40d611776\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",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m208.2/208.2 kB\u001b[0m \u001b[31m879.5 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[?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=1743099 sha256=14909ebdcd6beaf14d37bd9a2fef92a3c1d13c1e20db2372827df386a62a004e\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",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.4/43.4 kB\u001b[0m \u001b[31m371.2 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-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=1013770 sha256=e842f18249c9e72f8849fd64ea2e08d73ea0317edba9429493eeec3c66ec074b\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[31m1.6 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",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (1.7.3)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.7/site-packages (from torch-geometric) (3.1.2)\r\n",
      "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: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torch-geometric) (2022.9.24)\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: 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) (1.26.12)\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=60413107584523764c96842fb9cbb78f7a88310472763f6ebf707e63e84f0fcc\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"
   ]
  },
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     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('/kaggle/input/vkcup1/train_mod_v3.csv')\n",
    "df\n"
   ]
  },
  {
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   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)\n",
    "df_test.to_csv('test_mod_v3.csv')\n",
    "del df_test \n",
    "#del df\n"
   ]
  },
  {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>t</th>\n",
       "      <th>h</th>\n",
       "      <th>mask2drop</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <td>36</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
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       "      <th>16745086</th>\n",
       "      <td>17414504</td>\n",
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       "      <td>7</td>\n",
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       "  </tbody>\n",
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       "<p>16745091 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0        u        v   t  h  mask2drop\n",
       "0                  0        0        1  27  6          1\n",
       "1                  1        2        3  13  8          1\n",
       "2                  2        4        5  74  2          1\n",
       "3                  3        6        7  36  9          1\n",
       "4                  4        8        9  52  2          1\n",
       "...              ...      ...      ...  .. ..        ...\n",
       "16745086    17414504  2369189  2300645   6  7          1\n",
       "16745087    17414505    62448    88256  60  0          1\n",
       "16745088    17414506   919913  1183054  71  0          1\n",
       "16745089    17414507     1774      881  67  0          1\n",
       "16745090    17414509   407711   861430  26  7          1\n",
       "\n",
       "[16745091 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_TOTAL = max(df['u'].max(), df['v'].max()) + 1\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92a57d31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:00:32.837859Z",
     "iopub.status.busy": "2022-12-24T11:00:32.837481Z",
     "iopub.status.idle": "2022-12-24T11:00:32.852590Z",
     "shell.execute_reply": "2022-12-24T11:00:32.851517Z"
    },
    "id": "WGSemC5up2AR",
    "papermill": {
     "duration": 0.050655,
     "end_time": "2022-12-24T11:00:32.856186",
     "exception": false,
     "start_time": "2022-12-24T11:00:32.805531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def apk(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = min(len(target), k) \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",
    "def recall_k(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        num_matchings = 0\n",
    "        for cur_pred_elem in cur_pred:\n",
    "            for cur_target_elem in cur_target:\n",
    "                if cur_pred_elem == cur_target_elem:\n",
    "                    num_matchings += 1\n",
    "                    break\n",
