lin_methods_movielens.ipynb

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       "          userId  movieId  rating            timestamp\n",
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      ]
     },
     "execution_count": 1,
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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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   "source": [
    "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",
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   "source": [
    "df"
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   "outputs": [],
   "source": [
    "df = df.rename(columns={\"userId\": \"userId:token\", \"movieId\": \"movieId:token\", \"timestamp_int\": \"timestamp:float\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f7d5ec0",
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   "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)"
   ]
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       "          userId:token  movieId:token  rating\n",
       "4182421          28507           1176     4.0\n",
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   "source": [
    "df_train.head()"
   ]
  },
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   "outputs": [],
   "source": [
    "N_USERS = df['userId:token'].max() + 1\n",
    "N_ITEMS = df['movieId:token'].max() + 1"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "'cuda:0'"
      ]
     },
     "execution_count": 8,
     "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",
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    },
    "id": "ndh8NnjsV4bB",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from math import log2\n",
    "\n",
    "def apk(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred) \n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            cnt += 1\n",
    "            ans += cnt / (i + 1)\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def mapk(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += apk(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def mr(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (i + 1)\n",
    "            s.add(pred[i])\n",
    "            break\n",
    "    return ans\n",
    "\n",
    "def mrr(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += mr(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def ndcgunique(pred, target):\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1 / (log2(i + 2))\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "def ndcg(pred, target):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += ndcgunique(cur_pred, cur_target)\n",
    "    return sum_metric / len(pred) \n",
    "\n",
    "\n",
    "def precisionunique(pred, target, k):\n",
    "    if len(pred) >= k:\n",
    "        pred = pred[:k]\n",
    "\n",
    "    ans, cnt = 0, 0\n",
    "    tot = len(pred)\n",
    "    s = set()\n",
    "    for i in range(len(pred)):\n",
    "        if pred[i] in target and pred[i] not in s:\n",
    "            ans += 1\n",
    "            s.add(pred[i])\n",
    "    return ans / tot\n",
    "\n",
    "\n",
    "def precision(pred, target, k):\n",
    "    assert len(pred) == len(target)\n",
    "    sum_metric = 0\n",
    "    for cur_pred, cur_target in zip(pred, target):\n",
    "        sum_metric += precisionunique(cur_pred, cur_target, k)\n",
    "    return sum_metric / len(pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "206ecde4",
   "metadata": {
    "execution": {
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    "id": "sDWmsB_C8-yw",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "class EmbeddingMethodsDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return {\n",
    "            'user' : torch.LongTensor([df['userId:token'].iloc[index]]).item(),\n",
    "            'item' : torch.LongTensor([df['movieId:token'].iloc[index]]).item(),\n",
    "            'rating' : torch.FloatTensor([df['rating'].iloc[index]]).item()\n",
    "        }\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cacd83a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T15:29:07.683292Z",
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     "shell.execute_reply": "2023-01-07T15:29:07.687346Z"
    },
    "id": "03o_EZBUCUxh",
    "papermill": {
     "duration": 0.017748,
     "end_time": "2023-01-07T15:29:07.690710",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = EmbeddingMethodsDataset(df)\n",
    "loader = torch.utils.data.DataLoader(dataset, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4f2144",
   "metadata": {
    "id": "GE1yshY7LLsL",
    "papermill": {
     "duration": 0.00878,
     "end_time": "2023-01-07T15:29:07.708617",
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     "start_time": "2023-01-07T15:29:07.699837",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## SLIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d37156dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T15:29:07.727572Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn \n",
