sasrec_movielens.ipynb

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       "          userId  movieId  rating            timestamp\n",
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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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    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df = df.sort_values('timestamp')"
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       "          userId  movieId  rating           timestamp\n",
       "4182421    28507     1176     4.0 1995-01-09 11:46:44\n",
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       "12341178   85252       45     3.0 1996-01-29 00:00:00\n",
       "...          ...      ...     ...                 ...\n",
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       "12898527   89081    52458     4.0 2015-03-31 06:11:28\n",
       "12675921   87586     7151     3.5 2015-03-31 06:40:02\n",
       "\n",
       "[20000263 rows x 4 columns]"
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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",
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       "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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   "source": [
    "df['timestamp_int'] = df['timestamp'].values.astype(np.int64) // 10 ** 9\n",
    "df.head()"
   ]
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    {
     "data": {
      "text/plain": [
       "(array([ 239125.,  680732.,  279252., 1430997.,  883398., 4291193.,\n",
       "        2200156., 5561926., 1534824., 2898660.]),\n",
       " array([0.5 , 0.95, 1.4 , 1.85, 2.3 , 2.75, 3.2 , 3.65, 4.1 , 4.55, 5.  ]),\n",
       " <BarContainer object of 10 artists>)"
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    {
     "data": {
      "image/png": "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\n",
      "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": 6,
   "id": "fddf6faf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:56.858261Z",
     "iopub.status.busy": "2023-01-05T08:44:56.857980Z",
     "iopub.status.idle": "2023-01-05T08:44:57.457349Z",
     "shell.execute_reply": "2023-01-05T08:44:57.456430Z"
    },
    "id": "WA0zThs6dWUe",
    "outputId": "19bb5eb1-502e-49cd-9676-6893cbd2cb16",
    "papermill": {
     "duration": 0.610223,
     "end_time": "2023-01-05T08:44:57.459572",
     "exception": false,
     "start_time": "2023-01-05T08:44:56.849349",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1659326.,  984706., 3269908., 2069282., 2297922., 2946227.,\n",
       "        2207941., 1810870., 1456931., 1297150.]),\n",
       " array([7.89652004e+08, 8.53465204e+08, 9.17278404e+08, 9.81091603e+08,\n",
       "        1.04490480e+09, 1.10871800e+09, 1.17253120e+09, 1.23634440e+09,\n",
       "        1.30015760e+09, 1.36397080e+09, 1.42778400e+09]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['timestamp_int'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a1303b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:57.476729Z",
     "iopub.status.busy": "2023-01-05T08:44:57.476402Z",
     "iopub.status.idle": "2023-01-05T08:44:58.111879Z",
     "shell.execute_reply": "2023-01-05T08:44:58.110816Z"
    },
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     "exception": false,
     "start_time": "2023-01-05T08:44:57.467834",
     "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": 8,
   "id": "89129a93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:58.131627Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "          userId:token  movieId:token  rating           timestamp  \\\n",
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       "12341178         85252             45     3.0 1996-01-29 00:00:00   \n",
       "\n",
       "          timestamp:float  \n",
       "4182421         789652004  \n",
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea6a2031",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:58.166181Z",
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    },
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     "duration": 0.521829,
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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": 10,
   "id": "ff14c9ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:58.696290Z",
