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goodreads_children/goodreads.md
CHANGED
@@ -10,4 +10,7 @@ The Goodreads datasets consist of four datasets, specifically labeled as Goodrea
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Link prediction in the Goodreads dataset involves predicting potential connections between users and books. The goal is to predict whether a user will review a book.
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### Node Classification
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Node classification tasks in the Goodreads dataset include predicting the book's category.
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Link prediction in the Goodreads dataset involves predicting potential connections between users and books. The goal is to predict whether a user will review a book.
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### Node Classification
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Node classification tasks in the Goodreads dataset include predicting the book's category.
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## Dataset Source
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https://mengtingwan.github.io/data/goodreads.html
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goodreads_children/raw/download_data.py
ADDED
@@ -0,0 +1,35 @@
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import requests
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import gzip
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import os
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# 定义文件下载链接和对应的保存路径
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files = {
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#'goodreads_reviews_children.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_reviews_children.json.gz',
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'goodreads_books_history_biography.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_history_biography.json.gz',
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'goodreads_books_comics_graphic.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_comics_graphic.json.gz',
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'goodreads_books_mystery_thriller_crime.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_mystery_thriller_crime.json.gz'
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}
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download_folder_name = ["../../goodreads_history/raw", "../../goodreads_comics/raw", "../../goodreads_crime/raw"]
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def download_and_extract(filename, url, folder):
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if not os.path.exists(folder):
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os.makedirs(folder)
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response = requests.get(url)
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gz_path = os.path.join(folder, filename)
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with open(gz_path, 'wb') as f:
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f.write(response.content)
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json_path = gz_path.replace('.gz', '')
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with gzip.open(gz_path, 'rb') as f_in:
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with open(json_path, 'wb') as f_out:
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f_out.write(f_in.read())
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print(f"{filename} 下载并解压到 {folder} 完成")