    "\n",
    "class SLIM(nn.Module):\n",
    "    def __init__(self, n_users, n_items, hidden_dim):\n",
    "        super().__init__()\n",
    "\n",
    "        self.embeddings_users = nn.Embedding(n_users, hidden_dim)\n",
    "        self.embeddings_items = nn.Embedding(n_items, hidden_dim) \n",
    "\n",
    "    def forward(self, users, items):\n",
    "        users_hidden = self.embeddings_users(users)\n",
    "        items_hidden = self.embeddings_items(items)\n",
    "\n",
    "        return torch.sum(users_hidden * items_hidden, -1), users_hidden, items_hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6511298",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-01-07T15:29:07.762244Z"
    },
    "id": "AsII30qVFkQ9",
    "papermill": {
     "duration": 0.020246,
     "end_time": "2023-01-07T15:29:07.765190",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SLIMLoss(nn.Module):\n",
    "    def __init__(self, l, b):\n",
    "        super().__init__()\n",
    "        self.l = l \n",
    "        self.b = b\n",
    "        self.mse = nn.MSELoss()\n",
    "        self.mae = nn.L1Loss()\n",
    "\n",
    "    def forward(self, user_embeddings, item_embeddings, pred, target):\n",
    "        l2 = self.mse(user_embeddings.float(), user_embeddings.float()) + self.mse(item_embeddings.float(), item_embeddings.float())\n",
    "        l1 = self.mae(user_embeddings.float(), user_embeddings.float()) + self.mae(item_embeddings.float(), item_embeddings.float())\n",
    "        diff = self.mse(pred.float(), target.float())\n",
    "        return diff + self.l * l1 + self.b * l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b61742b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T15:29:07.783849Z",
     "iopub.status.busy": "2023-01-07T15:29:07.783551Z",
     "iopub.status.idle": "2023-01-07T15:29:10.866408Z",
     "shell.execute_reply": "2023-01-07T15:29:10.865407Z"
    },
    "id": "Avj9ZmvwHTzU",
    "papermill": {
     "duration": 3.094835,
     "end_time": "2023-01-07T15:29:10.868795",
     "exception": false,
     "start_time": "2023-01-07T15:29:07.773960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = SLIM(N_USERS, N_ITEMS, 64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "criterion = SLIMLoss(0.1, 0.1).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7863e78",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-01-07T16:37:49.754581Z"
    },
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    "outputId": "c3e03a51-5b93-4efc-a7ff-1d7572d43014",
    "papermill": {
     "duration": 4118.880448,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f710177cbac14bdeae76f139600997da",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49b42b55977140898de6ae0b9fa4b4f9",
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       "version_minor": 0
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       "  0%|          | 0/312505 [00:00<?, ?it/s]"
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    {
     "data": {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
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    {
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       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "\n",
    "epochs = 3\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    for batch in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        for key in batch.keys():\n",
    "            batch[key] = batch[key].to(device)\n",
    "        pred, user_emb, item_emb = model(batch['user'], batch['item'])\n",
    "        loss = criterion(user_emb, item_emb, pred, batch['rating']) + 0.0\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3b1794d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:37:49.779894Z",
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     "shell.execute_reply": "2023-01-07T16:37:50.003391Z"
    },
    "id": "qOrBV2NQJS3j",
    "papermill": {
     "duration": 0.23875,
     "end_time": "2023-01-07T16:37:50.007095",
     "exception": false,
     "start_time": "2023-01-07T16:37:49.768345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'slim.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c060c4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:37:50.030042Z",
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class FastKNN:\n",
    "    def __init__(self, num_projections = 10):\n",
    "        self.num_projections = num_projections\n",
    "        self.distributions = []\n",
    "        self.projection_matrixes = []\n",
    "        self.embeddings = None\n",
    "\n",
    "    def binary_search(self, index_projection, find_value):\n",
    "        left_index, right_index = 0, len(self.distributions[index_projection]) - 1\n",
    "        while left_index < right_index - 1:\n",
    "            middle_index = (left_index + right_index) // 2\n",