     "iopub.status.busy": "2023-01-05T08:44:58.694755Z",
     "iopub.status.idle": "2023-01-05T08:44:58.707381Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
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       "          userId:token  movieId:token  rating  timestamp:float\n",
       "4182421          28507           1176     4.0        789652004\n",
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    "df_train.head()"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6294c673",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:58.728678Z",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1658022., 1603648., 1556707., 1587068., 1582019., 1645064.,\n",
       "        1602175., 1585145., 1604241., 1576121.]),\n",
       " array([1.000000e+00, 1.385020e+04, 2.769940e+04, 4.154860e+04,\n",
       "        5.539780e+04, 6.924700e+04, 8.309620e+04, 9.694540e+04,\n",
       "        1.107946e+05, 1.246438e+05, 1.384930e+05]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_train['userId:token'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4100e949",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:59.581200Z",
     "iopub.status.busy": "2023-01-05T08:44:59.580497Z",
     "iopub.status.idle": "2023-01-05T08:44:59.810198Z",
     "shell.execute_reply": "2023-01-05T08:44:59.805172Z"
    },
    "id": "5q0VqiS0cngk",
    "outputId": "c475bf96-9ee3-4c24-dd63-4638b0c6d2ab",
    "papermill": {
     "duration": 0.250663,
     "end_time": "2023-01-05T08:44:59.813343",
     "exception": false,
     "start_time": "2023-01-05T08:44:59.562680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 138493, 1, 131262)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['userId:token'].min(), df['userId:token'].max(), df['movieId:token'].min(), df['movieId:token'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1b05c30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:44:59.865602Z",
     "iopub.status.busy": "2023-01-05T08:44:59.865217Z",
     "iopub.status.idle": "2023-01-05T08:45:01.571848Z",
     "shell.execute_reply": "2023-01-05T08:45:01.570823Z"
    },
    "id": "Rkb2zvzz_Fly",
    "outputId": "0239aadb-dff8-484e-d569-96e4e29f485c",
    "papermill": {
     "duration": 1.737677,
     "end_time": "2023-01-05T08:45:01.574356",
     "exception": false,
     "start_time": "2023-01-05T08:44:59.836679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda:0'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea04df65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:45:01.593768Z",
     "iopub.status.busy": "2023-01-05T08:45:01.592606Z",
     "iopub.status.idle": "2023-01-05T08:45:01.607352Z",
     "shell.execute_reply": "2023-01-05T08:45:01.606493Z"
    },
    "id": "ePjoZHohRGAU",
    "papermill": {
     "duration": 0.026272,
     "end_time": "2023-01-05T08:45:01.609315",
     "exception": false,
     "start_time": "2023-01-05T08:45:01.583043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from tqdm.auto import trange\n",
    "import random\n",
    "\n",
    "\n",
    "class LMDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df, n_users_total, n_items_total, batch_size, max_len):\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_users_total)]\n",
    "        self.max_len = max_len\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",
    "            cur_ts = df['timestamp:float'].iloc[i]\n",
    "            self.users_history[cur_user].append((cur_item, cur_rating, cur_ts))\n",
    "        \n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        batch_users = set()\n",
    "        while len(batch_users) < self.batch_size:\n",
    "            v = random.randint(0, self.n_users_total) - 1\n",
    "            if v not in batch_users:\n",
    "               batch_users.add(v)\n",
    "        batch_users = list(batch_users)\n",
    "        max_len = 0\n",
    "        for u in batch_users:\n",
    "            max_len = max(max_len, len(self.users_history[u]))\n",
    "        max_len = min(max_len, self.max_len)\n",
    "        history_input, history_output, mask = torch.zeros((self.batch_size, max_len + 1), dtype=torch.long), \\\n",
    "                                              torch.zeros((self.batch_size, max_len + 1), dtype=torch.long), \\\n",