    "        sum_metric += num_matchings / len(cur_pred)\n",
    "    return sum_metric / len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ba71d1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:00:32.915412Z",
     "iopub.status.busy": "2022-12-24T11:00:32.915075Z",
     "iopub.status.idle": "2022-12-24T11:01:40.404319Z",
     "shell.execute_reply": "2022-12-24T11:01:40.403282Z"
    },
    "id": "N67eOLXRroNY",
    "papermill": {
     "duration": 67.521123,
     "end_time": "2022-12-24T11:01:40.406936",
     "exception": false,
     "start_time": "2022-12-24T11:00:32.885813",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_csv('test_mod_v3.csv')\n",
    "\n",
    "target_arr = [[] for _ in range(N_TOTAL)]\n",
    "\n",
    "for i in range(df_test.shape[0]):\n",
    "    u, v = df_test['u'].iloc[i], df_test['v'].iloc[i]\n",
    "    if u >= N_TOTAL or v >= N_TOTAL:\n",
    "        continue\n",
    "    target_arr[u].append(v)\n",
    "    target_arr[v].append(u)\n",
    "del df_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b24fb2",
   "metadata": {
    "id": "_FfsOhvopWlS",
    "papermill": {
     "duration": 0.019144,
     "end_time": "2022-12-24T11:01:40.446251",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.427107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Part 1. Учим SLIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c3b9e68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:01:40.485207Z",
     "iopub.status.busy": "2022-12-24T11:01:40.484900Z",
     "iopub.status.idle": "2022-12-24T11:01:40.492938Z",
     "shell.execute_reply": "2022-12-24T11:01:40.491972Z"
    },
    "id": "Bj95HR_3oNP0",
    "papermill": {
     "duration": 0.02985,
     "end_time": "2022-12-24T11:01:40.494930",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.465080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "\n",
    "\n",
    "class EdgeDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df, p, lim_iterations):\n",
    "        super().__init__()\n",
    "        self.df = df\n",
    "        self.p = p\n",
    "        self.lim_iterations = lim_iterations\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        u, v, target = -1, -1, 0\n",
    "        if random.random() < self.p:\n",
    "            ind0 = random.randint(0, self.df.shape[0] - 1)\n",
    "            u, v = self.df['u'].iloc[ind0], self.df['v'].iloc[ind0]\n",
    "            target = 1\n",
    "        else:\n",
    "            u, v = random.randint(0, N_TOTAL - 1), random.randint(0, N_TOTAL - 1)\n",
    "        return torch.LongTensor([u, v, target])\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.lim_iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "407620dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:01:40.534529Z",
     "iopub.status.busy": "2022-12-24T11:01:40.533773Z",
     "iopub.status.idle": "2022-12-24T11:01:40.538727Z",
     "shell.execute_reply": "2022-12-24T11:01:40.537908Z"
    },
    "id": "rmNS_ZZTvxAU",
    "papermill": {
     "duration": 0.026588,
     "end_time": "2022-12-24T11:01:40.540621",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.514033",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = EdgeDataset(df_train, 0.4, N_TOTAL)\n",
    "loader = torch.utils.data.DataLoader(dataset, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7d5d0a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:01:40.579778Z",
     "iopub.status.busy": "2022-12-24T11:01:40.579115Z",
     "iopub.status.idle": "2022-12-24T11:01:40.586635Z",
     "shell.execute_reply": "2022-12-24T11:01:40.585748Z"
    },
    "id": "rWaZiigxwHzK",
    "papermill": {
     "duration": 0.029072,
     "end_time": "2022-12-24T11:01:40.588499",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.559427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SLIM(nn.Module):\n",
    "    def __init__(self, hidden_dim = 64):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(N_TOTAL, hidden_dim)\n",
    "\n",
    "    def forward(self, input): \n",
    "        input_embeddings = self.embedding(input) #b, 2, h\n",
    "        u, v = input_embeddings[:, 0, :], input_embeddings[:, 1, :]\n",
    "        return torch.sum(u * v, -1), input_embeddings\n",
    "         \n",
    "\n",
    "class SLIM_Loss(nn.Module):\n",
    "    def __init__(self, b, l):\n",
    "        super().__init__()\n",
    "        self.b = b\n",