    "            middle_value = self.distributions[index_projection][middle_index][0]\n",
    "            if middle_value < find_value:\n",
    "                left_index = middle_index\n",
    "            else:\n",
    "                right_index = middle_index\n",
    "\n",
    "        return left_index\n",
    "\n",
    "    def find_k_nearest(self, index_projection, find_value, k):\n",
    "        i0 = self.binary_search(index_projection, find_value)\n",
    "        left_index = max(0, i0 - k // 2)\n",
    "        right_index = min(len(self.distributions[index_projection]), left_index + k )\n",
    "        if right_index - left_index != k:\n",
    "            left_index = right_index - k\n",
    "        return [self.distributions[index_projection][i][1] for i in range(left_index, right_index)]\n",
    "\n",
    "    def fit(self, vectors):\n",
    "        for i in trange(self.num_projections):\n",
    "            projection = torch.randn((vectors.shape[1], 1))\n",
    "            values = (vectors @ projection).cpu().numpy().reshape(-1)\n",
    "            self.distributions.append(sorted([(values[j], j) for j in range(len(values))]))\n",
    "            self.projection_matrixes.append(projection.cpu())\n",
    "\n",
    "    def predict(self, vector, k):\n",
    "        k0 = 1 + (k // self.num_projections)\n",
    "        values = [torch.sum(vector * proj.reshape(-1)).item() for proj in self.projection_matrixes]\n",
    "        neighb = []\n",
    "        for i, value in enumerate(values):\n",
    "            cur_n = self.find_k_nearest(i, value, k0)\n",
    "            neighb += cur_n\n",
    "        neighb = list(set(neighb))\n",
    "        return neighb[:k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b850c64d",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "359cb75e615148cda8f8d061f523f94b",
       "version_major": 2,
       "version_minor": 0
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       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vectors = model.embeddings_items.weight.cpu().detach()\n",
    "knn = FastKNN()\n",
    "knn.fit(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25aeedce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:37:51.951775Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "len(knn.predict(vectors[64497], 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22ee9f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:37:51.981825Z",
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     "shell.execute_reply": "2023-01-07T16:37:51.985923Z"
    },
    "id": "at8k7zNUihIm",
    "outputId": "93fe0ebd-c8b9-47ce-d4c1-4687bfae5693",
    "papermill": {
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     "end_time": "2023-01-07T16:37:51.988750",
     "exception": false,
     "start_time": "2023-01-07T16:37:51.970781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([131263, 64])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b187aedd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:37:52.010309Z",
     "iopub.status.busy": "2023-01-07T16:37:52.008774Z",
     "iopub.status.idle": "2023-01-07T16:39:08.062352Z",
     "shell.execute_reply": "2023-01-07T16:39:08.061275Z"
    },
    "id": "9YAgPjL9bLxl",
    "outputId": "832d53ff-3fab-4226-cb2c-7b95e8fcf73e",
    "papermill": {
     "duration": 76.066973,
     "end_time": "2023-01-07T16:39:08.065361",
     "exception": false,
     "start_time": "2023-01-07T16:37:51.998388",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9723deb1c6014743bf2c311fdda5ac7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4000053 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_test = [[] for i in range(df['userId:token'].max() + 1)]\n",
    "for i in trange(df_test.shape[0]):\n",
    "    usr, item = df_test['userId:token'].iloc[i], df_test['movieId:token'].iloc[i]\n",
    "    g_test[usr].append(item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c41d38a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:39:08.087097Z",
     "iopub.status.busy": "2023-01-07T16:39:08.086183Z",
     "iopub.status.idle": "2023-01-07T16:46:44.427220Z",
     "shell.execute_reply": "2023-01-07T16:46:44.426175Z"
    },
    "id": "lbkpgho8bWiN",
    "outputId": "f4af2598-0a71-4560-f26a-ef01d59f8e04",
    "papermill": {
     "duration": 456.354366,
     "end_time": "2023-01-07T16:46:44.429946",
     "exception": false,
     "start_time": "2023-01-07T16:39:08.075580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f00ef79dfee4edda944ea108da9db05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16000210 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_train = [[] for i in range(df_train['userId:token'].max() + 1)]\n",
    "for i in trange(df_train.shape[0]):\n",
    "    cur_user = df['userId:token'].iloc[i]\n",
    "    cur_item = df['movieId:token'].iloc[i]\n",
    "    cur_rating = df['rating'].iloc[i]\n",
