Tabular - Graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2de3190",
   "metadata": {},
   "source": [
    "## 1. Tabular dataset -> Graph dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4be1775",
   "metadata": {},
   "source": [
    "### 1.1 Homogeneous"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928ec991",
   "metadata": {},
   "source": [
    "- `Nodes` - Football players (by ID)\n",
    "- `Edges` - If they play for the same team (see explanation below)\n",
    "- `Node Features` - The football player's position, specialities, ball control, ...\n",
    "- `Labels` - The football player's overall rating (node-level regression task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "010f6d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b33232ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "player_df = pd.read_csv('./Data/Tabular Dataset/tbl_player.csv')\n",
    "skill_df = pd.read_csv('./Data/Tabular Dataset/tbl_player_skill.csv')\n",
    "team_df = pd.read_csv('./Data/Tabular Dataset/tbl_team.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1becb399",
   "metadata": {},
   "outputs": [],
   "source": [
    "player_df = player_df[['int_player_id', 'str_player_name', 'str_positions', 'int_overall_rating', 'int_team_id']]\n",
    "skill_df = skill_df[['int_player_id', 'int_long_passing', 'int_ball_control', 'int_dribbling']]\n",
    "team_df = team_df[['int_team_id', 'str_team_name', 'int_overall']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6039585c",
   "metadata": {},
   "outputs": [],
   "source": [
    "player_df = player_df.merge(skill_df, on='int_player_id')\n",
    "fifa_df = player_df.merge(team_df, on='int_team_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8cd9e126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Players: 18767\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_player_id</th>\n",
       "      <th>str_player_name</th>\n",
       "      <th>str_positions</th>\n",
       "      <th>int_overall_rating</th>\n",
       "      <th>int_team_id</th>\n",
       "      <th>int_long_passing</th>\n",
       "      <th>int_ball_control</th>\n",
       "      <th>int_dribbling</th>\n",
       "      <th>str_team_name</th>\n",
       "      <th>int_overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW, ST, CF</td>\n",
       "      <td>93</td>\n",
       "      <td>5.0</td>\n",
       "      <td>91</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>92</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>88</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>3</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>GK</td>\n",
       "      <td>91</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>4</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CAM, CM</td>\n",
       "      <td>91</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93</td>\n",
       "      <td>92</td>\n",
       "      <td>88</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     int_player_id                      str_player_name str_positions  \\\n",
       "0                1       Lionel Andrés Messi Cuccittini    RW, ST, CF   \n",
       "33               2  Cristiano Ronaldo dos Santos Aveiro        ST, LW   \n",
       "57               3                            Jan Oblak            GK   \n",
       "121              5        Neymar da Silva Santos Júnior       LW, CAM   \n",
       "89               4                      Kevin De Bruyne       CAM, CM   \n",
       "\n",
       "     int_overall_rating  int_team_id  int_long_passing  int_ball_control  \\\n",
       "0                    93          5.0                91                96   \n",
       "33                   92          6.0                77                92   \n",
       "57                   91          8.0                40                30   \n",
       "121                  91          7.0                81                95   \n",
       "89                   91          2.0                93                92   \n",
       "\n",
       "     int_dribbling        str_team_name  int_overall  \n",
       "0               96         FC Barcelona           84  \n",
       "33              88             Juventus           83  \n",
       "57              12      Atlético Madrid           83  \n",
       "121             95  Paris Saint-Germain           83  \n",
       "89              88      Manchester City           85  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_df = fifa_df.sort_values(by='int_overall_rating', ascending=False)\n",
    "print('Players:', fifa_df.shape[0])\n",
    "fifa_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73f2bfb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(fifa_df['int_player_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6beb2c6f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_long_passing</th>\n",
       "      <th>int_ball_control</th>\n",
       "      <th>int_dribbling</th>\n",
       "      <th>CAM</th>\n",
       "      <th>CB</th>\n",
       "      <th>CDM</th>\n",
       "      <th>CF</th>\n",
       "      <th>CM</th>\n",
       "      <th>GK</th>\n",
       "      <th>LB</th>\n",
       "      <th>LM</th>\n",
       "      <th>LW</th>\n",
       "      <th>LWB</th>\n",
       "      <th>RB</th>\n",
       "      <th>RM</th>\n",
       "      <th>RW</th>\n",
       "      <th>RWB</th>\n",
       "      <th>ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>93</td>\n",
       "      <td>92</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "     int_long_passing  int_ball_control  int_dribbling  CAM  CB  CDM  CF  CM  \\\n",
       "0                  91                96             96    0   0    0   0   0   \n",
       "33                 77                92             88    0   0    0   0   0   \n",
       "57                 40                30             12    0   0    0   0   0   \n",
       "89                 93                92             88    1   0    0   0   0   \n",
       "121                81                95             95    0   0    0   0   0   \n",
       "\n",
       "     GK  LB  LM  LW  LWB  RB  RM  RW  RWB  ST  \n",
       "0     0   0   0   0    0   0   0   1    0   0  \n",
       "33    0   0   0   0    0   0   0   0    0   1  \n",
       "57    1   0   0   0    0   0   0   0    0   0  \n",
       "89    0   0   0   0    0   0   0   0    0   0  \n",
       "121   0   0   0   1    0   0   0   0    0   0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df = fifa_df.sort_values(by='int_player_id')\n",
    "node_features = sorted_df[['str_positions', 'int_long_passing', 'int_ball_control', 'int_dribbling']]\n",
    "\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "\n",
