{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch_geometric.data import Data\n",
    "import tqdm\n",
    "import pickle\n",
    "from torch_sparse import SparseTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = np.load(\"68841_tweets_multiclasses_filtered_0722_part2.npy\", allow_pickle=True)\n",
    "p2 = np.load(\"68841_tweets_multiclasses_filtered_0722_part2.npy\", allow_pickle=True)\n",
    "g = np.concatenate((p1, p2), axis=0)\n",
    "df = pd.DataFrame(data=g, columns=[\"event_id\", \"tweet_id\", \"text\", \"user_id\", \"created_at\", \"user_loc\",\n",
    "\t\t\t\"place_type\", \"place_full_name\", \"place_country_code\", \"hashtags\", \"user_mentions\", \"image_urls\", \"entities\",\n",
    "\t\t\t\"words\", \"filtered_words\", \"sampled_words\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "67682it [00:04, 15665.18it/s]\n"
     ]
    }
   ],
   "source": [
    "tweet_id2idx = {}\n",
    "user_id2idx = {}\n",
    "user = []\n",
    "tweet = []\n",
    "text_edges = []\n",
    "text_nodes = [-1] * len(df) * 20\n",
    "count = 0\n",
    "\n",
    "# Use df instead of g for iteration\n",
    "for _, row in tqdm.tqdm(df.iterrows()):\n",
    "    # Convert tweet_id and user_id to string to ensure consistency\n",
    "    tweet_id = str(row['tweet_id'])\n",
    "    user_id = str(row['user_id'])\n",
    "    \n",
    "    if tweet_id not in tweet_id2idx:\n",
    "        tweet_id2idx[tweet_id] = count\n",
    "        tweet.append(count)\n",
    "        count += 1\n",
    "    else:\n",
    "        tweet.append(tweet_id2idx[tweet_id])\n",
    "    text_nodes[tweet_id2idx[tweet_id]] = f\"tweet{tweet_id2idx[tweet_id]} of event{row['event_id']}\"\n",
    "    \n",
    "    if user_id not in user_id2idx:\n",
    "        user_id2idx[user_id] = count\n",
    "        user.append(count)\n",
    "        count += 1\n",
    "    else:\n",
    "        user.append(user_id2idx[user_id])\n",
    "    text_nodes[user_id2idx[user_id]] = f\"user\"\n",
    "    \n",
    "    text_edges.append(row['text'])\n",
    "    \n",
    "    for mention in row['user_mentions']:\n",
    "        if mention not in user_id2idx:\n",
    "            user_id2idx[mention] = count\n",
    "            user.append(count)\n",
    "            count += 1\n",
    "        else:\n",
    "            user.append(user_id2idx[mention])\n",
    "        tweet.append(tweet_id2idx[tweet_id])\n",
    "        text_nodes[user_id2idx[mention]] = f\"mentioned user\"\n",
    "        text_edges.append(row['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_nodes = text_nodes[:count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_index = [user, tweet]\n",
    "graph = Data(\n",
    "\t\t\ttext_nodes=text_nodes,\n",
    "\t\t\ttext_edges=text_edges,\n",
    "\t\t\tedge_index=torch.tensor(edge_index, dtype=torch.long)\n",
    "\t\t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../processed/twitter.pkl', 'wb') as f:\n",
    "    pickle.dump(graph, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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