    "    g_train[cur_user].append((cur_rating, cur_item))\n",
    "\n",
    "for i in range(len(g_train)):\n",
    "    g_train[i] = sorted(g_train[i])\n",
    "    g_train[i] = g_train[i][-5:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "113a6cf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:46:44.451752Z",
     "iopub.status.busy": "2023-01-07T16:46:44.451436Z",
     "iopub.status.idle": "2023-01-07T16:46:57.443388Z",
     "shell.execute_reply": "2023-01-07T16:46:57.442290Z"
    },
    "id": "qlUYPhd7bY9O",
    "outputId": "30f745bd-9d21-44c6-9511-d3beb7da786c",
    "papermill": {
     "duration": 13.006112,
     "end_time": "2023-01-07T16:46:57.446484",
     "exception": false,
     "start_time": "2023-01-07T16:46:44.440372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f927b341f6d4762ba4e457b54557647",
       "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 or len(g_train[i]) == 0:\n",
    "        continue\n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.predict(vectors[cur_item], k)[:(k // 5)]\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    \n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5449c097",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:46:57.468379Z",
     "iopub.status.busy": "2023-01-07T16:46:57.467793Z",
     "iopub.status.idle": "2023-01-07T16:47:35.746864Z",
     "shell.execute_reply": "2023-01-07T16:47:35.745357Z"
    },
    "id": "1wkDkyEbbbRL",
    "outputId": "e13d4f0b-9592-4cb8-a7e5-c53842fc01e2",
    "papermill": {
     "duration": 38.292109,
     "end_time": "2023-01-07T16:47:35.749095",
     "exception": false,
     "start_time": "2023-01-07T16:46:57.456986",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.005139110402268297\n",
      "precision@2 : 0.006290979975190502\n",
      "precision@3 : 0.005907023450883094\n",
      "precision@4 : 0.005626439836966153\n",
      "precision@5 : 0.005812511075668958\n",
      "precision@6 : 0.0057593478646110335\n",
      "precision@7 : 0.0053416369205842775\n",
      "precision@8 : 0.0052720184299131665\n",
      "precision@9 : 0.005080040167759451\n",
      "precision@10 : 0.005387205387205412\n",
      "precision@11 : 0.0054774217453643186\n",
      "precision@12 : 0.005508299367948471\n",
      "precision@13 : 0.005602584549952955\n",
      "precision@14 : 0.00549353180932132\n",
      "precision@15 : 0.005493531809321268\n",
      "precision@16 : 0.005416002126528443\n",
      "precision@17 : 0.005257685211245566\n",
      "precision@18 : 0.00556983086222857\n",
      "precision@19 : 0.005587966460542684\n",
      "ndcg: 0.001474809113499101\n",
      "mrr: 0.020730975743407275\n",
      "mapk@1 : 0.005139110402268297\n",
      "mapk@2 : 0.004474570264043948\n",
      "mapk@3 : 0.0035934392659538843\n",
      "mapk@4 : 0.0030162738496071835\n",
      "mapk@5 : 0.0026894677771870704\n",
      "mapk@6 : 0.002408588812097584\n",
      "mapk@7 : 0.0021223694156024967\n",
      "mapk@8 : 0.0019318340042024265\n",
      "mapk@9 : 0.001760941510779066\n",
      "mapk@10 : 0.001676996925534935\n",
      "mapk@11 : 0.0015933765692114766\n",
      "mapk@12 : 0.0015147429034102833\n",
      "mapk@13 : 0.0014506534207593363\n",
      "mapk@14 : 0.0013750635427934726\n",
      "mapk@15 : 0.0013101711462512441\n",
      "mapk@16 : 0.0012490523289300538\n",
      "mapk@17 : 0.0011894871222588822\n",
      "mapk@18 : 0.0011653997492017342\n",
      "mapk@19 : 0.0011246214277273658\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)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d59fad9",
   "metadata": {
    "id": "6xuuz2vILPmN",
    "papermill": {
     "duration": 0.011608,
     "end_time": "2023-01-07T16:47:35.772599",
     "exception": false,
     "start_time": "2023-01-07T16:47:35.760991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfddd0ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:47:35.797552Z",
     "iopub.status.busy": "2023-01-07T16:47:35.796700Z",
     "iopub.status.idle": "2023-01-07T16:47:35.961145Z",
     "shell.execute_reply": "2023-01-07T16:47:35.960140Z"
    },
    "id": "vJEbVaQ1LPJN",
    "papermill": {
     "duration": 0.179617,
     "end_time": "2023-01-07T16:47:35.963842",
     "exception": false,
     "start_time": "2023-01-07T16:47:35.784225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = SLIM(N_USERS, N_ITEMS, 64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "criterion = SLIMLoss(0.1, 0.0).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "525f67b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T16:47:35.989182Z",
     "iopub.status.busy": "2023-01-07T16:47:35.988880Z",
     "iopub.status.idle": "2023-01-07T17:57:58.251577Z",
     "shell.execute_reply": "2023-01-07T17:57:58.250598Z"
    },
    "id": "mJM6MV89L7_U",
    "outputId": "720e8483-bdb0-4a75-db79-cdd59349ae45",
    "papermill": {
     "duration": 4222.278337,
     "end_time": "2023-01-07T17:57:58.254195",
     "exception": false,