    "positions = node_features['str_positions'].str.split(',', expand=True)\n",
    "node_features['first_position'] = positions[0]\n",
    "\n",
    "node_features = pd.concat([node_features, pd.get_dummies(node_features['first_position'])], axis=1, join='inner')\n",
    "node_features.drop(['str_positions', 'first_position'], axis=1, inplace=True)\n",
    "node_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4a36bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18767, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = node_features.to_numpy() \n",
    "x.shape   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8aa4da07",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>83</td>\n",
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      ],
      "text/plain": [
       "     int_overall\n",
       "0             84\n",
       "33            83\n",
       "57            83\n",
       "89            85\n",
       "121           83"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df = fifa_df.sort_values(by='int_player_id')\n",
    "labels = sorted_df[['int_overall']]\n",
    "labels.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d86fa47c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18767, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = labels.to_numpy() \n",
    "y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07e10ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fifa_df['int_player_id'] = fifa_df.reset_index().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c5f5a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everton                   36\n",
       "Valencia CF               34\n",
       "FC Nantes                 34\n",
       "Villarreal CF             34\n",
       "Real Valladolid CF        34\n",
       "                          ..\n",
       "Wellington Phoenix        19\n",
       "Central Coast Mariners    19\n",
       "Melbourne Victory         19\n",
       "Brisbane Roar             19\n",
       "Adelaide United           19\n",
       "Name: str_team_name, Length: 681, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_df['str_team_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5351614",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools \n",
    "import numpy as np \n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "073f517d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 681/681 [00:01<00:00, 524.92it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[    0,     0,     0, ..., 18704, 18704, 18719],\n",
       "       [    7,    32,    45, ..., 18719, 18751, 18751]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = fifa_df['str_team_name'].unique() \n",
    "all_edges = np.array([], dtype=np.int32).reshape((0, 2))\n",
    "\n",
    "for team in tqdm(teams):\n",
    "    team_df = fifa_df[fifa_df['str_team_name'] == team]\n",
    "    players = team_df['int_player_id'].values \n",
    "    permutations = list(itertools.combinations(players, 2))\n",
    "    edge_sources = [e[0] for e in permutations]\n",
    "    edge_targets = [e[1] for e in permutations]\n",
    "    team_edges = np.column_stack([edge_sources, edge_targets])\n",
    "    all_edges = np.vstack([all_edges, team_edges])\n",
    "    \n",
    "edge_index = all_edges.transpose() \n",
    "edge_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5cd8d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86183\\anaconda3\\lib\\site-packages\\torch_geometric\\typing.py:31: UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: [WinError 127] 找不到指定的程序。\n",
      "  warnings.warn(f\"An issue occurred while importing 'torch-scatter'. \"\n"
     ]
    }
   ],
   "source": [
    "from torch_geometric.data import Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a494e48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Data(x=x, edge_index=edge_index, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "734dac90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data(x=[18767, 18], edge_index=[2, 251934], y=[18767, 1])\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b71e758",
   "metadata": {},
   "source": [
    "### 1.2 Heterogeneous"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d3edd7",
   "metadata": {},
   "source": [
    "- `Nodes` - Users and Animes (two node types with different features = heterogeneous)\n",
    "- `Edges` - If a user has rated a movie / the rating (edge weight)\n",
    "- `Node Features` - The movie attributes and for the users we have no explicit features so we have to figure something out later \n",
    "- `Labels` - The rating for a movie (link prediction regression task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbf73231",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "056b0e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "anime = pd.read_csv('./Data/Tabular Dataset/anime.csv')\n",
    "rating = pd.read_csv('./Data/Tabular Dataset/rating.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e32caf15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anime_id</th>\n",
       "      <th>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32281</td>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5114</td>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28977</td>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>Sci-Fi, Thriller</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.17</td>\n",
       "      <td>673572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9969</td>\n",
       "      <td>Gintama&amp;#039;</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.16</td>\n",
       "      <td>151266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                              name  \\\n",
       "0     32281                    Kimi no Na wa.   \n",
       "1      5114  Fullmetal Alchemist: Brotherhood   \n",
       "2     28977                          Gintama°   \n",
       "3      9253                       Steins;Gate   \n",
       "4      9969                     Gintama&#039;   \n",
       "\n",
       "                                               genre   type episodes  rating  \\\n",
       "0               Drama, Romance, School, Supernatural  Movie        1    9.37   \n",
       "1  Action, Adventure, Drama, Fantasy, Magic, Mili...     TV       64    9.26   \n",
       "2  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.25   \n",
       "3                                   Sci-Fi, Thriller     TV       24    9.17   \n",
       "4  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.16   \n",
       "\n",
       "   members  \n",
       "0   200630  \n",
       "1   793665  \n",
       "2   114262  \n",
       "3   673572  \n",