    "                                              torch.zeros((self.batch_size, max_len + 1), dtype=torch.long)\n",
    "        metadata = torch.zeros((self.batch_size, max_len + 1, 2))\n",
    "        for i, u in enumerate(batch_users):\n",
    "            for j in range(min(self.max_len,len(self.users_history[u]))):\n",
    "                cur_item, cur_rating, cur_ts = self.users_history[u][j]\n",
    "                history_input[i][j + 1] = cur_item\n",
    "                history_output[i][j] = cur_item\n",
    "                mask[i][j] = 1\n",
    "                metadata[i][j + 1][0] = cur_rating\n",
    "                metadata[i][j + 1][1] = cur_ts\n",
    "        return {\n",
    "            \"history_input\" : history_input.to(device),\n",
    "            \"history_output\": history_output.to(device),\n",
    "            \"mask\" : mask.to(device),\n",
    "            \"metadata\": metadata.to(device)\n",
    "        }\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.n_users_total // self.batch_size + 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2b72426",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:45:01.627774Z",
     "iopub.status.busy": "2023-01-05T08:45:01.627494Z",
     "iopub.status.idle": "2023-01-05T08:55:15.985324Z",
     "shell.execute_reply": "2023-01-05T08:55:15.984392Z"
    },
    "id": "krqWLeJvUBcX",
    "outputId": "396ecef2-e9bb-430a-88e9-167dd2a3644c",
    "papermill": {
     "duration": 614.369741,
     "end_time": "2023-01-05T08:55:15.987542",
     "exception": false,
     "start_time": "2023-01-05T08:45:01.617801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "407c48d2ad1c4a15b6cf7231cc97eef8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_dataset = LMDataset(df_train, df['userId:token'].max() + 1, df['movieId:token'].max() + 1, 8, 140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f26ce0ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:55:16.006954Z",
     "iopub.status.busy": "2023-01-05T08:55:16.006272Z",
     "iopub.status.idle": "2023-01-05T08:55:18.877051Z",
     "shell.execute_reply": "2023-01-05T08:55:18.876107Z"
    },
    "id": "RAglgZeD0PcO",
    "outputId": "abe54ad1-5223-4bc3-8ff2-fa9bc20c2f2d",
    "papermill": {
     "duration": 2.882592,
     "end_time": "2023-01-05T08:55:18.879121",
     "exception": false,
     "start_time": "2023-01-05T08:55:15.996529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'history_input': tensor([[   0, 1968, 1210,  ...,    0,    0,    0],\n",
       "         [   0, 1617, 2067,  ...,    0,    0,    0],\n",
       "         [   0,    0,    0,  ...,    0,    0,    0],\n",
       "         ...,\n",
       "         [   0, 1816,  838,  ..., 2716,  357,   21],\n",
       "         [   0,  435, 1644,  ..., 2130, 3505, 2067],\n",
       "         [   0,    0,    0,  ...,    0,    0,    0]], device='cuda:0'),\n",
       " 'history_output': tensor([[1968, 1210, 4014,  ...,    0,    0,    0],\n",
       "         [1617, 2067, 1777,  ...,    0,    0,    0],\n",
       "         [   0,    0,    0,  ...,    0,    0,    0],\n",
       "         ...,\n",
       "         [1816,  838, 1210,  ...,  357,   21,    0],\n",
       "         [ 435, 1644,  420,  ..., 3505, 2067,    0],\n",
       "         [   0,    0,    0,  ...,    0,    0,    0]], device='cuda:0'),\n",
       " 'mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
       "         [1, 1, 1,  ..., 0, 0, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [1, 1, 1,  ..., 1, 1, 0],\n",
       "         [1, 1, 1,  ..., 1, 1, 0],\n",
       "         [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0'),\n",
       " 'metadata': tensor([[[0.0000e+00, 0.0000e+00],\n",
       "          [5.0000e+00, 1.0223e+09],\n",
       "          [5.0000e+00, 1.0223e+09],\n",
       "          ...,\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00]],\n",
       " \n",
       "         [[0.0000e+00, 0.0000e+00],\n",
       "          [5.0000e+00, 9.9305e+08],\n",
       "          [5.0000e+00, 9.9305e+08],\n",
       "          ...,\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00]],\n",
       " \n",
       "         [[0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          ...,\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.0000e+00, 0.0000e+00],\n",
       "          [3.0000e+00, 9.7483e+08],\n",
       "          [3.0000e+00, 9.7483e+08],\n",