     "start_time": "2023-01-07T16:47:35.975858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b65a6d1b8586402c91f6ce6b76176e9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a016308341e641749dfe7b103de98e57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ba877db2e094b62a241c3133f0bc346",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "826441a105e2405e80c5d0f34dd1a9db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "\n",
    "epochs = 3\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    for batch in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        for key in batch.keys():\n",
    "            batch[key] = batch[key].to(device)\n",
    "        pred, user_emb, item_emb = model(batch['user'], batch['item'])\n",
    "        loss = criterion(user_emb, item_emb, pred, batch['rating']) + 0.0\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e2b4dec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T17:57:58.280893Z",
     "iopub.status.busy": "2023-01-07T17:57:58.280575Z",
     "iopub.status.idle": "2023-01-07T17:58:51.665958Z",
     "shell.execute_reply": "2023-01-07T17:58:51.664379Z"
    },
    "id": "qYF3I_nEHkCd",
    "outputId": "e1ef84f8-28dc-4db3-9095-ddd28cba79fa",
    "papermill": {
     "duration": 53.401014,
     "end_time": "2023-01-07T17:58:51.668050",
     "exception": false,
     "start_time": "2023-01-07T17:57:58.267036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cd052c1c5014e6d976016347e41298b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0332d6331ba4310ab599d28931dee97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/138494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.004075846181109339\n",
      "precision@2 : 0.00354421407052986\n",
      "precision@3 : 0.004548408057179982\n",
      "precision@4 : 0.0052720184299131665\n",
      "precision@5 : 0.005245436824384181\n",
      "precision@6 : 0.0056412073955933745\n",
      "precision@7 : 0.005468215994531771\n",
      "precision@8 : 0.005427077795498848\n",
      "precision@9 : 0.005276940949455554\n",
      "precision@10 : 0.005192273613326268\n",
      "precision@11 : 0.005139110402268286\n",
      "precision@12 : 0.005404926457558012\n",
      "precision@13 : 0.005316321105794775\n",
      "precision@14 : 0.005544163438900321\n",
      "precision@15 : 0.005410833481008905\n",
      "precision@16 : 0.005338472443735602\n",
      "precision@17 : 0.00530785147658211\n",
      "precision@18 : 0.005290477878197222\n",
      "precision@19 : 0.005228298683977394\n",
      "ndcg: 0.0014307231625157065\n",
      "mrr: 0.018885980910737354\n",
      "mapk@1 : 0.004075846181109339\n",
      "mapk@2 : 0.002835371256423888\n",
      "mapk@3 : 0.0026581605528973942\n",
      "mapk@4 : 0.002469874180400498\n",
      "mapk@5 : 0.002181463760411126\n",
      "mapk@6 : 0.0020443223659597914\n",
      "mapk@7 : 0.0018463064828979597\n",
      "mapk@8 : 0.0017041235242989641\n",
      "mapk@9 : 0.0015782222734789322\n",
      "mapk@10 : 0.0014700190431184562\n",
      "mapk@11 : 0.0013803174863539296\n",
      "mapk@12 : 0.0013391288222938075\n",
      "mapk@13 : 0.001264430682089045\n",
      "mapk@14 : 0.0012238416981334877\n",
      "mapk@15 : 0.0011627299328876488\n",
      "mapk@16 : 0.0011101339620910328\n",
      "mapk@17 : 0.0010640958996331353\n",
      "mapk@18 : 0.0010238166440551914\n",
      "mapk@19 : 0.0009863591881435951\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vectors = model.embeddings_items.weight.cpu().detach()\n",
    "knn = FastKNN()\n",
    "knn.fit(vectors)\n",
    "\n",
    "predictions = []\n",
    "target = []\n",
    "k = 40\n",
    "for i in trange(df_train['userId:token'].max() + 1):\n",
    "    if len(g_test[i]) == 0 or len(g_train[i]) == 0:\n",
    "        continue\n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.predict(vectors[cur_item], k)[:(k // 5)]\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    \n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n",
    "\n",
    "for i in range(1, 20):\n",
    "    print(f\"precision@{i} : {precision(predictions, target, i)}\")\n",
    "print(f\"ndcg: {ndcg(predictions, target)}\")\n",
    "print(f\"mrr: {mrr(predictions, target)}\")\n",
    "for i in range(1, 20):\n",
    "    print(f\"mapk@{i} : {mapk(predictions, target, i)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a420dca3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T17:58:51.698652Z",
     "iopub.status.busy": "2023-01-07T17:58:51.698316Z",
     "iopub.status.idle": "2023-01-07T17:58:51.899265Z",
     "shell.execute_reply": "2023-01-07T17:58:51.898261Z"
    },
    "id": "l35U8RbRL97l",
    "papermill": {
     "duration": 0.218698,
     "end_time": "2023-01-07T17:58:51.901803",
     "exception": false,
     "start_time": "2023-01-07T17:58:51.683105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'als.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96d4afc",
   "metadata": {
    "id": "We1W3-j1MBfm",
    "papermill": {