       "4   151266  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9b2ecf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>24</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>241</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  anime_id  rating\n",
       "0        1        20      -1\n",
       "1        1        24      -1\n",
       "2        1        79      -1\n",
       "3        1       226      -1\n",
       "4        1       241      -1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da7eca7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Dementia</th>\n",
       "      <th>Demons</th>\n",
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       "      <th>Ecchi</th>\n",
       "      <th>...</th>\n",
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       "      <th>Yaoi</th>\n",
       "      <th>Movie</th>\n",
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       "      <th>ONA</th>\n",
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       "      <th>TV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TV</td>\n",
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       "      <td>TV</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TV</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>TV</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TV</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TV</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TV</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    type episodes  Action  Adventure  Cars  Comedy  Dementia  Demons  Drama  \\\n",
       "0     TV       26       1          0     0       0         0       0      0   \n",
       "1  Movie        1       1          0     0       0         0       0      0   \n",
       "2     TV       26       1          0     0       0         0       0      0   \n",
       "3     TV       26       1          0     0       0         0       0      0   \n",
       "4     TV       52       0          1     0       0         0       0      0   \n",
       "5     TV      145       1          0     0       0         0       0      0   \n",
       "6     TV       24       0          0     0       1         0       0      0   \n",
       "7     TV       52       0          0     0       1         0       0      0   \n",
       "8     TV       24       1          0     0       0         0       0      0   \n",
       "9     TV       74       0          0     0       0         0       0      1   \n",
       "\n",
       "   Ecchi  ...  Supernatural  Thriller  Vampire  Yaoi  Movie  Music  ONA  OVA  \\\n",
       "0      0  ...             0         0        0     0      0      0    0    0   \n",
       "1      0  ...             0         0        0     0      1      0    0    0   \n",
       "2      0  ...             0         0        0     0      0      0    0    0   \n",
       "3      0  ...             0         0        0     0      0      0    0    0   \n",
       "4      0  ...             0         0        0     0      0      0    0    0   \n",
       "5      0  ...             0         0        0     0      0      0    0    0   \n",
       "6      0  ...             0         0        0     0      0      0    0    0   \n",
       "7      0  ...             0         0        0     0      0      0    0    0   \n",
       "8      0  ...             0         0        0     0      0      0    0    0   \n",
       "9      0  ...             0         0        0     0      0      0    0    0   \n",
       "\n",
       "   Special  TV  \n",
       "0        0   1  \n",
       "1        0   0  \n",
       "2        0   1  \n",
       "3        0   1  \n",
       "4        0   1  \n",
       "5        0   1  \n",
       "6        0   1  \n",
       "7        0   1  \n",
       "8        0   1  \n",
       "9        0   1  \n",
       "\n",
       "[10 rows x 48 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df = anime.sort_values(by='anime_id').set_index('anime_id')\n",
    "\n",
    "sorted_df = sorted_df.reset_index(drop=False)\n",
    "movie_id_mapping = sorted_df['anime_id']\n",
    "\n",
    "node_features = sorted_df[['type', 'genre', 'episodes']]\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "\n",
    "genres = node_features['genre'].str.split(',', expand=True)\n",
    "node_features['main_genre'] = genres[0] \n",
    "\n",
    "anime_node_features = pd.concat([node_features, pd.get_dummies(node_features['main_genre'])], axis=1, join='inner')\n",
    "anime_node_features = pd.concat([anime_node_features, pd.get_dummies(anime_node_features['type'])], axis=1, join='inner')\n",
    "anime_node_features.drop(['genre', 'main_genre'], axis=1, inplace=True)\n",
    "anime_node_features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7407d906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12294, 48)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = anime_node_features.to_numpy() \n",
    "x.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "995f2349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.712418</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.382979</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.263383</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean  count\n",
       "0 -0.712418    153\n",
       "1  2.666667      3\n",
       "2  7.382979     94\n",
       "3 -1.000000     52\n",
       "4  4.263383    467"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_rating = rating.groupby('user_id')['rating'].mean().rename('mean')\n",
    "num_rating = rating.groupby('user_id')['rating'].count().rename('count')\n",
    "user_node_features = pd.concat([mean_rating, num_rating], axis=1)\n",
    "\n",
    "user_node_features = user_node_features.reset_index(drop=False)\n",
    "user_id_mapping = user_node_features['user_id']\n",
    "\n",
    "user_node_features = user_node_features[['mean', 'count']]\n",
    "user_node_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa4a5b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73515, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = user_node_features.to_numpy()\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d2b774f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rating['rating'].hist() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8900d501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   20,    24,    79, ..., 29481, 34412, 30738], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating['anime_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0eec4dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1,     5,     6, ..., 34522, 34525, 34527], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime['anime_id'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2216053e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{30913, 30924, 20261}\n"
