dssm_vk_cup.ipynb

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     "text": [
      "Looking in links: https://data.pyg.org/whl/torch-1.11.0+cpu.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[31m273.3 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 \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-scatter: filename=torch_scatter-2.1.0-cp37-cp37m-linux_x86_64.whl size=318664 sha256=35c9bbd138b3ee5c9c1a76a1a9e756f945e3a611bcd51f35f19696611767b32d\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+cpu.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[31m460.8 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",
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      "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 \bdone\r\n",
      "\u001b[?25h  Created wheel for torch-sparse: filename=torch_sparse-0.6.16-cp37-cp37m-linux_x86_64.whl size=625437 sha256=53d0a16b5a9a15494334b614b3881911bb88e84409d0294595ef4b383505bdfc\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+cpu.html\r\n",
      "Collecting torch-cluster\r\n",
      "  Downloading https://data.pyg.org/whl/torch-1.11.0%2Bcpu/torch_cluster-1.6.0-cp37-cp37m-linux_x86_64.whl (317 kB)\r\n",
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      "\u001b[?25hInstalling 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",
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      "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: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->torch-geometric) (3.1.0)\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",
      "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=a1af8fb219c2567aff90dfd379d9e68ae0bf02594865d29548754814e0d57021\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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   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('../input/vkcup1/train_mod_v3.csv')\n",
    "df"
   ]
  },
  {
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   "execution_count": 3,
   "id": "8ee4f6ab",
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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"
   ]
  },
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745086</th>\n",
       "      <td>17414504</td>\n",
       "      <td>2369189</td>\n",
       "      <td>2300645</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745087</th>\n",
       "      <td>17414505</td>\n",
       "      <td>62448</td>\n",
       "      <td>88256</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745088</th>\n",
       "      <td>17414506</td>\n",
       "      <td>919913</td>\n",
       "      <td>1183054</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745089</th>\n",
       "      <td>17414507</td>\n",
       "      <td>1774</td>\n",
       "      <td>881</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745090</th>\n",
       "      <td>17414509</td>\n",
       "      <td>407711</td>\n",
       "      <td>861430</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47042848",
   "metadata": {
    "execution": {
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   "source": [
    "device = 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8c96923",
   "metadata": {
    "execution": {
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   },
   "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 = df\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",
    "            df1, df_cur = train_test_split(df1, test_size = int(len(self.df) / N_PARTS), random_state=42)\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",
    "\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=60):\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 get_random_connected_fixed_start(self, v0, thr=60):\n",
    "        i = 0\n",
    "        while v0 not in self.cur_graph.nodes:\n",
    "            self.__getitem__(i)\n",
    "            i += 1\n",
    "        g0 = ego_graph(self.cur_graph, n=v0, radius=1)\n",
    "        return g0\n",
    "\n",
    "    def generate_random_graph(self, p_connected, size_graph, thr=60):\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": 7,
   "id": "8ac81f8a",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "  0%|          | 0/13396072 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "graph_manager = GraphSampler(df_train, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f15ee5c3",
