File size: 4,319 Bytes
f765459 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | 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}")
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