     ]
    }
   ],
   "source": [
    "print(set(rating['anime_id'].unique()) - set(anime['anime_id'].unique()))\n",
    "rating = rating[~rating['anime_id'].isin([30913, 30924, 20261])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9723f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7813732     7\n",
       "7813733     9\n",
       "7813734    10\n",
       "7813735     9\n",
       "7813736     9\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = rating['rating']\n",
    "labels.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a7dca72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7813727,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = labels.to_numpy() \n",
    "y.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "938666bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  anime_id  rating\n",
       "0        0        10      -1\n",
       "1        0        14      -1\n",
       "2        0        58      -1\n",
       "3        0       202      -1\n",
       "4        0       217      -1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_map = movie_id_mapping.reset_index().set_index('anime_id').to_dict()\n",
    "rating['anime_id'] = rating['anime_id'].map(movie_map['index']).astype(int)\n",
    "\n",
    "user_map = user_id_mapping.reset_index().set_index('user_id').to_dict() \n",
    "rating['user_id'] = rating['user_id'].map(user_map['index']).astype(int)\n",
    "\n",
    "rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5340b81e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,     0,     0, ..., 73513, 73514, 73514],\n",
       "       [   10,    14,    58, ...,  8624,   718,  5226]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_index = rating[['user_id', 'anime_id']].values.transpose() \n",
    "edge_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b12e229e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_geometric.data import HeteroData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2750e00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = HeteroData()\n",
    "data['user'].x = user_node_features \n",
    "data['movie'].x = anime_node_features\n",
    "data['user', 'rating', 'movie'].edge_index = edge_index\n",
    "data['user', 'movie'].y = y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bbdad2e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeteroData(\n",
       "  \u001b[1muser\u001b[0m={ x=            mean  count\n",
       "0      -0.712418    153\n",
       "1       2.666667      3\n",
       "2       7.382979     94\n",
       "3      -1.000000     52\n",
       "4       4.263383    467\n",
       "...          ...    ...\n",
       "73510   7.846154     13\n",
       "73511   7.515152     33\n",
       "73512  10.000000      1\n",
       "73513   7.719388    196\n",
       "73514   9.000000      2\n",
       "\n",
       "[73515 rows x 2 columns] },\n",
       "  \u001b[1mmovie\u001b[0m={ x=          type episodes  Action  Adventure  Cars  Comedy  Dementia  Demons  \\\n",
       "0           TV       26       1          0     0       0         0       0   \n",
       "1        Movie        1       1          0     0       0         0       0   \n",
       "2           TV       26       1          0     0       0         0       0   \n",
       "3           TV       26       1          0     0       0         0       0   \n",
       "4           TV       52       0          1     0       0         0       0   \n",
       "...        ...      ...     ...        ...   ...     ...       ...     ...   \n",
       "12289      ONA       18       1          0     0       0         0       0   \n",
       "12290  Special        9       0          0     0       1         0       0   \n",
       "12291       TV  Unknown       0          0     0       0         0       0   \n",
       "12292       TV  Unknown       0          0     0       1         0       0   \n",
       "12293    Movie        1       0          1     0       0         0       0   \n",
       "\n",
       "       Drama  Ecchi  ...  Supernatural  Thriller  Vampire  Yaoi  Movie  Music  \\\n",
       "0          0      0  ...             0         0        0     0      0      0   \n",
       "1          0      0  ...             0         0        0     0      1      0   \n",
       "2          0      0  ...             0         0        0     0      0      0   \n",
       "3          0      0  ...             0         0        0     0      0      0   \n",
       "4          0      0  ...             0         0        0     0      0      0   \n",
       "...      ...    ...  ...           ...       ...      ...   ...    ...    ...   \n",
       "12289      0      0  ...             0         0        0     0      0      0   \n",
       "12290      0      0  ...             0         0        0     0      0      0   \n",
       "12291      1      0  ...             0         0        0     0      0      0   \n",
       "12292      0      0  ...             0         0        0     0      0      0   \n",
       "12293      0      0  ...             0         0        0     0      1      0   \n",
       "\n",
       "       ONA  OVA  Special  TV  \n",
       "0        0    0        0   1  \n",
       "1        0    0        0   0  \n",
       "2        0    0        0   1  \n",
       "3        0    0        0   1  \n",
       "4        0    0        0   1  \n",
       "...    ...  ...      ...  ..  \n",
       "12289    1    0        0   0  \n",
       "12290    0    0        1   0  \n",
       "12291    0    0        0   1  \n",
       "12292    0    0        0   1  \n",
       "12293    0    0        0   0  \n",
       "\n",
       "[12294 rows x 48 columns] },\n",
       "  \u001b[1m(user, rating, movie)\u001b[0m={\n",
       "    edge_index=[2, 7813727],\n",
       "    y=[7813727]\n",
       "  }\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c867761",
   "metadata": {},
   "source": [
    "## 2. Tabular dataset -> Temporal Graph dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa67de19",
   "metadata": {},
   "source": [
    "- `Nodes` - The locations between which the bikers can travel\n",
    "- `Node Features` - Attributes about a location, for example average number of bikers that start / end here, average traffic, ... (whatever is available)\n",
    "- `Edges / Edge weights` - Proximity between two addresses (available) / Connection according to road network (not available here)\n",
    "- `Edge features` - To incorporate the individual bike information, we can simply model this as edge features i.e. number of bikes, average trip duration, ...\n",