   "metadata": {
    "execution": {
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   "source": [
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch_geometric as tg\n",
    "from torch_geometric.utils.convert import to_networkx, from_networkx\n",
    "\n",
    "class DSSM(nn.Module):\n",
    "    def __init__(self, num_vertexes, embedding_dim, hidden_dim, num_layers):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(num_vertexes, embedding_dim)\n",
    "        self.layers = nn.ModuleList([])\n",
    "        self.layers.append(nn.Linear(embedding_dim, hidden_dim))\n",
    "        self.relu = nn.ReLU()\n",
    "        for i in range(num_layers):\n",
    "            self.layers.append(nn.Linear(hidden_dim, hidden_dim))\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, graph):\n",
    "        input_data = from_networkx(graph)\n",
    "        input_vertexes = torch.tensor(list(graph.nodes)).to(device)\n",
    "        hidden = self.embedding(input_vertexes)\n",
    "        for layer in self.layers:\n",
    "            hidden = layer(hidden)\n",
    "            hidden = self.relu(hidden)\n",
    "        return self.last(hidden)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b88ef96b",
   "metadata": {
    "execution": {
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    {
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      "text/plain": [
       "[array([ 2.10035592e-03, -5.30206785e-02, -1.45587057e-01, -3.78282741e-02,\n",
       "        -5.43446019e-02, -1.24613531e-02,  1.23851463e-01, -1.37026072e-01,\n",
       "         3.86417620e-02, -1.18383691e-02, -1.04333445e-01, -4.75835428e-02,\n",
       "        -2.93575786e-02,  6.56702071e-02,  1.39514089e-01, -1.34952828e-01,\n",
       "        -2.57464312e-03, -1.38507605e-01,  1.40072107e-02, -9.50008556e-02,\n",
       "         1.41775887e-02, -1.49391638e-02, -5.01794815e-02, -7.44022056e-02,\n",
       "        -5.79805449e-02, -6.30807206e-02,  8.49188641e-02,  3.14729661e-02,\n",
       "        -1.92264766e-02,  1.11878134e-01, -1.96006093e-02,  1.48942292e-01,\n",
       "        -5.59743084e-02,  2.83960700e-02,  5.14326654e-02, -5.39636612e-02,\n",
       "         1.05810285e-01, -2.04773918e-02,  9.13997740e-03, -1.12286415e-02,\n",
       "         1.11550100e-01, -5.51443025e-02, -6.51894212e-02,  1.75244734e-02,\n",
       "        -1.67994555e-02, -2.68531386e-02,  7.50448480e-02,  5.05681746e-02,\n",
       "         5.13083600e-02,  3.67240161e-02,  2.11970657e-02, -2.34860647e-02,\n",
       "         1.09981962e-01, -5.96532673e-02,  1.76289454e-02, -1.96866281e-02,\n",
       "         8.13394040e-02,  1.43598095e-01,  1.00324489e-01, -7.94163495e-02,\n",
       "        -1.68576956e-01, -1.41728669e-04,  6.13073595e-02, -3.04661319e-02],\n",
       "       dtype=float32),\n",
       " array([ 0.0197711 , -0.05023316, -0.14175601, -0.04135554, -0.046851  ,\n",
       "        -0.02747535,  0.1007366 , -0.12664606,  0.0585149 , -0.00170305,\n",
       "        -0.12149559, -0.01562321, -0.01902621,  0.06702466,  0.14085588,\n",
       "        -0.12169945, -0.01157425, -0.11744095,  0.00926825, -0.09723589,\n",
       "         0.01249395, -0.00820314, -0.05339535, -0.0713928 , -0.05954795,\n",
       "        -0.05479388,  0.07021842,  0.03684016, -0.02276203,  0.09312333,\n",
       "        -0.02592541,  0.15759687, -0.07949561,  0.02874164,  0.04846569,\n",
       "        -0.04386867,  0.08434823, -0.00975603,  0.01050078, -0.01291099,\n",
       "         0.10430591, -0.06623004, -0.05475802,  0.00818281, -0.02567668,\n",
       "        -0.02001186,  0.06838617,  0.04595795,  0.04536514,  0.04107507,\n",
       "         0.02405912, -0.03844703,  0.1068033 , -0.06204641,  0.01531923,\n",
       "        -0.02251247,  0.08763602,  0.144205  ,  0.10000179, -0.06906228,\n",
       "        -0.19074015, -0.01602944,  0.05787853, -0.02488193], dtype=float32),\n",
       " array([ 0.01673957, -0.05291846, -0.14043841, -0.03512123, -0.06189156,\n",
       "        -0.02169649,  0.11917024, -0.11833202,  0.04397057, -0.02009043,\n",
       "        -0.11194682, -0.0428986 , -0.0174606 ,  0.06077173,  0.14015579,\n",
       "        -0.12216896, -0.01462251, -0.1337263 ,  0.03769878, -0.0904185 ,\n",
       "         0.0281968 , -0.01421509, -0.05618021, -0.08293878, -0.06009499,\n",