       "          ...,\n",
       "          [4.0000e+00, 9.7483e+08],\n",
       "          [3.0000e+00, 9.7483e+08],\n",
       "          [3.0000e+00, 9.7483e+08]],\n",
       " \n",
       "         [[0.0000e+00, 0.0000e+00],\n",
       "          [1.0000e+00, 1.0328e+09],\n",
       "          [3.0000e+00, 1.0328e+09],\n",
       "          ...,\n",
       "          [4.0000e+00, 1.0414e+09],\n",
       "          [5.0000e+00, 1.0414e+09],\n",
       "          [4.0000e+00, 1.0414e+09]],\n",
       " \n",
       "         [[0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          ...,\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00],\n",
       "          [0.0000e+00, 0.0000e+00]]], device='cuda:0')}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14ba03ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:55:18.899273Z",
     "iopub.status.busy": "2023-01-05T08:55:18.897847Z",
     "iopub.status.idle": "2023-01-05T08:55:18.905346Z",
     "shell.execute_reply": "2023-01-05T08:55:18.904510Z"
    },
    "id": "m6Y2HEsx0TCs",
    "papermill": {
     "duration": 0.019231,
     "end_time": "2023-01-05T08:55:18.907311",
     "exception": false,
     "start_time": "2023-01-05T08:55:18.888080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SASRec(nn.Module):\n",
    "    def __init__(self, num_embeddings, embedding_dim, hidden_dim, num_layers, hidden_size, output_items):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n",
    "        self.positional_encoding = nn.Linear(2, embedding_dim)\n",
    "        encoder_layer = nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=4)\n",
    "        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)\n",
    "        \n",
    "        self.last = nn.Linear(embedding_dim, output_items)\n",
    "\n",
    "    def forward(self, history_input, metadata):\n",
    "        hidden = self.embedding(history_input) + self.positional_encoding(metadata)\n",
    "        output_gru = self.transformer(hidden)\n",
    "        return self.last(output_gru)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ae5ed03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:55:18.925818Z",
     "iopub.status.busy": "2023-01-05T08:55:18.925067Z",
     "iopub.status.idle": "2023-01-05T08:55:19.135050Z",
     "shell.execute_reply": "2023-01-05T08:55:19.134069Z"
    },
    "id": "M197kzXK1wNA",
    "papermill": {
     "duration": 0.221386,
     "end_time": "2023-01-05T08:55:19.137185",
     "exception": false,
     "start_time": "2023-01-05T08:55:18.915799",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def lr_func(iteration):\n",
    "    if iteration < 20000:\n",
    "        return 1e-4 * (2 + iteration / 1100)\n",
    "    else:\n",
    "        return 5000 / iteration ** 1.5\n",
    "    \n",
    "xs = np.linspace(0, 50000, 1000)\n",
    "ys = [lr_func(x) for x in xs]\n",
    "\n",
    "plt.plot(xs, ys);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "248d2532",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:55:19.157216Z",
     "iopub.status.busy": "2023-01-05T08:55:19.156862Z",
     "iopub.status.idle": "2023-01-05T08:55:19.393718Z",
     "shell.execute_reply": "2023-01-05T08:55:19.392670Z"
    },
    "id": "RPkybZbQn7ko",
    "papermill": {
     "duration": 0.2496,
     "end_time": "2023-01-05T08:55:19.396255",
     "exception": false,
     "start_time": "2023-01-05T08:55:19.146655",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = SASRec(df['movieId:token'].max() + 1, 64, 64, 2, 64, df['movieId:token'].max() + 1).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=[lr_func])\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "17c5907c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T08:55:19.415993Z",
     "iopub.status.busy": "2023-01-05T08:55:19.415695Z",
     "iopub.status.idle": "2023-01-05T10:52:43.178395Z",
     "shell.execute_reply": "2023-01-05T10:52:43.177423Z"
    },
    "id": "NrcKXCxnoKF5",
    "outputId": "44a584d8-58c6-4c53-de51-634c9902a797",
    "papermill": {
     "duration": 7043.775477,
     "end_time": "2023-01-05T10:52:43.181298",
     "exception": false,
     "start_time": "2023-01-05T08:55:19.405821",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6b0b4096d154eacab6996a05ffde15f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86276353a1b7446d9b8afb3f04c46aca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/17312 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