    "        self.l = l\n",
    "        self.mse = nn.MSELoss()\n",
    "        self.mae = nn.L1Loss()\n",
    "\n",
    "    def forward(self, output, embeddings, target):\n",
    "        return self.mse(output + 0.0, target + 0.0) + self.b * self.mse(embeddings + 0.0,  embeddings  + 0.0) + self.b * self.mae(embeddings + 0.0, embeddings + 0.0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aec2ef80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:01:40.628678Z",
     "iopub.status.busy": "2022-12-24T11:01:40.627841Z",
     "iopub.status.idle": "2022-12-24T11:01:40.654817Z",
     "shell.execute_reply": "2022-12-24T11:01:40.653990Z"
    },
    "id": "HO0vjJVZ2uFr",
    "outputId": "9259f976-9520-4b6a-8c27-486623d7272b",
    "papermill": {
     "duration": 0.04925,
     "end_time": "2022-12-24T11:01:40.656631",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.607381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92690459",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:01:40.696831Z",
     "iopub.status.busy": "2022-12-24T11:01:40.695966Z",
     "iopub.status.idle": "2022-12-24T11:01:44.802336Z",
     "shell.execute_reply": "2022-12-24T11:01:44.801377Z"
    },
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    "papermill": {
     "duration": 4.128747,
     "end_time": "2022-12-24T11:01:44.804855",
     "exception": false,
     "start_time": "2022-12-24T11:01:40.676108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "criterion = SLIM_Loss(0.01, 0.01)\n",
    "model = SLIM().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb39b389",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-12-24T11:53:48.376916Z"
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    "id": "psaWnR9ozgLI",
    "papermill": {
     "duration": 3123.558636,
     "end_time": "2022-12-24T11:53:48.383065",
     "exception": false,
     "start_time": "2022-12-24T11:01:44.824429",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5612500314c8497e882cec4aea6b436d",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8891593b45594ed0b91abe693728a347",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([128, 1])) that is different to the input size (torch.Size([128])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n",
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([106, 1])) that is different to the input size (torch.Size([106])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "344d4d2e8ca04b6bb9a01be61fc9e526",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "922232303f8341228d1ebfe18a8272ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5409a08c32a84766b2abed92d8a73ea6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec91c09e4dcb4e2dbd8ac7d8a68593ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "EPOCHS = 5\n",
    "losses = []\n",
    "\n",
    "\n",
    "for _ in trange(EPOCHS):\n",
    "    pbar = tqdm(loader)\n",
    "    for batch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        batch = batch.to(device)\n",
    "        edge, target = batch[:, :2], batch[:, 2:]\n",
    "        output, embeddings = model(edge)\n",
    "        loss = criterion(output, embeddings, target)\n",
    "        pbar.set_description(f\"Train loss: {loss.item()}\")\n",
    "        loss.backward()\n",
    "        losses.append(loss.item())\n",
    "        optimizer.step() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10a73aad",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, \"slim.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8791845",
   "metadata": {
    "id": "qQCltrcK-VpM",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51d9c1c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T11:53:50.282275Z",
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     "shell.execute_reply": "2022-12-24T11:53:50.290001Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"slim_losses.txt\", 'wb') as f:\n",
    "    pickle.dump(losses, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d97adc4",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import queue\n",
    "import random\n",
    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "\n",
    "def split_dataset(df_edges, n_parts):\n",
    "    edge2pos = {(df_edges['u'].iloc[i], df_edges['v'].iloc[i]): i for i in trange(df_edges.shape[0])}\n",
    "\n",
    "    G = [[] for i in range(N_TOTAL)]\n",
    "    for i in trange(df_edges.shape[0]):\n",