       "        -0.0519089 ,  0.08001146,  0.03250465, -0.01701133,  0.11002797,\n",
       "        -0.03068833,  0.14172448, -0.06190823,  0.03316807,  0.05467916,\n",
       "        -0.06954812,  0.09816482, -0.00789401,  0.0061632 , -0.0090829 ,\n",
       "         0.0942857 , -0.0626896 , -0.06990487,  0.01505221, -0.02496896,\n",
       "        -0.02983033,  0.08258307,  0.0403484 ,  0.05396493,  0.04231572,\n",
       "         0.02767518, -0.03094609,  0.11182463, -0.04600058,  0.01573679,\n",
       "        -0.00867831,  0.09298661,  0.12008023,  0.09798925, -0.06416974,\n",
       "        -0.17059502,  0.00283609,  0.06863102, -0.03218477], dtype=float32),\n",
       " array([ 0.00540724, -0.06837717, -0.138466  , -0.0405009 , -0.052816  ,\n",
       "        -0.02492985,  0.11310249, -0.12088213,  0.04945352, -0.00543894,\n",
       "        -0.10224362, -0.02478654, -0.02354077,  0.06688781,  0.1351256 ,\n",
       "        -0.12059413, -0.01335653, -0.13776892,  0.02367938, -0.09471479,\n",
       "         0.0331481 , -0.00484088, -0.06729071, -0.07403452, -0.05776521,\n",
       "        -0.06403646,  0.07241321,  0.03987541, -0.01569897,  0.11599197,\n",
       "        -0.02836382,  0.15158804, -0.0672529 ,  0.0256755 ,  0.04365977,\n",
       "        -0.05428741,  0.09930855, -0.00665088,  0.00788353, -0.00993907,\n",
       "         0.09585305, -0.05308956, -0.0759991 ,  0.01006809, -0.02480508,\n",
       "        -0.01909544,  0.08224794,  0.05139968,  0.0600621 ,  0.03991351,\n",
       "         0.02382048, -0.0383949 ,  0.11418192, -0.05288744,  0.01885108,\n",
       "        -0.01070497,  0.09622362,  0.1250848 ,  0.10924608, -0.06904136,\n",
       "        -0.17610043, -0.00438187,  0.05929531, -0.02421352], dtype=float32),\n",
       " array([ 0.01017158, -0.05597768, -0.13946205, -0.03545765, -0.05448082,\n",
       "        -0.03245982,  0.09939507, -0.11284934,  0.05619138,  0.00257913,\n",
       "        -0.10888456, -0.01443531, -0.019995  ,  0.05435036,  0.13143338,\n",
       "        -0.10581222, -0.00330033, -0.12652427,  0.0316917 , -0.09432884,\n",
       "         0.03153017,  0.00233754, -0.08009225, -0.07528065, -0.05425915,\n",
       "        -0.0682776 ,  0.07331392,  0.03980324, -0.02226805,  0.09674238,\n",
       "        -0.02578994,  0.1554876 , -0.0645121 ,  0.02443733,  0.0380659 ,\n",
       "        -0.04949729,  0.10376298,  0.0036062 ,  0.00843563, -0.00643806,\n",
       "         0.09868895, -0.06347679, -0.06898692, -0.00149664, -0.0277847 ,\n",
       "        -0.01448493,  0.0694847 ,  0.04224424,  0.05713892,  0.04046775,\n",
       "         0.02799662, -0.04403805,  0.1219354 , -0.05284003,  0.02270138,\n",
       "        -0.0162392 ,  0.09386792,  0.12414752,  0.11239456, -0.07002611,\n",
       "        -0.16716152, -0.01350043,  0.05532638, -0.02166011], dtype=float32)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm.auto import trange\n",
    "\n",
    "model = torch.load('/kaggle/input/default-ngcf/start_model.pt', map_location='cpu').cpu()\n",
    "\n",
    "vectors = [None for _ in range(N_TOTAL)]\n",
    "for i in trange(model.embedding.weight.shape[0]):\n",
    "    if vectors[i] is not None:\n",
    "        continue\n",
    "    cur_graph = graph_manager.get_random_connected_fixed_start(i)\n",
    "    output = model(cur_graph)\n",
    "    ind = -1\n",
    "    nodes = list(cur_graph.nodes)\n",
    "    for j in range(len(nodes)):\n",
    "        vectors[nodes[j]] = output[j].cpu().detach().numpy()\n",
    "vectors[:5]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "3a006bd1",
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    "execution": {
     "iopub.execute_input": "2023-01-08T15:04:27.031690Z",
     "iopub.status.busy": "2023-01-08T15:04:27.031280Z",
     "iopub.status.idle": "2023-01-08T15:10:57.133955Z",
     "shell.execute_reply": "2023-01-08T15:10:57.132335Z"
    },
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     "exception": false,
     "start_time": "2023-01-08T15:04:27.015743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(\"dssm.txt\", \"w\") as f:\n",
    "    f.write(f\"{len(vectors)} {len(vectors[0])}\\n\")\n",
    "    for i in range(len(vectors)):\n",
    "        for j in range(len(vectors[0])):\n",
    "            f.write(f\"{round(vectors[i][j].item(), 7)}\\n\")"
   ]
  },
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