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for folder, url in zip(download_folder_name,files.values()):
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folder_name = folder
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download_and_extract(os.path.basename(url), url, folder_name)
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goodreads_children/raw/process_final_goodreads.ipynb
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@@ -0,0 +1,310 @@
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{
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"cells": [
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{
|
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"cell_type": "code",
|
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"execution_count": 9,
|
6 |
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"id": "44b09756",
|
7 |
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"metadata": {},
|
8 |
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"outputs": [],
|
9 |
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"source": [
|
10 |
+
"import tqdm"
|
11 |
+
]
|
12 |
+
},
|
13 |
+
{
|
14 |
+
"cell_type": "code",
|
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+
"execution_count": 1,
|
16 |
+
"id": "2b881572b62f8ce1",
|
17 |
+
"metadata": {
|
18 |
+
"ExecuteTime": {
|
19 |
+
"end_time": "2024-10-05T02:27:58.446078900Z",
|
20 |
+
"start_time": "2024-10-05T02:27:46.539697200Z"
|
21 |
+
},
|
22 |
+
"collapsed": false
|
23 |
+
},
|
24 |
+
"outputs": [],
|
25 |
+
"source": [
|
26 |
+
"import json\n",
|
27 |
+
"path = \"goodreads_reviews_children.json\"\n",
|
28 |
+
"dict_edge = {} #example: 8842281e1d1347389f2ab93d60773d4d|23310161 : One of my favorite books.\n",
|
29 |
+
"dict_num_to_id = {} # reorder the node's id # TODO\n",
|
30 |
+
"edge_score = []\n",
|
31 |
+
"count = 0\n",
|
32 |
+
"with open(path) as f:\n",
|
33 |
+
" for line in f:\n",
|
34 |
+
" d = json.loads(line)\n",
|
35 |
+
" edge = d[\"user_id\"] + \"|\" + d[\"book_id\"]\n",
|
36 |
+
" dict_edge[edge] = d[\"review_text\"]\n",
|
37 |
+
" edge_score.append(d[\"rating\"])\n",
|
38 |
+
" if d[\"user_id\"] not in dict_num_to_id:\n",
|
39 |
+
" dict_num_to_id[d[\"user_id\"]] = count\n",
|
40 |
+
" count += 1\n",
|
41 |
+
" if d[\"book_id\"] not in dict_num_to_id:\n",
|
42 |
+
" dict_num_to_id[d[\"book_id\"]] = count\n",
|
43 |
+
" count += 1"
|
44 |
+
]
|
45 |
+
},
|
46 |
+
{
|
47 |
+
"cell_type": "code",
|
48 |
+
"execution_count": 3,
|
49 |
+
"id": "c64f4d2d8949368f",
|
50 |
+
"metadata": {
|
51 |
+
"ExecuteTime": {
|
52 |
+
"end_time": "2024-10-05T02:28:48.893801500Z",
|
53 |
+
"start_time": "2024-10-05T02:28:27.239080300Z"
|
54 |
+
},
|
55 |
+
"collapsed": false
|
56 |
+
},
|
57 |
+
"outputs": [],
|
58 |
+
"source": [
|
59 |
+
"path = \"goodreads_book_genres_initial.json\"\n",
|
60 |
+
"bookid_to_label = {}\n",
|
61 |
+
"with open(path,'rb') as f:\n",
|
62 |
+
" for line in f:\n",
|
63 |
+
" d = json.loads(line)\n",
|
64 |
+
" label_list = []\n",
|
65 |
+
" for x in d[\"genres\"]:\n",
|
66 |
+
" label_list.append(x)\n",
|
67 |
+
" bookid_to_label[d[\"book_id\"]] = label_list"
|
68 |
+
]
|
69 |
+
},
|
70 |
+
{
|
71 |
+
"cell_type": "code",
|
72 |
+
"execution_count": 4,
|
73 |
+
"id": "8b0b220f7b0f42d2",
|
74 |
+
"metadata": {
|
75 |
+
"ExecuteTime": {
|
76 |
+
"end_time": "2024-10-05T02:28:48.915454200Z",
|
77 |
+
"start_time": "2024-10-05T02:28:48.898878300Z"
|
78 |
+
},
|
79 |
+
"collapsed": false
|
80 |
+
},
|
81 |
+
"outputs": [],
|
82 |
+
"source": [
|
83 |
+
"dict_month = {\n",
|
84 |
+
" \"1\": \"January\", \n",
|
85 |
+
" \"2\": \"February\", \n",
|
86 |
+
" \"3\": \"March\", \n",