    "        u, v = df_edges['u'].iloc[i], df_edges['v'].iloc[i]\n",
    "        G[u].append(v)\n",
    "        G[v].append(u)\n",
    "    \n",
    "    used = set()\n",
    "    q = queue.Queue()\n",
    "    for i in range(n_parts):\n",
    "        u = random.randint(0, N_TOTAL - 1)\n",
    "        used.add(u)\n",
    "        q.put((u, i))\n",
    "\n",
    "    while q.qsize() > 0:\n",
    "        cur_v, cur_marker = q.get()\n",
    "        for u in G[cur_v]:\n",
    "            cur_edge_id = edge2pos[(u, cur_v)] if (u, cur_v) in edge2pos.keys() else edge2pos[(cur_v, u)]\n",
    "            df_edges['mask2drop'].iloc[cur_edge_id] = cur_marker\n",
    "            if u not in used:\n",
    "                q.put((u, cur_marker))\n",
    "                used.add(u)\n",
    "    return df_edges\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b59dfaa9",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import random\n",
    "from networkx import ego_graph\n",
    "from tqdm.auto import trange\n",
    "\n",
    "\n",
    "class GraphSampler:\n",
    "    def __init__(self, df, N_PARTS, number_same_graph = 10000, number_same_graph2 = 70000):\n",
    "        self.df = split_dataset(df, N_PARTS)\n",
    "        self.N_PARTS = N_PARTS\n",
    "        self.number_same_graph = number_same_graph\n",
    "        self.number_same_graph2 = number_same_graph2\n",
    "        self.DF_TRAIN = []\n",
    "        self.iteration = 0\n",
    "        df1 = df\n",
    "        for i in range(N_PARTS):\n",
    "            df_cur = self.df[self.df['mask2drop'] == i]\n",
    "            self.DF_TRAIN.append(df_cur)\n",
    "        self.cur_graph = nx.Graph()\n",
    "\n",
    "        self.edges_set = [set() for i in range(N_TOTAL)]\n",
    "        for i in trange(self.df.shape[0]):\n",
    "            u, v = self.df['u'].iloc[i], self.df['v'].iloc[i]\n",
    "            self.edges_set[u].add(v)\n",
    "            self.edges_set[v].add(u)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        del self.cur_graph\n",
    "        self.cur_graph = nx.Graph()\n",
    "        self.cur_graph.add_nodes_from(list(range(N_TOTAL)))\n",
    "        self.cur_graph.add_edges_from(self.DF_TRAIN[index][['u', 'v']].values)\n",
    "\n",
    "    def get_random_subgraph(self, size_graph):\n",
    "        mask = np.random.choice(self.indices, size=size_graph)\n",
    "        return self.cur_graph.subgraph(self.indices[mask])\n",
    "\n",
    "    def get_random_connected(self, thr=10):\n",
    "        v0 = random.randint(0, N_TOTAL - 1)\n",
    "        g0 = ego_graph(self.cur_graph, n=v0, radius=1)\n",
    "        if len(g0.nodes) < thr:\n",
    "            g0 = ego_graph(self.cur_graph, n=v0, radius=2)\n",
    "        return g0\n",
    "\n",
    "    def generate_random_graph(self, p_connected, size_graph, thr=10):\n",
    "        if self.iteration % self.number_same_graph == 0:\n",
    "            self.__getitem__(random.randint(0, self.N_PARTS - 1))\n",
    "        self.iteration += 1\n",
    "        if random.random() < p_connected:\n",
    "            return self.get_random_connected(thr)\n",
    "        else:\n",
    "            return self.get_random_subgraph(size_graph)\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def inference_model(self, model, num_samples_big, degree_threshold, diameter, K, lim2evaluate = None):\n",
    "        sum_apk, cnt_apk = 0, 1e-9\n",
    "        n_vertexes = lim2evaluate if lim2evaluate is not None else N_TOTAL\n",
    "        for i in trange(n_vertexes):\n",
    "            if len(target_arr[i]) == 0:\n",
    "                continue\n",
    "            if self.iteration % self.number_same_graph2 == 0:\n",
    "                self.__getitem__(random.randint(0, self.N_PARTS - 1))\n",
    "            self.iteration += 1\n",
    "            recommendations = []\n",
    "            if len(self.edges_set[i]) >= degree_threshold:\n",
    "                g0 = ego_graph(self.cur_graph, n=i, radius=diameter)\n",
    "                vertexes = list(g0.nodes)\n",
    "                id2pos = {val: pos for pos, val in enumerate(vertexes)}\n",
    "                test_edges = []\n",
    "            \n",
    "                for v0 in vertexes:\n",
    "                    if v0 not in self.edges_set[i] and i not in self.edges_set[v0]:\n",
    "                        test_edges.append((i, v0))\n",