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       "model_id": "879e0c828a594a578d6b08f73f2f48d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/17312 [00:00<?, ?it/s]"
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    {
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       "version_major": 2,
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       "  0%|          | 0/17312 [00:00<?, ?it/s]"
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    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/17312 [00:00<?, ?it/s]"
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    {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/17312 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c954524557994a12983d25a78a22e0f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/17312 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "e2ac3776042248a3b905afe80c95e058",
       "version_major": 2,
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      "text/plain": [
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    },
    {
     "data": {
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       "model_id": "0ceda8855fee4f5fa6e27279627d8f5b",
       "version_major": 2,
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     "metadata": {},
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    {
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      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb6a7c52d8064b8c8f14d151cc22bb31",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fae31473b4fa42beb9b7b98b317d2af9",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6ee57f243354517a5d6321d1f56f673",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm \n",
    "\n",
    "epochs = 12\n",
    "\n",
    "history = []\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    pbar = trange(len(train_dataset))\n",
    "    for i in pbar:\n",
    "        batch = train_dataset[i]\n",
    "        optimizer.zero_grad()\n",
    "        output_model = model(batch['history_input'], batch['metadata'])\n",
    "        total_len = len(batch['history_output'].reshape(-1))\n",
    "        loss = criterion(output_model.reshape(total_len, -1), batch['history_output'].reshape(-1))\n",
    "        history.append(loss.item())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a941dc77",
   "metadata": {
    "id": "R-yWQfCRWRqh",
    "outputId": "52916135-5841-488e-aedb-0f44c4cdc29e",
    "papermill": {
     "duration": 0.010346,
     "end_time": "2023-01-05T10:52:43.202398",
     "exception": false,
     "start_time": "2023-01-05T10:52:43.192052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f0fc222",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T10:52:43.224780Z",
     "iopub.status.busy": "2023-01-05T10:52:43.224027Z",
     "iopub.status.idle": "2023-01-05T10:52:43.432259Z",
     "shell.execute_reply": "2023-01-05T10:52:43.431157Z"
    },
    "id": "vmFq8AIoU7Og",
    "papermill": {
     "duration": 0.22225,
     "end_time": "2023-01-05T10:52:43.435066",
     "exception": false,
     "start_time": "2023-01-05T10:52:43.212816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07824acf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T10:52:43.457715Z",
     "iopub.status.busy": "2023-01-05T10:52:43.457399Z",
     "iopub.status.idle": "2023-01-05T10:52:43.472225Z",
     "shell.execute_reply": "2023-01-05T10:52:43.471372Z"
    },
    "id": "6zP-zISqWeDi",
    "outputId": "592fb2f1-e016-42eb-ca17-4e815c5c07ef",
    "papermill": {
     "duration": 0.028358,
     "end_time": "2023-01-05T10:52:43.474394",
     "exception": false,
     "start_time": "2023-01-05T10:52:43.446036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"metrics.txt\", 'wb') as f:\n",
    "    pickle.dump(history, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ddbc2bb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T10:52:43.496854Z",
     "iopub.status.busy": "2023-01-05T10:52:43.496084Z",
     "iopub.status.idle": "2023-01-05T10:52:43.864411Z",
     "shell.execute_reply": "2023-01-05T10:52:43.863426Z"
    },
    "papermill": {
     "duration": 0.381952,
     "end_time": "2023-01-05T10:52:43.866743",
     "exception": false,