|
87 |
+
" \"4\": \"April\", \n",
|
88 |
+
" \"5\": \"May\", \n",
|
89 |
+
" \"6\": \"June\", \n",
|
90 |
+
" \"7\": \"July\", \n",
|
91 |
+
" \"8\": \"August\", \n",
|
92 |
+
" \"9\": \"September\", \n",
|
93 |
+
" \"10\": \"October\", \n",
|
94 |
+
" \"11\": \"November\", \n",
|
95 |
+
" \"12\": \"December\"\n",
|
96 |
+
"}\n"
|
97 |
+
]
|
98 |
+
},
|
99 |
+
{
|
100 |
+
"cell_type": "code",
|
101 |
+
"execution_count": 14,
|
102 |
+
"id": "b32fd5e90ab106d",
|
103 |
+
"metadata": {
|
104 |
+
"ExecuteTime": {
|
105 |
+
"end_time": "2024-10-05T02:28:58.994242600Z",
|
106 |
+
"start_time": "2024-10-05T02:28:48.916449100Z"
|
107 |
+
},
|
108 |
+
"collapsed": false
|
109 |
+
},
|
110 |
+
"outputs": [
|
111 |
+
{
|
112 |
+
"name": "stderr",
|
113 |
+
"output_type": "stream",
|
114 |
+
"text": [
|
115 |
+
"124082it [00:06, 18574.44it/s]\n"
|
116 |
+
]
|
117 |
+
}
|
118 |
+
],
|
119 |
+
"source": [
|
120 |
+
"path = \"goodreads_books_children.json\"\n",
|
121 |
+
"bookid_to_text = {}\n",
|
122 |
+
"text = \"This book tittled [title] is a [format] edition published by [publisher] in [publication_month] [publication_year] about [description], consisting of [num_pages] pages.\"\n",
|
123 |
+
"with open(path,'rb') as f:\n",
|
124 |
+
" for line in tqdm.tqdm(f):\n",
|
125 |
+
" d = json.loads(line)\n",
|
126 |
+
" book_id = d[\"book_id\"]\n",
|
127 |
+
" book_text = text.replace(\"[title]\", d[\"title\"])\n",
|
128 |
+
" book_text = book_text.replace(\"[publisher]\", d[\"publisher\"])\n",
|
129 |
+
" book_text = book_text.replace(\"[format]\", d[\"format\"])\n",
|
130 |
+
" try:\n",
|
131 |
+
" book_text = book_text.replace(\"[publication_month]\", dict_month[d[\"publication_month\"]])\n",
|
132 |
+
" except:\n",
|
133 |
+
" book_text = book_text.replace(\"[publication_month]\", \"Unknown Month\")\n",
|
134 |
+
" book_text = book_text.replace(\"[publication_year]\", d[\"publication_year\"])\n",
|
135 |
+
" book_text = book_text.replace(\"[description]\", d[\"description\"])\n",
|
136 |
+
" book_text = book_text.replace(\"[num_pages]\", d[\"num_pages\"])\n",
|
137 |
+
" bookid_to_text[book_id] = book_text"
|
138 |
+
]
|
139 |
+
},
|
140 |
+
{
|
141 |
+
"cell_type": "code",
|
142 |
+
"execution_count": 19,
|
143 |
+
"id": "5e69e274cb42bf36",
|
144 |
+
"metadata": {
|
145 |
+
"ExecuteTime": {
|
146 |
+
"end_time": "2024-10-05T02:28:59.093097Z",
|
147 |
+
"start_time": "2024-10-05T02:28:59.065287600Z"
|
148 |
+
},
|
149 |
+
"collapsed": false
|
150 |
+
},
|
151 |
+
"outputs": [],
|
152 |
+
"source": [
|
153 |
+
"edge1 = [] \n",
|
154 |
+
"edge2 = [] # edge1 edge2 are to generate edge_index\n",
|
155 |
+
"text_nodes = [None] * len(dict_num_to_id)\n",
|
156 |
+
"text_edges = []\n",
|
157 |
+
"text_node_labels = [-1] * len(dict_num_to_id)"
|
158 |
+
]
|
159 |
+
},
|
160 |
+
{
|
161 |
+
"cell_type": "code",
|
162 |
+
"execution_count": 21,
|
163 |
+
"id": "f2adedbc870feda",
|
164 |
+
"metadata": {
|
165 |
+
"ExecuteTime": {
|
166 |
+
"end_time": "2024-10-05T02:29:16.329596100Z",
|
167 |
+
"start_time": "2024-10-05T02:29:13.594803Z"
|
168 |
+
},
|
169 |
+
"collapsed": false
|
170 |
+
},
|
171 |
+
"outputs": [
|
172 |
+
{
|
173 |
+
"name": "stderr",
|
174 |
+
"output_type": "stream",
|
175 |
+
"text": [
|
176 |
+
"100%|██████████| 734640/734640 [00:02<00:00, 363991.85it/s]\n"
|
177 |
+
]
|
178 |
+
}
|
179 |
+
],
|
180 |
+
"source": [
|
181 |
+
"for edge, edge_text in tqdm.tqdm(dict_edge.items()):\n",
|
182 |
+
" node1 = edge.split(\"|\")[0]\n",
|
183 |
+
" node2 = edge.split(\"|\")[1]\n",
|
184 |
+
" node1_id = int(dict_num_to_id[node1])\n",
|
185 |
+
" node2_id = int(dict_num_to_id[node2])\n",
|
186 |
+