    "- `Labels` - The trip duration, which makes it a link-prediction task\n",
    "- `Timesteps` - 60 min steps (we have 1 month of data = ~ 700 graphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d645d35f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>2013-06-01 00:00:01</td>\n",
       "      <td>2013-06-01 00:11:36</td>\n",
       "      <td>444</td>\n",
       "      <td>Broadway &amp; W 24 St</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>19678</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>693</td>\n",
       "      <td>2013-06-01 00:00:08</td>\n",
       "      <td>2013-06-01 00:11:41</td>\n",
       "      <td>444</td>\n",
       "      <td>Broadway &amp; W 24 St</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16649</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2059</td>\n",
       "      <td>2013-06-01 00:00:44</td>\n",
       "      <td>2013-06-01 00:35:03</td>\n",
       "      <td>406</td>\n",
       "      <td>Hicks St &amp; Montague St</td>\n",
       "      <td>40.695128</td>\n",
       "      <td>-73.995951</td>\n",
       "      <td>406.0</td>\n",
       "      <td>Hicks St &amp; Montague St</td>\n",
       "      <td>40.695128</td>\n",
       "      <td>-73.995951</td>\n",
       "      <td>19599</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>2013-06-01 00:01:04</td>\n",
       "      <td>2013-06-01 00:03:07</td>\n",
       "      <td>475</td>\n",
       "      <td>E 15 St &amp; Irving Pl</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>262.0</td>\n",
       "      <td>Washington Park</td>\n",
       "      <td>40.691782</td>\n",
       "      <td>-73.973730</td>\n",
       "      <td>16352</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1521</td>\n",
       "      <td>2013-06-01 00:01:22</td>\n",
       "      <td>2013-06-01 00:26:43</td>\n",
       "      <td>2008</td>\n",
       "      <td>Little West St &amp; 1 Pl</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>310.0</td>\n",
       "      <td>State St &amp; Smith St</td>\n",
       "      <td>40.689269</td>\n",
       "      <td>-73.989129</td>\n",
       "      <td>15567</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           695  2013-06-01 00:00:01  2013-06-01 00:11:36               444   \n",
       "1           693  2013-06-01 00:00:08  2013-06-01 00:11:41               444   \n",
       "2          2059  2013-06-01 00:00:44  2013-06-01 00:35:03               406   \n",
       "3           123  2013-06-01 00:01:04  2013-06-01 00:03:07               475   \n",
       "4          1521  2013-06-01 00:01:22  2013-06-01 00:26:43              2008   \n",
       "\n",
       "       start station name  start station latitude  start station longitude  \\\n",
       "0      Broadway & W 24 St               40.742354               -73.989151   \n",
       "1      Broadway & W 24 St               40.742354               -73.989151   \n",
       "2  Hicks St & Montague St               40.695128               -73.995951   \n",
       "3     E 15 St & Irving Pl               40.735243               -73.987586   \n",
       "4   Little West St & 1 Pl               40.705693               -74.016777   \n",
       "\n",
       "   end station id        end station name  end station latitude  \\\n",
       "0           434.0         9 Ave & W 18 St             40.743174   \n",
       "1           434.0         9 Ave & W 18 St             40.743174   \n",
       "2           406.0  Hicks St & Montague St             40.695128   \n",
       "3           262.0         Washington Park             40.691782   \n",
       "4           310.0     State St & Smith St             40.689269   \n",
       "\n",
       "   end station longitude  bikeid    usertype  birth year  gender  \n",
       "0             -74.003664   19678  Subscriber      1983.0       1  \n",
       "1             -74.003664   16649  Subscriber      1984.0       1  \n",
       "2             -73.995951   19599    Customer         NaN       0  \n",
       "3             -73.973730   16352  Subscriber      1960.0       1  \n",
       "4             -73.989129   15567  Subscriber      1983.0       1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips = pd.read_csv('./Data/Tabular Dataset/201306-citibike-tripdata.csv')\n",
    "trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "83fe872f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>2013-06-01 00:00:01</td>\n",
       "      <td>2013-06-01 00:11:36</td>\n",
       "      <td>444</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>19678</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>693</td>\n",
       "      <td>2013-06-01 00:00:08</td>\n",
       "      <td>2013-06-01 00:11:41</td>\n",
       "      <td>444</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16649</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>2013-06-01 00:01:04</td>\n",
       "      <td>2013-06-01 00:03:07</td>\n",
       "      <td>475</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>262.0</td>\n",
       "      <td>40.691782</td>\n",
       "      <td>-73.973730</td>\n",
       "      <td>16352</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1521</td>\n",
       "      <td>2013-06-01 00:01:22</td>\n",
       "      <td>2013-06-01 00:26:43</td>\n",
       "      <td>2008</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>310.0</td>\n",
       "      <td>40.689269</td>\n",
       "      <td>-73.989129</td>\n",
       "      <td>15567</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2057</td>\n",
       "      <td>2013-06-01 00:02:33</td>\n",
       "      <td>2013-06-01 00:36:50</td>\n",
       "      <td>285</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>532.0</td>\n",
       "      <td>40.710451</td>\n",
       "      <td>-73.960876</td>\n",
       "      <td>15693</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           695  2013-06-01 00:00:01  2013-06-01 00:11:36               444   \n",
       "1           693  2013-06-01 00:00:08  2013-06-01 00:11:41               444   \n",
       "3           123  2013-06-01 00:01:04  2013-06-01 00:03:07               475   \n",
       "4          1521  2013-06-01 00:01:22  2013-06-01 00:26:43              2008   \n",
       "6          2057  2013-06-01 00:02:33  2013-06-01 00:36:50               285   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               40.742354               -73.989151           434.0   \n",
       "1               40.742354               -73.989151           434.0   \n",
       "3               40.735243               -73.987586           262.0   \n",