    "                    \n",
    "                output = model(g0)\n",
    "                pred_logits = [(torch.sum(output[id2pos[v0]] * output[id2pos[v1]]).item(), ind)\n",
    "                           for ind, (v0, v1) in enumerate(test_edges)]\n",
    "                pred_logits = sorted(pred_logits)[::-1][:K]\n",
    "                recommendations = [ind for _, ind in pred_logits]\n",
    "            else:\n",
    "                g0 = nx.Graph()\n",
    "                g0.add_node(i)\n",
    "                for _ in range(num_samples_big):\n",
    "                     g0.add_node(random.randint(0, N_TOTAL - 1))\n",
    "                vertexes = list(g0.nodes)\n",
    "                id2pos = {val: pos for pos, val in enumerate(vertexes)}\n",
    "                test_edges = []\n",
    "            \n",
    "                for v0 in vertexes:\n",
    "                    if v0 not in self.edges_set[i] and i not in self.edges_set[v0]:\n",
    "                        test_edges.append((i, v0))\n",
    "                    \n",
    "                output = model(g0)\n",
    "                pred_logits = [(torch.sum(output[id2pos[v0]] * output[id2pos[v1]]).item(), ind)\n",
    "                           for ind, (v0, v1) in enumerate(test_edges)]\n",
    "                pred_logits = sorted(pred_logits)[::-1][:K]\n",
    "                recommendations = [ind for _, ind in pred_logits]\n",
    "            \n",
    "            sum_apk += apk(recommendations, target_arr[i], K)\n",
    "            cnt_apk += 1\n",
    "        return sum_apk / cnt_apk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21dbe088",
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    {
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       "  0%|          | 0/13396072 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "graph_manager = GraphSampler(df_train, 60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "335bc6e3",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "from torch_geometric.utils.convert import to_networkx, from_networkx\n",
    "\n",
    "class ModelAdapter(nn.Module):\n",
    "    def __init__(self, init_model):\n",
    "        super().__init__()\n",
    "        self.init_model = init_model\n",
    "    \n",
    "    def forward(self, graph):\n",
    "        input_data = from_networkx(graph)\n",
    "        input_vertexes = torch.tensor(list(graph.nodes)).to(device)\n",
    "        hidden = self.init_model.embedding(input_vertexes)\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a10529a",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6523e59334d74a969f46096af6876e3f",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/15000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "1.413635030694818e-05"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adapter = ModelAdapter(model).to(device)\n",
    "graph_manager.inference_model(model_adapter, 50, 20, 3, 30, 15000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416d26d3",
   "metadata": {
    "id": "iaYWu6-ELQa3",
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    },
    "tags": []
   },
   "source": [
    "## ALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ad11c0f",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ALS_Loss(nn.Module):\n",
    "    def __init__(self, l):\n",
    "        super().__init__()\n",
    "        self.l = l\n",
    "        self.mse = nn.MSELoss()\n",
    "\n",
    "    def forward(self, output, embeddings, target):\n",
    "        return self.mse(output + 0.0, target + 0.0) + self.l * self.mse(embeddings + 0.0,  embeddings  + 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea1a8c03",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "criterion = ALS_Loss(0.01)\n",
    "model = SLIM(64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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   "outputs": [
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([128, 1])) that is different to the input size (torch.Size([128])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n",
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([106, 1])) that is different to the input size (torch.Size([106])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "444cbae427bf4408b1944fb93e726e85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aee5d7e8ddc43f29b8d3339346981b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "EPOCHS = 5\n",
