Delete Reddit

Reddit/processed/reddit_graph.pkl

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-version https://git-lfs.github.com/spec/v1
-oid sha256:8aed3368b44889670caa82468ea6c78944c6eede3a03891c6b70b56d137db70f
-size 134

Reddit/raw/68841_tweets_multiclasses_filtered_0722_part1.npy

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-version https://git-lfs.github.com/spec/v1
-oid sha256:fdc595c36f74073feeb9dea9af01a467dd64743ceec15442085d8c3f2f187339
-size 20623408

Reddit/raw/Reddit-processing.py

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-import os
-import pandas as pd
-import math
-import pickle as pkl
-import torch
-from torch_geometric.data import Data
-
-# Get the directory of the current script
-script_dir = os.path.dirname(os.path.abspath(__file__))
-base_dir = os.path.dirname(script_dir)
-raw_dir = os.path.join(base_dir, 'processed/original')
-
-# Define the file path
-reddit_path = os.path.join(raw_dir, 'reddit_1m.csv')
-
-# Read the Reddit data
-df = pd.read_csv(reddit_path)
-print(df.shape)
-
-# Select required columns
-df_graph = df[['subreddit_id', 'subreddit', 'name', 'body', 'score', 'author', 'author_flair_text', 'distinguished']]
-df_graph.rename(columns={'name': 'post_id',
-                         'body': 'post',
-                         'author': 'user',
-                         'author_flair_text': 'user_flair'},
-                inplace=True, errors='raise')
-
-# Drop duplicates, deleted posts, and rows with NaN post_id
-df_graph = df_graph.drop_duplicates()
-df_graph = df_graph[df_graph['post'] != '[deleted]']
-df_graph = df_graph.dropna(subset=['post_id'])
-print(df_graph.shape)
-print(df_graph['post_id'].nunique())
-
-# Encode distinguished and user_flair
-df_graph['distinguished'] = df_graph['distinguished'].apply(lambda x: 0 if pd.isna(x) else 1)
-df_graph['user_flair'] = df_graph['user_flair'].apply(lambda x: "" if pd.isna(x) else x)
-
-text_nodes = []
-
-# Create sub_id2idx
-sub_id2idx = {}
-sub_nodes = []
-for _, row in df_graph.iterrows():
-    sub_id = row['subreddit_id']
-    if sub_id not in sub_nodes:
-        sub_id2idx[sub_id] = len(sub_nodes)
-        sub_nodes.append(sub_id)
-        text_nodes.append(row['subreddit'])
-node_labels = [-1] * len(sub_nodes)  # No labels
-
-print("Length of sub nodes:", len(sub_nodes))
-print("Sample sub node labels:", node_labels[:5])
-print("Sample sub node texts:", text_nodes[:5])
-
-# Create user_n2idx
-user_n2idx = {}  # Username to index mapping
-user_nodes = []
-for _, row in df_graph.iterrows():
-    user_n = row['user']
-    if user_n in user_nodes:  # Existing user: add new flair and update label
-        if row['user_flair'] not in text_nodes[user_n2idx[user_n]]:
-            text_nodes[user_n2idx[user_n]] += "\n" + row['user_flair']
-        node_labels[user_n2idx[user_n]] = max(row['distinguished'], node_labels[user_n2idx[user_n]])
-    else:  # New user: add the user to user_n2idx
-        user_n2idx[user_n] = len(user_nodes) + len(sub_nodes)
-        user_nodes.append(user_n)
-        text_nodes.append(row['user_flair'])
-        node_labels.append(row['distinguished'])
-
-print("Length of user nodes:", len(user_nodes))
-print("Sample user node labels:", node_labels[-10:])
-print("Sample user node texts:", text_nodes[-10:])
-
-# Record edge information
-edge_index = [[], []]
-text_edges = []
-edge_scr_labels = []  # Continuous score
-edge_spe_labels = []  # Binary special label
-all_edges = set()
-
-for _, row in df_graph.iterrows():
-    user_idx = user_n2idx[row['user']]
-    sub_idx = sub_id2idx[row['subreddit_id']]
-
-    if (user_idx, sub_idx) not in all_edges:  # Only keep one edge between two nodes
-        edge_index[0].append(user_idx)
-        edge_index[1].append(sub_idx)
-
-        text_edges.append(row['post'])
-        edge_scr_labels.append(row['score'])
-        edge_spe_labels.append(row['distinguished'])
-
-        all_edges.add((user_idx, sub_idx))
-
-print("Length of edges:", len(edge_index[0]))
-print("Sample edge score labels:", edge_scr_labels[-10:])
-print("Sample edge special labels:", edge_spe_labels[-10:])
-print("Sample edge texts:", text_edges[-10:])
-
-edge_scr_labels = [0 if math.isnan(x) else x for x in edge_scr_labels]
-edge_spe_labels = [0 if math.isnan(x) else x for x in edge_spe_labels]
-
-# Save as torch data
-graph = Data(
-    text_nodes=text_nodes,
-    text_edges=text_edges,
-    node_labels=torch.tensor(node_labels, dtype=torch.long),
-    edge_index=torch.tensor(edge_index, dtype=torch.long),
-    edge_score_labels=torch.tensor(edge_scr_labels, dtype=torch.long),
-    edge_special_labels=torch.tensor(edge_spe_labels, dtype=torch.long),
-)
-
-output_file = os.path.join(base_dir, 'output/reddit_graph.pkl')
-with open(output_file, 'wb') as file:
-    pkl.dump(graph, file)
-
-print(f"Data processing complete. Processed data saved to: {output_file}")

Reddit/raw/download_data.sh

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-#!/bin/bash
-
-# Get the directory of the current script
-SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
-BASE_DIR="$(dirname "$SCRIPT_DIR")"
-RAW_DIR="$BASE_DIR/raw"
-
-# Create the raw directory
-mkdir -p "$RAW_DIR"
-
-# Define URLs of the files to be downloaded
-urls=(
-    "https://github.com/YuweiCao-UIC/KPGNN/raw/main/datasets/Twitter/68841_tweets_multiclasses_filtered_0722_part1.npy"
-)
-
-# Download each file to the raw directory
-for url in "${urls[@]}"; do
-    wget -P "$RAW_DIR" "$url"
-done
-
-echo "Download complete."
-
-
-