       "4               40.705693               -74.016777           310.0   \n",
       "6               40.734546               -73.990741           532.0   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.743174             -74.003664   19678  Subscriber   \n",
       "1             40.743174             -74.003664   16649  Subscriber   \n",
       "3             40.691782             -73.973730   16352  Subscriber   \n",
       "4             40.689269             -73.989129   15567  Subscriber   \n",
       "6             40.710451             -73.960876   15693  Subscriber   \n",
       "\n",
       "   birth year  gender  \n",
       "0      1983.0       1  \n",
       "1      1984.0       1  \n",
       "3      1960.0       1  \n",
       "4      1983.0       1  \n",
       "6      1991.0       1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = ['start station name', 'end station name']\n",
    "trips.dropna(inplace=True)\n",
    "trips.drop(cols_to_drop, axis=1, inplace=True)\n",
    "trips.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "006dee43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>2013-06-01 00:00:01</td>\n",
       "      <td>2013-06-01 00:11:36</td>\n",
       "      <td>0</td>\n",
       "      <td>40.742354</td>\n",
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       "      <td>299</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>19678</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>693</td>\n",
       "      <td>2013-06-01 00:00:08</td>\n",
       "      <td>2013-06-01 00:11:41</td>\n",
       "      <td>0</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>299</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16649</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>2013-06-01 00:01:04</td>\n",
       "      <td>2013-06-01 00:03:07</td>\n",
       "      <td>1</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>187</td>\n",
       "      <td>40.691782</td>\n",
       "      <td>-73.973730</td>\n",
       "      <td>16352</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1521</td>\n",
       "      <td>2013-06-01 00:01:22</td>\n",
       "      <td>2013-06-01 00:26:43</td>\n",
       "      <td>2</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>257</td>\n",
       "      <td>40.689269</td>\n",
       "      <td>-73.989129</td>\n",
       "      <td>15567</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2057</td>\n",
       "      <td>2013-06-01 00:02:33</td>\n",
       "      <td>2013-06-01 00:36:50</td>\n",
       "      <td>3</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>280</td>\n",
       "      <td>40.710451</td>\n",
       "      <td>-73.960876</td>\n",
       "      <td>15693</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           695  2013-06-01 00:00:01  2013-06-01 00:11:36                 0   \n",
       "1           693  2013-06-01 00:00:08  2013-06-01 00:11:41                 0   \n",
       "3           123  2013-06-01 00:01:04  2013-06-01 00:03:07                 1   \n",
       "4          1521  2013-06-01 00:01:22  2013-06-01 00:26:43                 2   \n",
       "6          2057  2013-06-01 00:02:33  2013-06-01 00:36:50                 3   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               40.742354               -73.989151             299   \n",
       "1               40.742354               -73.989151             299   \n",
       "3               40.735243               -73.987586             187   \n",
       "4               40.705693               -74.016777             257   \n",
       "6               40.734546               -73.990741             280   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.743174             -74.003664   19678  Subscriber   \n",
       "1             40.743174             -74.003664   16649  Subscriber   \n",
       "3             40.691782             -73.973730   16352  Subscriber   \n",
       "4             40.689269             -73.989129   15567  Subscriber   \n",
       "6             40.710451             -73.960876   15693  Subscriber   \n",
       "\n",
       "   birth year  gender  \n",
       "0      1983.0       1  \n",
       "1      1984.0       1  \n",
       "3      1960.0       1  \n",
       "4      1983.0       1  \n",
       "6      1991.0       1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = trips['start station id'].unique()\n",
    "new_ids = list(range(len(trips['start station id'].unique())))\n",
    "mapping = dict(zip(locations, new_ids))\n",
    "\n",
    "trips['start station id'] = trips['start station id'].map(mapping)\n",
    "trips['end station id'] = trips['end station id'].map(mapping)\n",
    "trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ff7afe0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>2013-06-01 00:00:01</td>\n",
       "      <td>2013-06-01 00:11:36</td>\n",
       "      <td>0</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>299</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>19678</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>693</td>\n",
       "      <td>2013-06-01 00:00:08</td>\n",
       "      <td>2013-06-01 00:11:41</td>\n",
       "      <td>0</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>299</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16649</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>2013-06-01 00:01:04</td>\n",
       "      <td>2013-06-01 00:03:07</td>\n",
       "      <td>1</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>187</td>\n",
       "      <td>40.691782</td>\n",
       "      <td>-73.973730</td>\n",
       "      <td>16352</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1521</td>\n",
       "      <td>2013-06-01 00:01:22</td>\n",
       "      <td>2013-06-01 00:26:43</td>\n",
       "      <td>2</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>257</td>\n",
       "      <td>40.689269</td>\n",
       "      <td>-73.989129</td>\n",
       "      <td>15567</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2057</td>\n",
       "      <td>2013-06-01 00:02:33</td>\n",
       "      <td>2013-06-01 00:36:50</td>\n",
       "      <td>3</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>280</td>\n",
       "      <td>40.710451</td>\n",
       "      <td>-73.960876</td>\n",
       "      <td>15693</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           695  2013-06-01 00:00:01  2013-06-01 00:11:36                 0   \n",
       "1           693  2013-06-01 00:00:08  2013-06-01 00:11:41                 0   \n",
       "3           123  2013-06-01 00:01:04  2013-06-01 00:03:07                 1   \n",
       "4          1521  2013-06-01 00:01:22  2013-06-01 00:26:43                 2   \n",