     "duration": 0.01416,
     "end_time": "2023-01-07T17:58:51.930885",
     "exception": false,
     "start_time": "2023-01-07T17:58:51.916725",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Implicit ALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "80df6bd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T17:58:51.961284Z",
     "iopub.status.busy": "2023-01-07T17:58:51.960913Z",
     "iopub.status.idle": "2023-01-07T17:58:51.969018Z",
     "shell.execute_reply": "2023-01-07T17:58:51.968167Z"
    },
    "id": "8HmYEfp_MA7l",
    "papermill": {
     "duration": 0.025612,
     "end_time": "2023-01-07T17:58:51.971007",
     "exception": false,
     "start_time": "2023-01-07T17:58:51.945395",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ImplicitALSLoss(nn.Module):\n",
    "    def __init__(self, df):\n",
    "        super().__init__()\n",
    "        self.df = df\n",
    "        self.user_degree = df['userId:token'].value_counts()\n",
    "        self.users_set = set(self.user_degree.index)\n",
    "\n",
    "    def forward(self, user_ids, pred, target):\n",
    "        weights = torch.tensor([self.user_degree[x.item()] if x.item() in self.users_set else 1 for x in user_ids]).to(device)\n",
    "        difference = (pred - target) * (pred - target)\n",
    "        return torch.sum(weights * difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b624281",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T17:58:52.002107Z",
     "iopub.status.busy": "2023-01-07T17:58:52.000458Z",
     "iopub.status.idle": "2023-01-07T17:58:52.386767Z",
     "shell.execute_reply": "2023-01-07T17:58:52.385596Z"
    },
    "id": "nFnbg4i5Nm9q",
    "papermill": {
     "duration": 0.404304,
     "end_time": "2023-01-07T17:58:52.389462",
     "exception": false,
     "start_time": "2023-01-07T17:58:51.985158",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = SLIM(N_USERS, N_ITEMS, 64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "criterion = ImplicitALSLoss(df_train).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5c9c13d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T17:58:52.425139Z",
     "iopub.status.busy": "2023-01-07T17:58:52.424770Z",
     "iopub.status.idle": "2023-01-07T19:29:15.716573Z",
     "shell.execute_reply": "2023-01-07T19:29:15.715591Z"
    },
    "id": "LWuZEV7tOcI-",
    "outputId": "ba0645fe-0283-4508-f551-8ecb51f71d56",
    "papermill": {
     "duration": 5423.31242,
     "end_time": "2023-01-07T19:29:15.719117",
     "exception": false,
     "start_time": "2023-01-07T17:58:52.406697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fa8180ecfb248439d9461b8ed34f052",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6168fc6cb60d4fb19d489b897fde8c6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fb84cfe7c4d4bd18e6e49a06db8d59b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d034c24e29b5439786e7ccbc64adf1d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "\n",
    "epochs = 3\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    for batch in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        for key in batch.keys():\n",
    "            batch[key] = batch[key].to(device)\n",
    "        pred, user_emb, item_emb = model(batch['user'], batch['item'])\n",
    "        loss = criterion(batch['user'], pred, batch['rating']) + 0.0\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84a2b807",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T19:29:15.751110Z",
     "iopub.status.busy": "2023-01-07T19:29:15.749621Z",
     "iopub.status.idle": "2023-01-07T19:30:09.065535Z",
     "shell.execute_reply": "2023-01-07T19:30:09.064386Z"
    },
    "id": "RWiTWZhwH1p8",
    "papermill": {
     "duration": 53.334033,
     "end_time": "2023-01-07T19:30:09.068133",
     "exception": false,
     "start_time": "2023-01-07T19:29:15.734100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7456d8f54fc427f807f44c45ed57a20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebb3fedce18c4108aae26355794cbefd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/138494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.003012581959950381\n",
      "precision@2 : 0.0034556087187666137\n",
      "precision@3 : 0.0032488628979857053\n",
      "precision@4 : 0.0033227006911217436\n",
      "precision@5 : 0.003437887648413958\n",
      "precision@6 : 0.0034260736015121985\n",
      "precision@7 : 0.0036454773296878498\n",
      "precision@8 : 0.0036328194222931065\n",
      "precision@9 : 0.003563904148699461\n",
      "precision@10 : 0.0035796562112351596\n",
      "precision@11 : 0.00352810400657291\n",
      "precision@12 : 0.0036032843050386896\n",
      "precision@13 : 0.003626003626003617\n",
      "precision@14 : 0.003594845700108866\n",
      "precision@15 : 0.0037450528678598764\n",
      "precision@16 : 0.0037435761119971648\n",
      "precision@17 : 0.0037344549728450544\n",
      "precision@18 : 0.0037263472935987656\n",