    "losses = []\n",
    "\n",
    "\n",
    "for _ in trange(EPOCHS):\n",
    "    pbar = tqdm(loader)\n",
    "    for batch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        batch = batch.to(device)\n",
    "        edge, target = batch[:, :2], batch[:, 2:]\n",
    "        output, embeddings = model(edge)\n",
    "        loss = criterion(output, embeddings, target)\n",
    "        pbar.set_description(f\"Train loss: {loss.item()}\")\n",
    "        loss.backward()\n",
    "        losses.append(loss.item())\n",
    "        optimizer.step() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20afe4d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:00.030506Z",
     "iopub.status.busy": "2022-12-24T13:38:00.030182Z",
     "iopub.status.idle": "2022-12-24T13:38:02.221313Z",
     "shell.execute_reply": "2022-12-24T13:38:02.220017Z"
    },
    "id": "jIKWugahR2ee",
    "papermill": {
     "duration": 2.218054,
     "end_time": "2022-12-24T13:38:02.225155",
     "exception": false,
     "start_time": "2022-12-24T13:38:00.007101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "torch.save(model, \"als.pt\")\n",
    "with open(\"als_losses.txt\", 'wb') as f:\n",
    "    pickle.dump(losses, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88edcf16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:02.486274Z",
     "iopub.status.busy": "2022-12-24T13:38:02.485839Z",
     "iopub.status.idle": "2022-12-24T13:38:38.162694Z",
     "shell.execute_reply": "2022-12-24T13:38:38.161630Z"
    },
    "id": "AlB0pWOcMhtt",
    "papermill": {
     "duration": 35.792301,
     "end_time": "2022-12-24T13:38:38.166449",
     "exception": false,
     "start_time": "2022-12-24T13:38:02.374148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7dc8f9fb4c349309e82eebb22f9fb61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.079299965890347e-05"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adapter = ModelAdapter(model).to(device)\n",
    "graph_manager.inference_model(model_adapter, 50, 20, 3, 30, 15000)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0a6432",
   "metadata": {
    "id": "1FFdlL2PSP-t",
    "papermill": {
     "duration": 0.022657,
     "end_time": "2022-12-24T13:38:38.212360",
     "exception": false,
     "start_time": "2022-12-24T13:38:38.189703",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## implicit ALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a29fd5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:38.257695Z",
     "iopub.status.busy": "2022-12-24T13:38:38.257399Z",
     "iopub.status.idle": "2022-12-24T13:38:51.787120Z",
     "shell.execute_reply": "2022-12-24T13:38:51.786118Z"
    },
    "id": "7UwhyQoPR04t",
    "papermill": {
     "duration": 13.555365,
     "end_time": "2022-12-24T13:38:51.789808",
     "exception": false,
     "start_time": "2022-12-24T13:38:38.234443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "degrees = np.zeros(N_TOTAL + 1) + 0.5\n",
    "for u in df_train['u'].values:\n",
    "    degrees[u] += 1\n",
    "for u in df_train['v'].values:\n",
    "    degrees[u] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "851aef18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:51.837094Z",
     "iopub.status.busy": "2022-12-24T13:38:51.836790Z",
     "iopub.status.idle": "2022-12-24T13:38:52.216255Z",
     "shell.execute_reply": "2022-12-24T13:38:52.215391Z"
    },
    "id": "Vzi-s9dsTuU9",
    "outputId": "484cdd6c-7c68-48c7-92ec-540e89a5d738",
    "papermill": {
     "duration": 0.405206,
     "end_time": "2022-12-24T13:38:52.218540",
     "exception": false,
     "start_time": "2022-12-24T13:38:51.813334",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "uniq, counts = np.unique(degrees[degrees > 0], return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.hist(np.log(counts), bins=20)\n",
    "plt.xlabel(\"Node's degree\")\n",
    "plt.ylabel(\"log of counts\")\n",
    "plt.title(\"Distribution of node's degree\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c35abb4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:52.266014Z",
     "iopub.status.busy": "2022-12-24T13:38:52.265731Z",
     "iopub.status.idle": "2022-12-24T13:38:52.272534Z",
     "shell.execute_reply": "2022-12-24T13:38:52.271549Z"
    },
    "id": "RwT9SNjQT3d0",
    "papermill": {
     "duration": 0.032821,