       "6          2057  2013-06-01 00:02:33  2013-06-01 00:36:50                 3   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               40.742354               -73.989151             299   \n",
       "1               40.742354               -73.989151             299   \n",
       "3               40.735243               -73.987586             187   \n",
       "4               40.705693               -74.016777             257   \n",
       "6               40.734546               -73.990741             280   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.743174             -74.003664   19678  Subscriber   \n",
       "1             40.743174             -74.003664   16649  Subscriber   \n",
       "3             40.691782             -73.973730   16352  Subscriber   \n",
       "4             40.689269             -73.989129   15567  Subscriber   \n",
       "6             40.710451             -73.960876   15693  Subscriber   \n",
       "\n",
       "   birth year  gender  \n",
       "0      1983.0       1  \n",
       "1      1984.0       1  \n",
       "3      1960.0       1  \n",
       "4      1983.0       1  \n",
       "6      1991.0       1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips = trips.sort_values(by='starttime')\n",
    "trips.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0bba07ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta \n",
    "import seaborn as sns \n",
    "\n",
    "sns.set(rc={'figure.figsize': (20, 6)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f66ff290",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trips['starttime'] = pd.to_datetime(trips['starttime'], format='%Y-%m-%d %H:%M:%S')\n",
    "trips['stoptime'] = pd.to_datetime(trips['stoptime'], format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "start_date = datetime.strptime('2013-06-01 00:00:01', '%Y-%m-%d %H:%M:%S')\n",
    "end_date = datetime.strptime('2013-07-01 00:10:34', '%Y-%m-%d %H:%M:%S')\n",
    "interval = timedelta(minutes=60)\n",
    "\n",
    "bucket_elements = []\n",
    "while start_date <= end_date:\n",
    "    bucket_elements.append(trips[((start_date + interval) >= trips['stoptime']) & (start_date <= trips['stoptime'])].shape[0])\n",
    "    start_date += interval \n",
    "    \n",
    "sns.scatterplot(x='index', y='trips_per_hour', data=pd.DataFrame(bucket_elements, columns=['trips_per_hour']).reset_index())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7e3dbff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full shape: (337, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.76524844, 0.79829787],\n",
       "       [0.39452544, 0.36964539],\n",
       "       [0.16691461, 0.18751773],\n",
       "       [0.92740256, 0.86297872],\n",
       "       [0.6155906 , 0.62014184],\n",
       "       [0.27700089, 0.28028369],\n",
       "       [0.62600417, 0.62099291],\n",
       "       [0.27848855, 0.22978723],\n",
       "       [0.12734305, 0.11177305],\n",
       "       [0.2692651 , 0.24879433]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outgoing_trips = trips.groupby('start station id').count()['bikeid'].values \n",
    "incoming_trips = trips.groupby('end station id').count()['bikeid'].values \n",
    "\n",
    "outgoing_trips = (outgoing_trips - np.min(outgoing_trips)) / (np.max(outgoing_trips) - np.min(outgoing_trips))\n",
    "incoming_trips = (incoming_trips - np.min(incoming_trips)) / (np.max(incoming_trips) - np.min(incoming_trips))\n",
    "\n",
    "node_features = np.stack([outgoing_trips, incoming_trips]).transpose() \n",
    "print('Full shape:', node_features.shape)\n",
    "node_features[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09a4ad68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.extmath import cartesian \n",
    "from geopy.distance import geodesic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e414254f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station id</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>0</td>\n",
       "      <td>1229.224446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>1</td>\n",
       "      <td>1618.732623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>2</td>\n",
       "      <td>4307.215223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>3</td>\n",
       "      <td>1452.477060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.983799</td>\n",
       "      <td>40.726218</td>\n",
       "      <td>118</td>\n",
       "      <td>2522.253218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   end station longitude  end station latitude  end station id  \\\n",
       "0             -74.003664             40.743174             299   \n",
       "1             -74.003664             40.743174             299   \n",
       "2             -74.003664             40.743174             299   \n",
       "3             -74.003664             40.743174             299   \n",
       "4             -74.003664             40.743174             299   \n",
       "\n",
       "   start station longitude  start station latitude  start station id  \\\n",
       "0               -73.989151               40.742354                 0   \n",
       "1               -73.987586               40.735243                 1   \n",
       "2               -74.016777               40.705693                 2   \n",
       "3               -73.990741               40.734546                 3   \n",
       "4               -73.983799               40.726218               118   \n",
       "\n",
       "      distance  \n",
       "0  1229.224446  \n",
       "1  1618.732623  \n",
       "2  4307.215223  \n",
       "3  1452.477060  \n",
       "4  2522.253218  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = ['start station longitude', 'start station latitude', 'start station id']\n",
    "all_starts = trips.drop_duplicates(subset='start station id', keep='first')[subset]\n",
    "\n",
    "subset = ['end station longitude', 'end station latitude', 'end station id']\n",
    "all_ends = trips.drop_duplicates(subset='end station id', keep='first')[subset]\n",
    "\n",
    "distance_matrix = all_ends.merge(all_starts, how='cross')\n",
    "distance_matrix['distance'] = distance_matrix.apply(lambda x: geodesic((x['start station latitude'], x['start station longitude']), \n",
    "                                                                       (x['end station latitude'], x['end station longitude'])).meters, axis=1)\n",
    "distance_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7dc21e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station id</th>\n",
       "      <th>distance</th>\n",