      "precision@19 : 0.0037004392960071717\n",
      "ndcg: 0.0010193366822890942\n",
      "mrr: 0.013929539240239127\n",
      "mapk@1 : 0.003012581959950381\n",
      "mapk@2 : 0.002480949849370902\n",
      "mapk@3 : 0.001969007816961033\n",
      "mapk@4 : 0.001698269242128891\n",
      "mapk@5 : 0.001528737669088545\n",
      "mapk@6 : 0.001367475928879437\n",
      "mapk@7 : 0.0012733854839117984\n",
      "mapk@8 : 0.001169590643274855\n",
      "mapk@9 : 0.001079016283694645\n",
      "mapk@10 : 0.0010136452241715386\n",
      "mapk@11 : 0.0009493221324452301\n",
      "mapk@12 : 0.0009071308513094801\n",
      "mapk@13 : 0.0008614689880792189\n",
      "mapk@14 : 0.0008171140775378638\n",
      "mapk@15 : 0.0007925687245198133\n",
      "mapk@16 : 0.0007610311413142373\n",
      "mapk@17 : 0.00073255800944498\n",
      "mapk@18 : 0.0007053181590739824\n",
      "mapk@19 : 0.0006788184784134659\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vectors = model.embeddings_items.weight.cpu().detach()\n",
    "knn = FastKNN()\n",
    "knn.fit(vectors)\n",
    "\n",
    "predictions = []\n",
    "target = []\n",
    "k = 40\n",
    "for i in trange(df_train['userId:token'].max() + 1):\n",
    "    if len(g_test[i]) == 0 or len(g_train[i]) == 0:\n",
    "        continue\n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.predict(vectors[cur_item], k)[:(k // 5)]\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    \n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n",
    "\n",
    "for i in range(1, 20):\n",
    "    print(f\"precision@{i} : {precision(predictions, target, i)}\")\n",
    "print(f\"ndcg: {ndcg(predictions, target)}\")\n",
    "print(f\"mrr: {mrr(predictions, target)}\")\n",
    "for i in range(1, 20):\n",
    "    print(f\"mapk@{i} : {mapk(predictions, target, i)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cfb1d50c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T19:30:09.103269Z",
     "iopub.status.busy": "2023-01-07T19:30:09.102943Z",
     "iopub.status.idle": "2023-01-07T19:30:09.307138Z",
     "shell.execute_reply": "2023-01-07T19:30:09.306161Z"
    },
    "id": "RymZinEsRX-y",
    "papermill": {
     "duration": 0.224474,
     "end_time": "2023-01-07T19:30:09.309769",
     "exception": false,
     "start_time": "2023-01-07T19:30:09.085295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model, 'implicitals.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a82a84",
   "metadata": {
    "id": "r8V-2GEOSDU7",
    "papermill": {
     "duration": 0.016626,
     "end_time": "2023-01-07T19:30:09.344022",
     "exception": false,
     "start_time": "2023-01-07T19:30:09.327396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## SVD++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed46db2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T19:30:09.378528Z",
     "iopub.status.busy": "2023-01-07T19:30:09.378193Z",
     "iopub.status.idle": "2023-01-07T19:30:09.385442Z",
     "shell.execute_reply": "2023-01-07T19:30:09.384379Z"
    },
    "id": "zY8R5ZasSCy7",
    "papermill": {
     "duration": 0.027005,
     "end_time": "2023-01-07T19:30:09.387570",
     "exception": false,
     "start_time": "2023-01-07T19:30:09.360565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn \n",
    "\n",
    "class SVDPlusPlus(nn.Module):\n",
    "    def __init__(self, n_users, n_items, hidden_dim):\n",
    "        super().__init__()\n",
    "\n",
    "        self.b_embeddings_users = nn.Embedding(n_users, 1)\n",
    "        self.b_embeddings_items = nn.Embedding(n_items, 1)\n",
    "\n",
    "        self.embeddings_users = nn.Embedding(n_users, hidden_dim)\n",
    "        self.embeddings_items = nn.Embedding(n_items, hidden_dim) \n",
    "\n",
    "    def forward(self, users, items):\n",
    "        users_b = self.b_embeddings_users(users).squeeze(1)\n",
    "        items_b = self.b_embeddings_items(items).squeeze(1)\n",
    "\n",
    "        users_hidden = self.embeddings_users(users)\n",
    "        items_hidden = self.embeddings_items(items)\n",
    "\n",
    "        return torch.sum(users_hidden * items_hidden, -1) + users_b + items_b, users_hidden, items_hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3de4f956",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T19:30:09.423352Z",
     "iopub.status.busy": "2023-01-07T19:30:09.421772Z",
     "iopub.status.idle": "2023-01-07T19:30:09.593551Z",
     "shell.execute_reply": "2023-01-07T19:30:09.592553Z"
    },
    "id": "uGqv2-5aU6ct",
    "papermill": {
     "duration": 0.191941,
     "end_time": "2023-01-07T19:30:09.596103",
     "exception": false,
     "start_time": "2023-01-07T19:30:09.404162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = SVDPlusPlus(N_USERS, N_ITEMS, 64).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
    "criterion = SLIMLoss(0.1, 0.0).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "612c0166",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T19:30:09.631364Z",
