recvae_movielens.ipynb

{
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       "          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",
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       "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')"
   ]
  },
  {
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   "execution_count": 3,
   "id": "70a74ada",
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    {
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       "          userId  movieId  rating           timestamp  timestamp_int\n",
       "4182421    28507     1176     4.0 1995-01-09 11:46:44      789652004\n",
       "18950979  131160     1079     3.0 1995-01-09 11:46:49      789652009\n",
       "18950936  131160       47     5.0 1995-01-09 11:46:49      789652009\n",
       "18950930  131160       21     3.0 1995-01-09 11:46:49      789652009\n",
       "12341178   85252       45     3.0 1996-01-29 00:00:00      822873600"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "df['timestamp_int'] = df['timestamp'].values.astype(np.int64) // 10 ** 9\n",
    "df.head()"
   ]
  },
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    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(df['rating'].values);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b4e0b3b",
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     "exception": false,
     "start_time": "2023-01-03T08:16:40.387535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"userId\": \"userId:token\", \"movieId\": \"movieId:token\", \"timestamp_int\": \"timestamp:float\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f80bffb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:16:41.224820Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "262d4eeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:16:41.796635Z",
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    },
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    "outputId": "02ffbf0a-783b-45ac-cdc0-fc05eac5ab13",
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     "duration": 1.527118,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f7c1d0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:16:43.327471Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm.auto import trange\n",
    "\n",
    "\n",
    "class RatingDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df, n_users_total, n_items_total, batch_size): \n",
    "        self.n_users_total = n_users_total\n",
    "        self.n_items_total = n_items_total\n",
    "        self.batch_size = batch_size\n",
    "        self.users_history = [[] for _ in range(self.n_items_total)]\n",
    "        for i in trange(df.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",
    "            self.users_history[cur_item].append((cur_user, cur_rating))\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        answer = torch.zeros(self.n_users_total)\n",
    "        for user, rating in self.users_history[index]:\n",
    "            answer[user] = rating\n",
    "        return answer\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.users_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83f3513d",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2023-01-03T08:16:43.345291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "773b2942dd354dba9562ac8ec974d1b0",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = RatingDataset(df_train, df['userId:token'].max() + 1, df['movieId:token'].max() + 1, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dd510c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:15.909052Z",
     "iopub.status.busy": "2023-01-03T08:25:15.907996Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loader = torch.utils.data.DataLoader(dataset, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00afe8ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:15.931059Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "N_TOTAL = df['userId:token'].max() + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5d5ea18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:15.974034Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self, hidden2_dim, hidden_dim):\n",
    "        super().__init__()\n",
    "\n",
    "\n",
    "        self.encoder_common = nn.Sequential(\n",
    "            nn.Linear(N_TOTAL, hidden2_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden2_dim, hidden_dim),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.encoder_mu = nn.Linear(hidden_dim, hidden_dim) \n",
    "        self.encoder_sigma = nn.Linear(hidden_dim, hidden_dim) \n",
    "        self.decoder_common = nn.Sequential(\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden2_dim)\n",
    "        )\n",
    "        self.decoder_mu = nn.Linear(hidden2_dim, N_TOTAL)\n",
    "        self.decoder_sigma = nn.Linear(hidden2_dim, N_TOTAL)\n",
    "\n",
    "\n",
    "    def gaussian_sampler(self, mu, logsigma):\n",
    "        noise = torch.randn_like(mu)\n",
    "        return noise * logsigma.exp() + mu\n",
    "\n",
    "    def encode(self, x):\n",
    "        hidden = self.encoder_common(x)\n",
    "        mu = self.encoder_mu(hidden)\n",
    "        sigma = self.encoder_sigma(hidden)\n",
    "        return mu, sigma\n",
    "\n",
    "    def decode(self, mu, sigma):\n",
    "        sample = self.gaussian_sampler(mu, sigma)\n",
    "        hidden = self.decoder_common(sample)\n",