       "      <th>edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>0</td>\n",
       "      <td>1229.224446</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>1</td>\n",
       "      <td>1618.732623</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>2</td>\n",
       "      <td>4307.215223</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>3</td>\n",
       "      <td>1452.477060</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-74.003664</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>299</td>\n",
       "      <td>-73.983799</td>\n",
       "      <td>40.726218</td>\n",
       "      <td>118</td>\n",
       "      <td>2522.253218</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   end station longitude  end station latitude  end station id  \\\n",
       "0             -74.003664             40.743174             299   \n",
       "1             -74.003664             40.743174             299   \n",
       "2             -74.003664             40.743174             299   \n",
       "3             -74.003664             40.743174             299   \n",
       "4             -74.003664             40.743174             299   \n",
       "\n",
       "   start station longitude  start station latitude  start station id  \\\n",
       "0               -73.989151               40.742354                 0   \n",
       "1               -73.987586               40.735243                 1   \n",
       "2               -74.016777               40.705693                 2   \n",
       "3               -73.990741               40.734546                 3   \n",
       "4               -73.983799               40.726218               118   \n",
       "\n",
       "      distance   edge  \n",
       "0  1229.224446  False  \n",
       "1  1618.732623  False  \n",
       "2  4307.215223  False  \n",
       "3  1452.477060  False  \n",
       "4  2522.253218  False  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix['edge'] = distance_matrix['distance'] < 500 \n",
    "distance_matrix.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2695d703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4,   6,  15, ..., 315, 317, 327],\n",
       "       [299, 299, 299, ..., 272, 272, 272]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_index = distance_matrix[distance_matrix['edge'] == True][['start station id', 'end station id']].values\n",
    "edge_index = edge_index.transpose() \n",
    "edge_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8eade561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[294.92884499,   0.        ,   0.        ],\n",
       "       [327.27388739,   0.        ,   0.        ],\n",
       "       [419.30404213,   0.        ,   0.        ],\n",
       "       ...,\n",
       "       [379.44132957,   0.        ,   0.        ],\n",
       "       [349.85678078,   0.        ,   0.        ],\n",
       "       [332.80260533,   0.        ,   0.        ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_feature = distance_matrix[distance_matrix['edge'] == True]['distance'].values \n",
    "edge_type_feature = np.zeros_like(distance_feature)\n",
    "trip_duration_feature = np.zeros_like(distance_feature)\n",
    "static_edge_features = np.stack([distance_feature, edge_type_feature, trip_duration_feature]).transpose() \n",
    "static_edge_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d95fe1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_dynamic_edges(s):\n",
    "    trip_indices = s[['start station id', 'end station id']].values \n",
    "    trip_durations = s['tripduration']\n",
    "    \n",
    "    distance_feature = pd.DataFrame(trip_indices, columns=['start station id', 'end station id']).merge(distance_matrix, on=['start station id', 'end station id'], how='left')['distance'].values\n",
    "    edge_type_feature = np.ones_like(distance_feature)\n",
    "    trip_duration_feature = trip_durations \n",
    "    edge_features = np.stack([distance_feature, edge_type_feature, trip_duration_feature]).transpose() \n",
    "    return edge_features, trip_indices.transpose()\n",
    "\n",
    "\n",
    "start_date = datetime.strptime('2013-06-01 00:00:01', '%Y-%m-%d %H:%M:%S')\n",
    "end_date = datetime.strptime('2013-07-01 00:10:34', '%Y-%m-%d %H:%M:%S')\n",
    "interval = timedelta(minutes=60)\n",
    "\n",
    "xs = []\n",
    "edge_indices = []\n",
    "ys = [] \n",
    "y_indices = []\n",
    "edge_features = [] \n",
    "\n",
    "while start_date <= end_date:\n",
    "    current_snapshot = trips[((start_date + interval) >= trips['stoptime']) & (start_date <= trips['stoptime'])]\n",
    "    subsequent_snapshot = trips[((start_date + 2 * interval) >= trips['stoptime']) & (start_date + interval <= trips['stoptime'])]\n",
    "    \n",
    "    current_snapshot = current_snapshot.groupby(['start station id', 'end station id']).mean(numeric_only=True).reset_index() \n",
    "    subsequent_snapshot = subsequent_snapshot.groupby(['start station id', 'end station id']).mean(numeric_only=True).reset_index()\n",
    "    \n",
    "    edge_feats, additional_edge_index = extract_dynamic_edges(current_snapshot)\n",
    "    extended_edge_index = np.concatenate([edge_index, additional_edge_index], axis=1)\n",
    "    extended_edge_feats = np.concatenate([edge_feats, static_edge_features], axis=0)\n",
    "    \n",
    "    y = subsequent_snapshot['tripduration'].values \n",
    "    y_index = subsequent_snapshot[['start station id', 'end station id']].values \n",
    "    \n",
    "    xs.append(node_features)\n",
    "    edge_indices.append(extended_edge_index)\n",
    "    edge_features.append(extended_edge_feats)\n",
    "    ys.append(y)\n",
    "    y_indices.append(y_index.transpose())\n",
    "    \n",
    "    start_date += interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3da8a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example of graph snapshot 2: \n",
      "\n",
      "          Node feature shape: (337, 2) \n",
      "\n",
      "          Edge index shape: (2, 2773) \n",
      "\n",
      "          Edge feature shape: (2773, 3) \n",
      " \n",
      "          Labels shape: (25,) \n",
      "\n",
      "          Labels mask shape: (2, 25)\n",
      "      \n"
     ]
    }
   ],
   "source": [
    "i = 2\n",
    "print(f'''Example of graph snapshot {i}: \\n\n",
    "          Node feature shape: {xs[i].shape} \\n\n",
    "          Edge index shape: {edge_indices[i].shape} \\n\n",
    "          Edge feature shape: {edge_features[i].shape} \\n \n",
    "          Labels shape: {ys[i].shape} \\n\n",
    "          Labels mask shape: {y_indices[i].shape}\n",
    "      ''')"
   ]
  }
 ],
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