     "end_time": "2022-12-24T13:38:52.274708",
     "exception": false,
     "start_time": "2022-12-24T13:38:52.241887",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ImplicitALS_Loss(nn.Module):\n",
    "    def __init__(self, l, degrees):\n",
    "        super().__init__()\n",
    "        self.l = l\n",
    "        self.degrees = degrees\n",
    "        self.mse = nn.MSELoss()\n",
    "\n",
    "    def forward(self, output, embeddings, target, vertexes):\n",
    "        cur_c = torch.tensor([self.degrees[vertexes[i][0].item()] + self.degrees[vertexes[i][1].item()] for i in range(vertexes.shape[0])]).to(device)\n",
    "        return torch.mean(cur_c * torch.sum((output + 0.0 - target) ** 2, -1)) + self.l * self.mse(embeddings + 0.0,  embeddings  + 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1685230d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:52.321580Z",
     "iopub.status.busy": "2022-12-24T13:38:52.320776Z",
     "iopub.status.idle": "2022-12-24T13:38:53.783331Z",
     "shell.execute_reply": "2022-12-24T13:38:53.782140Z"
    },
    "id": "tuAO5QQwdD4u",
    "papermill": {
     "duration": 1.488368,
     "end_time": "2022-12-24T13:38:53.785813",
     "exception": false,
     "start_time": "2022-12-24T13:38:52.297445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "criterion = ImplicitALS_Loss(10, degrees)\n",
    "model = SLIM(64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fd9598bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T13:38:53.834499Z",
     "iopub.status.busy": "2022-12-24T13:38:53.832912Z",
     "iopub.status.idle": "2022-12-24T14:36:51.059116Z",
     "shell.execute_reply": "2022-12-24T14:36:51.058147Z"
    },
    "id": "2NKW-SK3dLGj",
    "outputId": "f29a442c-d23e-4d20-abf3-4947a06613b0",
    "papermill": {
     "duration": 3477.251887,
     "end_time": "2022-12-24T14:36:51.061130",
     "exception": false,
     "start_time": "2022-12-24T13:38:53.809243",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cbedf01a9334a0db9e4516b22733518",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dc0b5fec8364548b8253319a53a2bd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3a3fedf0ebb48838d54903751ade17d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd08b29a56c04c26b5baa064419d4e1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bf1ec2d063f4a10b707176adff97b2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01ff7f243f05400bb609654aba8c8808",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19707 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "EPOCHS = 5\n",
    "losses = []\n",
    "\n",
    "\n",
    "for _ in trange(EPOCHS):\n",
    "    pbar = tqdm(loader)\n",
    "    for batch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        batch = batch.to(device)\n",
    "        edge, target = batch[:, :2], batch[:, 2:]\n",
    "        output, embeddings = model(edge)\n",
    "        loss = criterion(output, embeddings, target, edge)\n",
    "        pbar.set_description(f\"Train loss: {loss.item()}\")\n",
    "        loss.backward()\n",
    "        losses.append(loss.item())\n",
    "        optimizer.step() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49592758",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T14:36:51.111480Z",
     "iopub.status.busy": "2022-12-24T14:36:51.110102Z",
     "iopub.status.idle": "2022-12-24T14:36:53.106487Z",
     "shell.execute_reply": "2022-12-24T14:36:53.105129Z"
    },
    "id": "6K_ThHA4drve",
    "papermill": {
     "duration": 2.025801,
     "end_time": "2022-12-24T14:36:53.111393",
     "exception": false,
     "start_time": "2022-12-24T14:36:51.085592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "torch.save(model, \"implicitals.pt\")\n",
    "with open(\"implicitals_losses.txt\", 'wb') as f:\n",
    "    pickle.dump(losses, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "31ac1b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T14:36:53.280821Z",
     "iopub.status.busy": "2022-12-24T14:36:53.280193Z",
     "iopub.status.idle": "2022-12-24T14:37:24.598543Z",
     "shell.execute_reply": "2022-12-24T14:37:24.597478Z"
    },
    "id": "oMZj44sAdOeJ",
    "outputId": "3104735c-2dc1-4687-9594-afca635cb966",
    "papermill": {
     "duration": 31.411694,
     "end_time": "2022-12-24T14:37:24.602208",
     "exception": false,
