{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import pickle as pkl\n",
    "\n",
    "from torch_geometric.data import Data\n",
    "import torch\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5327/3476166533.py:2: DtypeWarning: Columns (2,3,4,6,7,8,9,14,17,18,21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"reddit.csv\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1070077, 22)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5327/3476166533.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_graph.rename(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256388, 8)\n"
     ]
    }
   ],
   "source": [
    "## Preprocessing\n",
    "df = pd.read_csv(\"reddit.csv\")\n",
    "print(df.shape)\n",
    "\n",
    "# select columns\n",
    "df_graph = df[\n",
    "    [\n",
    "        \"subreddit_id\",\n",
    "        \"subreddit\",\n",
    "        \"name\",\n",
    "        \"body\",\n",
    "        \"score\",\n",
    "        \"author\",\n",
    "        \"author_flair_text\",\n",
    "        \"distinguished\",\n",
    "    ]\n",
    "]\n",
    "df_graph.rename(\n",
    "    columns={\n",
    "        \"name\": \"post_id\",\n",
    "        \"body\": \"post\",\n",
    "        \"author\": \"user\",\n",
    "        \"author_flair_text\": \"user_flair\",\n",
    "    },\n",
    "    inplace=True,\n",
    "    errors=\"raise\",\n",
    ")\n",
    "\n",
    "# drop na, duplicates and deleted post\n",
    "df_graph = df_graph.drop_duplicates()\n",
    "df_graph = df_graph[df_graph[\"post\"] != \"[deleted]\"]\n",
    "df_graph = df_graph.dropna(subset=[\"post_id\"])\n",
    "df_graph = df_graph.dropna(subset=[\"user_flair\"])\n",
    "df_graph = df_graph.dropna(subset=[\"subreddit\"])\n",
    "df_graph = df_graph.dropna(subset=[\"post\"])\n",
    "print(df_graph.shape)\n",
    "\n",
    "df_graph[\"distinguished\"] = df_graph[\"distinguished\"].apply(\n",
    "    lambda x: \"ordinary\" if pd.isna(x) else \"distinguished\"\n",
    ")\n",
    "df_graph[\"user_flair\"] = df_graph[\"user_flair\"].apply(lambda x: \"\" if pd.isna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>subreddit_id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>post_id</th>\n",
       "      <th>post</th>\n",
       "      <th>score</th>\n",
       "      <th>user</th>\n",
       "      <th>user_flair</th>\n",
       "      <th>distinguished</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>t5_2qhon</td>\n",
       "      <td>comicbooks</td>\n",
       "      <td>t1_cqug9dk</td>\n",
       "      <td>It's not contradictory. Snyder's rendition of ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>eskimo_bros</td>\n",
       "      <td>Luke Cage</td>\n",
       "      <td>ordinary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit_id   subreddit     post_id  \\\n",
       "3     t5_2qhon  comicbooks  t1_cqug9dk   \n",
       "\n",
       "                                                post  score         user  \\\n",
       "3  It's not contradictory. Snyder's rendition of ...    1.0  eskimo_bros   \n",
       "\n",
       "  user_flair distinguished  \n",
       "3  Luke Cage      ordinary  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_graph\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "256388it [00:15, 17023.98it/s]\n"
     ]
    }
   ],
   "source": [
    "text_nodes = []\n",
    "node_labels = []\n",
    "sub_id2idx = {}\n",
    "sub_nodes = []\n",
    "user_id2idx = {} \n",
    "user_nodes = []\n",
    "count = 0\n",
    "text_edges = []\n",
    "for _, row in tqdm.tqdm(df.iterrows()):\n",
    "    sub_id = str(row[\"subreddit\"])\n",
    "    user_id = str(row['user'])\n",
    "\n",
    "    if sub_id not in sub_id2idx:\n",
    "        sub_id2idx[sub_id] = count\n",
    "        sub_nodes.append(count)\n",
    "        count += 1\n",
    "    else:\n",
    "        sub_nodes.append(sub_id2idx[sub_id])\n",
    "    text_nodes.append(f\"subreddit {sub_id}\")\n",
    "    node_labels.append(-1)\n",
    "\n",
    "    if user_id not in user_id2idx:\n",
    "        user_id2idx[user_id] = count\n",
    "        user_nodes.append(count)\n",
    "        count += 1\n",
    "    else:\n",
    "        user_nodes.append(user_id2idx[user_id])\n",
    "    text_nodes.append(f\"user {user_id} has flair {row['user_flair']}\")\n",
    "    node_labels.append(row['distinguished'])\n",
    "    text_edges.append(str(row['post']))\n",
    "    \n",
    "\n",
    "## Save it as torch data\n",
    "graph = Data(\n",
    "    text_nodes=text_nodes,\n",
    "    text_edges=text_edges,\n",
    "    node_labels=node_labels,\n",
    "    edge_index=torch.tensor([user_nodes, sub_nodes], dtype=torch.long),\n",
    ")\n",
    "\n",
    "with open(\"../processed/reddit.pkl\", \"wb\") as file:\n",
    "    pkl.dump(graph, file)"
   ]
  }
 ],
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