     "start_time": "2023-01-05T10:52:43.484791",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5461a94e90>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.plot(np.arange(len(history)), history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77b6c714",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T10:52:43.890978Z",
     "iopub.status.busy": "2023-01-05T10:52:43.890680Z",
     "iopub.status.idle": "2023-01-05T10:52:43.899281Z",
     "shell.execute_reply": "2023-01-05T10:52:43.898184Z"
    },
    "id": "a0EMuGcwuPg1",
    "papermill": {
     "duration": 0.023329,
     "end_time": "2023-01-05T10:52:43.901483",
     "exception": false,
     "start_time": "2023-01-05T10:52:43.878154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def reccomend_for_user(user_id, k):\n",
    "    users_history = train_dataset.users_history[user_id]\n",
    "    answer = []\n",
    "    mark = 5.0\n",
    "    ts = users_history[-1][2] if len(users_history) > 0 else 0\n",
    "\n",
    "    for i in range(k):\n",
    "        max_len = len(users_history)\n",
    "        history_input = torch.zeros((1, max_len + 1), dtype=torch.long).to(device)\n",
    "        metadata = torch.zeros((1, max_len + 1, 2)).to(device)\n",
    "        for j in range(len(users_history)):\n",
    "            cur_item, cur_rating, cur_ts = users_history[j]\n",
    "            history_input[0][j + 1] = cur_item\n",
    "            metadata[0][j + 1][0] = cur_rating\n",
    "            metadata[0][j + 1][1] = cur_ts\n",
    "        output = model(history_input, metadata)[0][-1][1:].argmax(-1).item()\n",
    "        answer.append(output)\n",
    "        users_history.append((output, mark, ts))\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ded7e53c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-05T10:52:43.924369Z",
     "iopub.status.busy": "2023-01-05T10:52:43.923605Z",
     "iopub.status.idle": "2023-01-05T10:52:43.938878Z",
     "shell.execute_reply": "2023-01-05T10:52:43.938048Z"
    },
    "id": "wAW3gaz3ztV9",
    "papermill": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "source": [
    "from math import log2\n",
    "\n",
    "def apk(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred) \n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            cnt += 1\n",
    "            ans += cnt / (i + 1)\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def mapk(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += apk(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def mr(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (i + 1)\n",
    "            s.add(pred[i])\n",
    "            break\n",
    "    return ans\n",
    "\n",
    "def mrr(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += mr(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def ndcgunique(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (log2(i + 2))\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "def ndcg(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += ndcgunique(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def precisionunique(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def precision(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += precisionunique(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e22a9ca0",
   "metadata": {
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     "exception": false,
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c0228e6886c4852b564540c7d064838",
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       "  0%|          | 0/4000053 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dataset = LMDataset(df_test, df['userId:token'].max() + 1, df['movieId:token'].max() + 1, 32, 140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b55e5e0c",
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a81a9826e9df4bbaa69e39217f9405fc",
       "version_major": 2,
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       "  0%|          | 0/138494 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "predictions = []\n",
    "target = []\n",
    "k = 20\n",
    "for i in trange(len(test_dataset.users_history)):\n",
    "    if len(test_dataset.users_history[i]) == 0:\n",
    "        continue\n",
    "    out = reccomend_for_user(i, k)\n",
    "    predictions.append(out)\n",