     "start_time": "2022-12-24T14:36:53.190514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b09deac90c34d9f9be8d564bc303472",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "6.910477537497069e-06"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adapter = ModelAdapter(model).to(device)\n",
    "graph_manager.inference_model(model_adapter, 50, 20, 3, 30, 15000)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dce50136",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T14:37:24.651625Z",
     "iopub.status.busy": "2022-12-24T14:37:24.650728Z",
     "iopub.status.idle": "2022-12-24T14:37:24.864324Z",
     "shell.execute_reply": "2022-12-24T14:37:24.863379Z"
    },
    "id": "xiRstJgBdqWX",
    "outputId": "f48babcc-9e56-4ad8-c376-831e6c12b709",
    "papermill": {
     "duration": 0.24028,
     "end_time": "2022-12-24T14:37:24.866622",
     "exception": false,
     "start_time": "2022-12-24T14:37:24.626342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mean_window(arr, window_size):\n",
    "    answers = []\n",
    "    for i in range(len(arr) // window_size - 1):\n",
    "        start_ind = i * window_size\n",
    "        end_ind = start_ind + window_size\n",
    "        answers.append(sum(arr[start_ind:end_ind]) / window_size)\n",
    "    return answers\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, figsize=(10, 10))\n",
    "cur_losses = mean_window(losses, 100)\n",
    "plt.plot(np.arange(len(cur_losses)), cur_losses)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569a7ee1",
   "metadata": {
    "id": "xg0Da8Fwg-ys",
    "papermill": {
     "duration": 0.024354,
     "end_time": "2022-12-24T14:37:24.916156",
     "exception": false,
     "start_time": "2022-12-24T14:37:24.891802",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## SVD++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bf7ef421",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-24T14:37:24.966819Z",
     "iopub.status.busy": "2022-12-24T14:37:24.966533Z",
     "iopub.status.idle": "2022-12-24T14:37:24.973216Z",
     "shell.execute_reply": "2022-12-24T14:37:24.972270Z"
    },
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   "source": [
    "class SVDPlusPlus(nn.Module):\n",
    "    def __init__(self, hidden_dim = 64):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(N_TOTAL, hidden_dim)\n",
    "        self.embeddings_b = nn.Embedding(N_TOTAL, 1)\n",
    "\n",
    "\n",
    "    def forward(self, input):\n",
    "        input_embeddings = self.embedding(input) #b, 2, h\n",
    "        input_b = self.embeddings_b(input)\n",
    "        u, v = input_embeddings[:, 0, :], input_embeddings[:, 1, :]\n",
    "        u_b, v_b = input_b[:, 0, 0], input_b[:, 1, 0]\n",
    "        return u_b + v_b + torch.sum(u * v, -1), input_embeddings, input_b\n"
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    "class SVDPlusPlusLoss(nn.Module):\n",
    "    def __init__(self, b):\n",
    "        super().__init__()\n",
    "        self.b = b\n",
    "        self.mse = nn.MSELoss()\n",
    "\n",
    "    def forward(self, output, embeddings, embeddings_b, target):\n",
    "        return self.mse(output + 0.0, target + 0.0) + self.b * (self.mse(embeddings + 0.0,  embeddings  + 0.0) + self.mse(embeddings_b + 0.0, embeddings_b + 0.0))"
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   "source": [
    "criterion = SVDPlusPlusLoss(0.1)\n",
    "model = SVDPlusPlus(64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) "
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    "from tqdm.auto import tqdm, trange\n",
    "\n",
    "EPOCHS = 5\n",
    "losses = []\n",
    "\n",
    "\n",
    "for _ in trange(EPOCHS):\n",
    "    pbar = tqdm(loader)\n",
    "    for batch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        batch = batch.to(device)\n",
    "        edge, target = batch[:, :2], batch[:, 2:]\n",
    "        output, embeddings, embeddings_b = model(edge)\n",
    "        loss = criterion(output, embeddings, embeddings_b, target)\n",
    "        pbar.set_description(f\"Train loss: {loss.item()}\")\n",
    "        loss.backward()\n",
    "        losses.append(loss.item())\n",
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    "import pickle\n",
    "\n",
    "\n",
    "torch.save(model, \"svdplusplus.pt\")\n",
    "with open(\"svdplusplus_losses.txt\", 'wb') as f:\n",
    "    pickle.dump(losses, f)"
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