     "iopub.status.busy": "2023-01-07T19:30:09.631043Z",
     "iopub.status.idle": "2023-01-07T20:45:24.193610Z",
     "shell.execute_reply": "2023-01-07T20:45:24.192577Z"
    },
    "id": "jNUCtFSxVK1Y",
    "outputId": "bcae7f53-e7e0-480a-df9c-805df6fdf961",
    "papermill": {
     "duration": 4514.58383,
     "end_time": "2023-01-07T20:45:24.197272",
     "exception": false,
     "start_time": "2023-01-07T19:30:09.613442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15722480a6bb4661b624a9c027700be6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ea8b494bf664585b04b6363127ad916",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05b1f61427aa40d7935ae30ffbfce871",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce371710861f410b9c18d250f74ad2b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/312505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import trange, tqdm\n",
    "\n",
    "epochs = 3\n",
    "\n",
    "for _ in trange(epochs):\n",
    "    for batch in tqdm(loader):\n",
    "        optimizer.zero_grad()\n",
    "        for key in batch.keys():\n",
    "            batch[key] = batch[key].to(device)\n",
    "        pred, user_emb, item_emb = model(batch['user'], batch['item'])\n",
    "        loss = criterion(user_emb, item_emb, pred, batch['rating']) + 0.0\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d695563c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-07T20:45:24.235218Z",
     "iopub.status.busy": "2023-01-07T20:45:24.233645Z",
     "iopub.status.idle": "2023-01-07T20:46:16.856078Z",
     "shell.execute_reply": "2023-01-07T20:46:16.854670Z"
    },
    "id": "vsjLldCkVQoj",
    "papermill": {
     "duration": 52.64307,
     "end_time": "2023-01-07T20:46:16.858201",
     "exception": false,
     "start_time": "2023-01-07T20:45:24.215131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fd7c19df3694fb5b189206a37c6f7e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d116d434b09b4164b1ba45b32bd628a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/138494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision@1 : 0.006379585326953748\n",
      "precision@2 : 0.005936558568137515\n",
      "precision@3 : 0.005788882981865431\n",
      "precision@4 : 0.006645401382243487\n",
      "precision@5 : 0.006592238171185537\n",
      "precision@6 : 0.00747238466536711\n",
      "precision@7 : 0.007392217918533727\n",
      "precision@8 : 0.007066276803118908\n",
      "precision@9 : 0.007147498375568568\n",
      "precision@10 : 0.007159312422470361\n",
      "precision@11 : 0.006959547629404103\n",
      "precision@12 : 0.007339476637722264\n",
      "precision@13 : 0.007361059992638983\n",
      "precision@14 : 0.007506139085086453\n",
      "precision@15 : 0.0074428495481127606\n",
      "precision@16 : 0.007265638844586213\n",
      "precision@17 : 0.007143154975972357\n",
      "precision@18 : 0.007053970504262983\n",
      "precision@19 : 0.006974173871680872\n",
      "ndcg: 0.0018744111528039926\n",
      "mrr: 0.025350579771252026\n",
      "mapk@1 : 0.006379585326953748\n",
      "mapk@2 : 0.004607478291688818\n",
      "mapk@3 : 0.0037214247740563525\n",
      "mapk@4 : 0.003378079035973773\n",
      "mapk@5 : 0.0029930887825624654\n",
      "mapk@6 : 0.002824049461476362\n",
      "mapk@7 : 0.0025833583477693767\n",
      "mapk@8 : 0.0023518128233040517\n",
      "mapk@9 : 0.002202077397009169\n",
      "mapk@10 : 0.0020740192231420295\n",
      "mapk@11 : 0.001935266764177892\n",
      "mapk@12 : 0.0018724449246777862\n",
      "mapk@13 : 0.0017871314061965525\n",
      "mapk@14 : 0.001714631473688207\n",
      "mapk@15 : 0.0016349772463541744\n",
      "mapk@16 : 0.0015618647995043534\n",
      "mapk@17 : 0.0014932243024493126\n",
      "mapk@18 : 0.001432114007298254\n",
      "mapk@19 : 0.0013763727833145981\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "vectors = model.embeddings_items.weight.cpu().detach()\n",
    "knn = FastKNN()\n",
    "knn.fit(vectors)\n",
    "\n",
    "predictions = []\n",
    "target = []\n",
    "k = 40\n",
    "for i in trange(df_train['userId:token'].max() + 1):\n",
    "    if len(g_test[i]) == 0 or len(g_train[i]) == 0:\n",
    "        continue\n",
    "    out = []\n",
    "    for l in g_train[i]:\n",
    "        cur_item = l[1]\n",
    "        neighb = knn.predict(vectors[cur_item], k)[:(k // 5)]\n",
    "        for e in neighb:\n",
    "            out.append(e)\n",
    "    \n",
    "    predictions.append(out)\n",
    "    target.append(g_test[i])\n",
    "\n",
    "for i in range(1, 20):\n",
    "    print(f\"precision@{i} : {precision(predictions, target, i)}\")\n",
    "print(f\"ndcg: {ndcg(predictions, target)}\")\n",
    "print(f\"mrr: {mrr(predictions, target)}\")\n",
    "for i in range(1, 20):\n",
    "    print(f\"mapk@{i} : {mapk(predictions, target, i)}\")\n"
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