ss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n",
      "Current cuda device: 0\n",
      "Count of using GPUs: 1\n"
     ]
    }
   ],
   "source": [
    "import os.path as osp\n",
    "import os\n",
    "\n",
    "#torch 전에 할댕해줘야함\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]= \"1\"\n",
    "\n",
    "\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "from torch_geometric.datasets import AmazonBook, MovieLens100K\n",
    "from torch_geometric.nn import LightGCN\n",
    "from torch_geometric.utils import degree\n",
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('Device:', device)\n",
    "print('Current cuda device:', torch.cuda.current_device())\n",
    "print('Count of using GPUs:', torch.cuda.device_count())\n",
    "# 아마존\n",
    "path = osp.join('./', 'data', 'Amazon')\n",
    "dataset = AmazonBook(path)\n",
    "data = dataset[0]\n",
    "num_users, num_books = data['user'].num_nodes, data['book'].num_nodes\n",
    "data = data.to_homogeneous().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('./data/Gowalla/raw/filtered_total.txt',sep = \" \" ,header =None, index_col = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by = 2)\n",
    "df[2] = pd.to_datetime(df[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[             0      1                         2\n",
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       " 777         11   8377 2010-01-15 17:33:44+00:00\n",
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       " ...        ...    ...                       ...\n",
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       " \n",
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       " ...        ...    ...                       ...\n",
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       " ...        ...    ...                       ...\n",
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       " ...        ...    ...                       ...\n",
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       " \n",
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       " ...        ...    ...                       ...\n",
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       " \n",
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       "              0      1                         2\n",
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       " ...        ...    ...                       ...\n",
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       " \n",
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       " 4            0  40979 2010-10-16 18:50:42+00:00\n",
       " ...        ...    ...                       ...\n",
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       " \n",
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       "              0      1                         2\n",
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       " 1355        14  14185 2009-11-22 22:20:26+00:00\n",
       " 1356        14   2524 2009-11-22 18:06:11+00:00\n",
       " ...        ...    ...                       ...\n",
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       " \n",
       " [18694 rows x 3 columns],\n",
       "              0      1                         2\n",
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       " 786         11  22141 2009-12-31 03:58:20+00:00\n",
       " 787         11  21514 2009-12-29 19:55:42+00:00\n",
       " ...        ...    ...                       ...\n",
       " 1025617  29749  11682 2009-12-30 20:51:02+00:00\n",
       " 1025618  29749  17245 2009-12-30 16:54:31+00:00\n",
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       " 1025621  29749  13134 2009-12-29 16:01:50+00:00\n",
       " \n",
       " [36133 rows x 3 columns]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_df = []\n",
    "for i in range(12):\n",
    "    month_df.append(df.loc[df[2].dt.month == i+1])\n",
    "month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_df"
   ]
  }
 ],
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