    "    target.append([x[0] for x in test_dataset.users_history[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32def898",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.001420903062835491\n",
      "precision@2 : 0.040906220397852855\n",
      "precision@3 : 0.036375118408589036\n",
      "precision@4 : 0.03047837069782128\n",
      "precision@5 : 0.025917271866120587\n",
      "precision@6 : 0.02255025786759234\n",
      "precision@7 : 0.020009923767423315\n",
      "precision@8 : 0.018037574992106092\n",
      "precision@9 : 0.016514051152508962\n",
      "precision@10 : 0.015184717398169506\n",
      "precision@11 : 0.014134397336164459\n",
      "precision@12 : 0.013201241974528476\n",
      "precision@13 : 0.012455369070462559\n",
      "precision@14 : 0.011820560241778988\n",
      "precision@15 : 0.011230396800336374\n",
      "precision@16 : 0.01074952636564572\n",
      "precision@17 : 0.01029365329965317\n",
      "precision@18 : 0.009890186997858959\n",
      "precision@19 : 0.009539162082661357\n",
      "ndcg: 0.004481689448274301\n",
      "mrr: 0.05616783003331632\n",
      "mapk@1 : 0.001420903062835491\n",
      "mapk@2 : 0.020824123776444586\n",
      "mapk@3 : 0.017328000561344533\n",
      "mapk@4 : 0.013990632564993055\n",
      "mapk@5 : 0.01159541100936777\n",
      "mapk@6 : 0.009879486369855653\n",
      "mapk@7 : 0.00859894447201063\n",
      "mapk@8 : 0.007620283391221949\n",
      "mapk@9 : 0.006847651514265168\n",
      "mapk@10 : 0.0062099340420308635\n",
      "mapk@11 : 0.005687408429966422\n",
      "mapk@12 : 0.005243498424939744\n",
      "mapk@13 : 0.004870420149803733\n",
      "mapk@14 : 0.0045491145161293016\n",
      "mapk@15 : 0.004265206597540018\n",
      "mapk@16 : 0.004018119265395844\n",
      "mapk@17 : 0.0037965091673917246\n",
      "mapk@18 : 0.003600405246950016\n",
      "mapk@19 : 0.003424292700472646\n"
     ]
    }
   ],
   "source": [
    "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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   "execution_count": 29,
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     "name": "stdout",
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     "text": [
      "precision@1 : 1.0\n",
      "precision@2 : 1.0\n",
      "precision@3 : 1.0\n",
      "precision@4 : 1.0\n",
      "precision@5 : 1.0\n",
      "precision@6 : 1.0\n",
      "precision@7 : 1.0\n",
      "precision@8 : 1.0\n",
      "precision@9 : 1.0\n",
      "precision@10 : 1.0\n",
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      "precision@12 : 1.0\n",
      "precision@13 : 1.0\n",
      "precision@14 : 1.0\n",
      "precision@15 : 1.0\n",
      "precision@16 : 1.0\n",
      "precision@17 : 1.0\n",
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      "precision@30 : 1.0\n",
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      "precision@38 : 1.0\n",
      "precision@39 : 1.0\n",
      "ndcg: 0.25948577327982253\n",
      "mrr: 1.0\n",
      "mapk@1 : 1.0\n",
      "mapk@2 : 1.0\n",
      "mapk@3 : 1.0\n",
      "mapk@4 : 1.0\n",
      "mapk@5 : 1.0\n",
      "mapk@6 : 1.0\n",
      "mapk@7 : 1.0\n",
      "mapk@8 : 1.0\n",
      "mapk@9 : 1.0\n",
      "mapk@10 : 1.0\n",
      "mapk@11 : 1.0\n",
      "mapk@12 : 1.0\n",
      "mapk@13 : 1.0\n",
      "mapk@14 : 1.0\n",
      "mapk@15 : 1.0\n",
      "mapk@16 : 1.0\n",
      "mapk@17 : 1.0\n",
      "mapk@18 : 1.0\n",
      "mapk@19 : 1.0\n",
      "mapk@20 : 1.0\n",
      "mapk@21 : 1.0\n",
      "mapk@22 : 1.0\n",
      "mapk@23 : 1.0\n",
      "mapk@24 : 1.0\n",
      "mapk@25 : 1.0\n",
      "mapk@26 : 1.0\n",
      "mapk@27 : 1.0\n",
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      "mapk@29 : 1.0\n",
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      "mapk@31 : 1.0\n",
      "mapk@32 : 1.0\n",
      "mapk@33 : 1.0\n",
      "mapk@34 : 1.0\n",
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      "mapk@39 : 1.0\n"
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    "for i in range(1, 40):\n",
    "    print(f\"precision@{i} : {precision(target, target, i)}\")\n",
    "print(f\"ndcg: {ndcg(target, target)}\")\n",
    "print(f\"mrr: {mrr(target, target)}\")\n",
    "for i in range(1, 40):\n",
    "    print(f\"mapk@{i} : {mapk(target, target, i)}\")"
   ]
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