    "        decoded_mu, decoded_sigma = self.decoder_mu(hidden), self.decoder_sigma(hidden)\n",
    "        return decoded_mu, decoded_sigma\n",
    "\n",
    "    def forward(self, x):\n",
    "        latent_mu, latent_logsigma = self.encode(x)\n",
    "        reconstruction_mu, reconstruction_logsigma = self.decode(latent_mu, latent_logsigma)\n",
    "        return reconstruction_mu, reconstruction_logsigma, latent_mu, latent_logsigma\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a013724",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:16.004343Z",
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     "shell.execute_reply": "2023-01-03T08:25:16.010239Z"
    },
    "id": "KL27Hd7l0lb2",
    "papermill": {
     "duration": 0.018163,
     "end_time": "2023-01-03T08:25:16.013515",
     "exception": false,
     "start_time": "2023-01-03T08:25:15.995352",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def KL_divergence(mu, logsigma):\n",
    "    return -0.5 * torch.sum(1 + 2 * logsigma - mu ** 2 - torch.exp(logsigma) ** 2)\n",
    "\n",
    "\n",
    "def log_likelihood(x, mu, logsigma):\n",
    "    sigma = torch.exp(logsigma)\n",
    "    pi2 = (2 * math.pi) ** 0.5\n",
    "    return torch.sum(-( (x - mu) ** 2 / (2 * sigma ** 2)) + torch.log(1 / (sigma * pi2)))\n",
    "\n",
    "\n",
    "def loss_vae(x, mu_gen, logsigma_gen, mu_z, logsigma_z):\n",
    "    return -log_likelihood(x, mu_gen, logsigma_gen) + KL_divergence(mu_z, logsigma_z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca5def21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:16.028794Z",
     "iopub.status.busy": "2023-01-03T08:25:16.028461Z",
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    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vae = VAE(16, 64).to(device)\n",
    "optimizer = torch.optim.Adam(vae.parameters(), lr=1e-3)\n",
    "train_history, dev_history = [], []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a93591d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T08:25:16.126627Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(vae, 'vae.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df2e93a8",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    {
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       "version_major": 2,
       "version_minor": 0
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      ]
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54c61187f60b4d238e14b25014fbdc8e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "EPOCHS = 20\n",
    "\n",
    "for epoch in trange(EPOCHS):\n",
    "    i = 0\n",
    "    for batch in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        reconstruction = vae(batch[0].to(device))\n",
    "        loss = loss_vae(batch[0].to(device), *reconstruction)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_history.append((len(train_history), loss.item()))\n",
    "        if len(train_history) % 100 == 0 and len(train_history) > 0 and False:\n",
    "            clear_output(True)\n",
    "            plt.scatter(*zip(*train_history), alpha=0.1, label='train_loss')\n",
    "            plt.legend(); plt.grid(); plt.show()\n",
    "            if len(dev_history):\n",
    "                plt.plot(*zip(*dev_history), color='red', label='dev_loss')\n",
    "            plt.legend(); plt.grid(); plt.show()\n",
    "        i += 1\n",
    "        if i % 10000 == 0:\n",
    "            print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6dc6d061",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef450ca25cf94de7a8d699c23e3480a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/131263 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectors = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i in trange(len(dataset)):\n",
    "        cur_vector, _ = vae.encode(dataset[i].unsqueeze(0))\n",
    "        vectors.append(cur_vector.squeeze(0).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ef92131",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:13:26.996239Z",
     "iopub.status.busy": "2023-01-03T10:13:26.995884Z",
     "iopub.status.idle": "2023-01-03T10:13:41.864193Z",
     "shell.execute_reply": "2023-01-03T10:13:41.863197Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting nearpy\r\n",
      "  Downloading NearPy-1.0.0-py2.py3-none-any.whl (64 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.4/64.4 kB\u001b[0m \u001b[31m253.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hCollecting bitarray\r\n",
      "  Downloading bitarray-2.6.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (234 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m234.2/234.2 kB\u001b[0m \u001b[31m848.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from nearpy) (1.7.3)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from nearpy) (1.21.6)\r\n",
      "Requirement already satisfied: future in /opt/conda/lib/python3.7/site-packages (from nearpy) (0.18.2)\r\n",
      "Installing collected packages: bitarray, nearpy\r\n",
      "Successfully installed bitarray-2.6.2 nearpy-1.0.0\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": [
    "!pip install nearpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d89484eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:13:41.891408Z",
     "iopub.status.busy": "2023-01-03T10:13:41.891014Z",
     "iopub.status.idle": "2023-01-03T10:13:42.011930Z",
     "shell.execute_reply": "2023-01-03T10:13:42.010851Z"
    },
