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{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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