" edge1.append(node1_id)\n",
|
187 |
+
" edge2.append(node2_id)\n",
|
188 |
+
" text_nodes[node1_id] = \"user\"\n",
|
189 |
+
" text_nodes[node2_id] = bookid_to_text[node2]\n",
|
190 |
+
" text_edges.append(edge_text)\n",
|
191 |
+
" text_node_labels[node2_id] = bookid_to_label[node2]"
|
192 |
+
]
|
193 |
+
},
|
194 |
+
{
|
195 |
+
"cell_type": "code",
|
196 |
+
"execution_count": 22,
|
197 |
+
"id": "3305934f1a11caa7",
|
198 |
+
"metadata": {
|
199 |
+
"ExecuteTime": {
|
200 |
+
"end_time": "2024-10-05T02:30:27.810685400Z",
|
201 |
+
"start_time": "2024-10-05T02:30:07.522283400Z"
|
202 |
+
},
|
203 |
+
"collapsed": false
|
204 |
+
},
|
205 |
+
"outputs": [],
|
206 |
+
"source": [
|
207 |
+
"from torch_geometric.data import Data\n",
|
208 |
+
"import torch"
|
209 |
+
]
|
210 |
+
},
|
211 |
+
{
|
212 |
+
"cell_type": "code",
|
213 |
+
"execution_count": 23,
|
214 |
+
"id": "5030fa8672f2b177",
|
215 |
+
"metadata": {
|
216 |
+
"ExecuteTime": {
|
217 |
+
"end_time": "2024-10-05T02:30:28.108006200Z",
|
218 |
+
"start_time": "2024-10-05T02:30:28.066559800Z"
|
219 |
+
},
|
220 |
+
"collapsed": false
|
221 |
+
},
|
222 |
+
"outputs": [],
|
223 |
+
"source": [
|
224 |
+
"edge_index = torch.tensor([edge1,edge2])"
|
225 |
+
]
|
226 |
+
},
|
227 |
+
{
|
228 |
+
"cell_type": "code",
|
229 |
+
"execution_count": 25,
|
230 |
+
"id": "21085a8a04df7062",
|
231 |
+
"metadata": {
|
232 |
+
"ExecuteTime": {
|
233 |
+
"end_time": "2024-10-05T02:30:28.123004600Z",
|
234 |
+
"start_time": "2024-10-05T02:30:28.082105900Z"
|
235 |
+
},
|
236 |
+
"collapsed": false
|
237 |
+
},
|
238 |
+
"outputs": [],
|
239 |
+
"source": [
|
240 |
+
"new_data = Data(\n",
|
241 |
+
" edge_index=edge_index,\n",
|
242 |
+
" text_nodes=text_nodes,\n",
|
243 |
+
" text_edges=text_edges,\n",
|
244 |
+
" text_node_labels=text_node_labels,\n",
|
245 |
+
" edge_score=edge_score\n",
|
246 |
+
")"
|
247 |
+
]
|
248 |
+
},
|
249 |
+
{
|
250 |
+
"cell_type": "code",
|
251 |
+
"execution_count": 29,
|
252 |
+
"id": "0355133f",
|
253 |
+
"metadata": {},
|
254 |
+
"outputs": [],
|
255 |
+
"source": [
|
256 |
+
"new_data.edge_score = torch.tensor(edge_score, dtype=torch.long)"
|
257 |
+
]
|
258 |
+
},
|
259 |
+
{
|
260 |
+
"cell_type": "code",
|
261 |
+
"execution_count": 30,
|
262 |
+
"id": "d39601d90a0171c5",
|
263 |
+
"metadata": {
|
264 |
+
"ExecuteTime": {
|
265 |
+
"end_time": "2024-10-05T02:31:58.346932900Z",
|
266 |
+
"start_time": "2024-10-05T02:31:57.351248600Z"
|
267 |
+
},
|
268 |
+
"collapsed": false
|
269 |
+
},
|
270 |
+
"outputs": [
|
271 |
+
{
|
272 |
+
"name": "stdout",
|
273 |
+
"output_type": "stream",
|
274 |
+
"text": [
|
275 |
+
"Data saved to ../processed/children.pkl\n"
|
276 |
+
]
|
277 |
+
}
|
278 |
+
],
|
279 |
+
"source": [
|
280 |
+
"import pickle\n",
|
281 |
+
"output_file_path = '../processed/children.pkl'\n",
|
282 |
+
"with open(output_file_path, 'wb') as output_file:\n",
|
283 |
+
" pickle.dump(new_data, output_file)\n",
|
284 |
+
"\n",
|
285 |
+
"print(f\"Data saved to {output_file_path}\")"
|
286 |
+
]
|
287 |
+
}
|
288 |
+
],
|
289 |
+
"metadata": {
|
290 |
+
"kernelspec": {
|
291 |
+
"display_name": "Python 3",
|
292 |
+
"language": "python",
|
293 |
+
"name": "python3"
|
294 |
+
},
|
295 |
+
"language_info": {
|
296 |
+
"codemirror_mode": {
|
297 |
+
"name": "ipython",
|
298 |
+
"version": 3
|
299 |
+
},
|
300 |
+
"file_extension": ".py",
|
301 |
+
"mimetype": "text/x-python",
|
302 |
+
"name": "python",
|
303 |
+
"nbconvert_exporter": "python",
|
304 |
+
"pygments_lexer": "ipython3",
|
305 |
+
"version": "3.10.12"
|
306 |
+
}
|
307 |
+
},
|
308 |
+
"nbformat": 4,
|
309 |
+
"nbformat_minor": 5
|
310 |
+
}
|