    "id": "dPgVsyaTLNpr",
    "papermill": {
     "duration": 0.136356,
     "end_time": "2023-01-03T10:13:42.014601",
     "exception": false,
     "start_time": "2023-01-03T10:13:41.878245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nearpy import Engine\n",
    "from nearpy.hashes import RandomBinaryProjections\n",
    "from nearpy.distances import EuclideanDistance\n",
    "\n",
    "class KNN:\n",
    "    def __init__(self, data_vec):\n",
    "        self.data_vec = data_vec\n",
    "        self.rbp = RandomBinaryProjections('default', 80)\n",
    "        self.engine = Engine(data_vec.shape[1], lshashes=[self.rbp], distance=EuclideanDistance())\n",
    "        for index in range(len(data_vec)):\n",
    "            self.engine.store_vector(data_vec[index], index)\n",
    "\n",
    "    def find_neighbours(self, query, k=80):\n",
    "        neighb = self.engine.neighbours(query)[:k]\n",
    "        return [cur[1] for cur in neighb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6db0c333",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:13:42.038564Z",
     "iopub.status.busy": "2023-01-03T10:13:42.038173Z",
     "iopub.status.idle": "2023-01-03T10:13:46.403522Z",
     "shell.execute_reply": "2023-01-03T10:13:46.402337Z"
    },
    "id": "aTNa-rpWMB6k",
    "papermill": {
     "duration": 4.380471,
     "end_time": "2023-01-03T10:13:46.406244",
     "exception": false,
     "start_time": "2023-01-03T10:13:42.025773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn = KNN(np.array(vectors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf4b2002",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:13:46.432338Z",
     "iopub.status.busy": "2023-01-03T10:13:46.431884Z",
     "iopub.status.idle": "2023-01-03T10:13:46.450405Z",
     "shell.execute_reply": "2023-01-03T10:13:46.449199Z"
    },
    "id": "KQ45H9H5MN-z",
    "papermill": {
     "duration": 0.034372,
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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(target) \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(target)\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",
    "    return ans / tot\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(target)\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(target) \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": 22,
   "id": "4f6b12ac",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/4000053 [00:00<?, ?it/s]"
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     "metadata": {},
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   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19da0159",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:15:13.752029Z",
     "iopub.status.busy": "2023-01-03T10:15:13.751139Z",
     "iopub.status.idle": "2023-01-03T10:23:28.767698Z",
     "shell.execute_reply": "2023-01-03T10:23:28.766496Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "g_train = [[] for i in range(df_train['userId:token'].max() + 1)]\n",
    "for i in range(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:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97f42f7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T10:23:28.796494Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c058a9d15d548c58feec93857baff3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/138494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = []\n",
    "target = []\n",
    "k = 40\n",
    "for i in trange(df_train['userId:token'].max() + 1):\n",
    "    if len(g_test[i]) == 0:\n",
    "        continue\n",
    "    \n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.find_neighbours(vectors[cur_item], k // 5)\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47771934",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-03T17:37:25.781177Z",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.0\n",
      "precision@2 : 0.0002987577489993314\n",
      "precision@3 : 0.00038420716543522364\n",
      "precision@4 : 0.00039685634870608946\n",
      "precision@5 : 0.0003971912109933591\n",
      "precision@6 : 0.0004045916993900507\n",
      "precision@7 : 0.0004974165432254667\n",
      "precision@8 : 0.0005074079548496308\n",
      "precision@9 : 0.0005074079548496308\n",
      "precision@10 : 0.0005074079548496308\n",
      "precision@11 : 0.0005074079548496308\n",
      "precision@12 : 0.0005074079548496308\n",
      "precision@13 : 0.0005074079548496308\n",
      "precision@14 : 0.0005074079548496308\n",
      "precision@15 : 0.0005074079548496308\n",
      "precision@16 : 0.0005074079548496308\n",
      "precision@17 : 0.0005074079548496308\n",
      "precision@18 : 0.0005074079548496308\n",
      "precision@19 : 0.0005074079548496308\n",
      "ndcg: 0.0002735267715048098\n",
      "mrr: 0.00019683431474909078\n",
      "mapk@1 : 0.0\n",
      "mapk@2 : 0.0001493788744996657\n",
      "mapk@3 : 0.00018402317881828574\n",
      "mapk@4 : 0.0001881799045037732\n",
      "mapk@5 : 0.00018829105856947874\n",
      "mapk@6 : 0.00019013838877025244\n",
      "mapk@7 : 0.0002068119807445081\n",
      "mapk@8 : 0.00020865454862149268\n",
      "mapk@9 : 0.00020865454862149268\n",
      "mapk@10 : 0.00020865454862149268\n",
      "mapk@11 : 0.00020865454862149268\n",
      "mapk@12 : 0.00020865454862149268\n",
      "mapk@13 : 0.00020865454862149268\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)}\")"
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