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goodreads_children/goodreads.md

@@ -10,4 +10,7 @@ The Goodreads datasets consist of four datasets, specifically labeled as Goodrea
 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.
 
 ### Node Classification
-Node classification tasks in the Goodreads dataset include predicting the book's category. 
+Node classification tasks in the Goodreads dataset include predicting the book's category. 
+
+## Dataset Source
+https://mengtingwan.github.io/data/goodreads.html

goodreads_children/raw/download_data.py

@@ -0,0 +1,35 @@
+import requests
+import gzip
+import os
+
+# 定义文件下载链接和对应的保存路径
+files = {
+    #'goodreads_reviews_children.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_reviews_children.json.gz',
+     'goodreads_books_history_biography.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_history_biography.json.gz',
+     'goodreads_books_comics_graphic.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_comics_graphic.json.gz',
+     'goodreads_books_mystery_thriller_crime.json.gz': 'https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_mystery_thriller_crime.json.gz'
+}
+
+download_folder_name = ["../../goodreads_history/raw", "../../goodreads_comics/raw", "../../goodreads_crime/raw"]
+
+def download_and_extract(filename, url, folder):
+
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+    
+    response = requests.get(url)
+    gz_path = os.path.join(folder, filename)
+    
+    with open(gz_path, 'wb') as f:
+        f.write(response.content)
+    
+    json_path = gz_path.replace('.gz', '')
+    with gzip.open(gz_path, 'rb') as f_in:
+        with open(json_path, 'wb') as f_out:
+            f_out.write(f_in.read())
+    
+    print(f"{filename} 下载并解压到 {folder} 完成")
+
+for folder, url in zip(download_folder_name,files.values()):
+    folder_name =  folder
+    download_and_extract(os.path.basename(url), url, folder_name)

goodreads_children/raw/process_final_goodreads.ipynb

@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "44b09756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2b881572b62f8ce1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:27:58.446078900Z",
+     "start_time": "2024-10-05T02:27:46.539697200Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "path = \"goodreads_reviews_children.json\"\n",
+    "dict_edge = {} #example: 8842281e1d1347389f2ab93d60773d4d|23310161 : One of my favorite books.\n",
+    "dict_num_to_id = {} # reorder the node's id  # TODO\n",
+    "edge_score = []\n",
+    "count = 0\n",
+    "with open(path) as f:\n",
+    "    for line in f:\n",
+    "        d = json.loads(line)\n",
+    "        edge = d[\"user_id\"] + \"|\" + d[\"book_id\"]\n",
+    "        dict_edge[edge] = d[\"review_text\"]\n",
+    "        edge_score.append(d[\"rating\"])\n",
+    "        if d[\"user_id\"] not in dict_num_to_id:\n",
+    "            dict_num_to_id[d[\"user_id\"]] = count\n",
+    "            count += 1\n",
+    "        if d[\"book_id\"] not in dict_num_to_id:\n",
+    "            dict_num_to_id[d[\"book_id\"]] = count\n",
+    "            count += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c64f4d2d8949368f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:28:48.893801500Z",
+     "start_time": "2024-10-05T02:28:27.239080300Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "path = \"goodreads_book_genres_initial.json\"\n",
+    "bookid_to_label = {}\n",
+    "with open(path,'rb') as f:\n",
+    "    for line in f:\n",
+    "        d = json.loads(line)\n",
+    "        label_list = []\n",
+    "        for x in d[\"genres\"]:\n",
+    "            label_list.append(x)\n",
+    "        bookid_to_label[d[\"book_id\"]] = label_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8b0b220f7b0f42d2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:28:48.915454200Z",
+     "start_time": "2024-10-05T02:28:48.898878300Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dict_month = {\n",
+    "    \"1\": \"January\", \n",
+    "    \"2\": \"February\", \n",
+    "    \"3\": \"March\", \n",
+    "    \"4\": \"April\", \n",
+    "    \"5\": \"May\", \n",
+    "    \"6\": \"June\", \n",
+    "    \"7\": \"July\", \n",
+    "    \"8\": \"August\", \n",
+    "    \"9\": \"September\", \n",
+    "    \"10\": \"October\", \n",
+    "    \"11\": \"November\", \n",
+    "    \"12\": \"December\"\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b32fd5e90ab106d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:28:58.994242600Z",
+     "start_time": "2024-10-05T02:28:48.916449100Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "124082it [00:06, 18574.44it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = \"goodreads_books_children.json\"\n",
+    "bookid_to_text = {}\n",
+    "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",
+    "with open(path,'rb') as f:\n",
+    "    for line in tqdm.tqdm(f):\n",
+    "        d = json.loads(line)\n",
+    "        book_id = d[\"book_id\"]\n",
+    "        book_text = text.replace(\"[title]\", d[\"title\"])\n",
+    "        book_text = book_text.replace(\"[publisher]\", d[\"publisher\"])\n",
+    "        book_text = book_text.replace(\"[format]\", d[\"format\"])\n",
+    "        try:\n",
+    "            book_text = book_text.replace(\"[publication_month]\", dict_month[d[\"publication_month\"]])\n",
+    "        except:\n",
+    "            book_text = book_text.replace(\"[publication_month]\", \"Unknown Month\")\n",
+    "        book_text = book_text.replace(\"[publication_year]\", d[\"publication_year\"])\n",
+    "        book_text = book_text.replace(\"[description]\", d[\"description\"])\n",
+    "        book_text = book_text.replace(\"[num_pages]\", d[\"num_pages\"])\n",
+    "        bookid_to_text[book_id] = book_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5e69e274cb42bf36",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:28:59.093097Z",
+     "start_time": "2024-10-05T02:28:59.065287600Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "edge1 = [] \n",
+    "edge2 = []  # edge1 edge2 are to generate edge_index\n",
+    "text_nodes = [None] * len(dict_num_to_id)\n",
+    "text_edges = []\n",
+    "text_node_labels = [-1] * len(dict_num_to_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f2adedbc870feda",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:29:16.329596100Z",
+     "start_time": "2024-10-05T02:29:13.594803Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 734640/734640 [00:02<00:00, 363991.85it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for edge, edge_text in tqdm.tqdm(dict_edge.items()):\n",
+    "    node1 = edge.split(\"|\")[0]\n",
+    "    node2 = edge.split(\"|\")[1]\n",
+    "    node1_id = int(dict_num_to_id[node1])\n",
+    "    node2_id = int(dict_num_to_id[node2])\n",
+    "    edge1.append(node1_id)\n",
+    "    edge2.append(node2_id)\n",
+    "    text_nodes[node1_id] = \"user\"\n",
+    "    text_nodes[node2_id] = bookid_to_text[node2]\n",
+    "    text_edges.append(edge_text)\n",
+    "    text_node_labels[node2_id] = bookid_to_label[node2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3305934f1a11caa7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:30:27.810685400Z",
+     "start_time": "2024-10-05T02:30:07.522283400Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from torch_geometric.data import Data\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5030fa8672f2b177",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:30:28.108006200Z",
+     "start_time": "2024-10-05T02:30:28.066559800Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "edge_index = torch.tensor([edge1,edge2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "21085a8a04df7062",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:30:28.123004600Z",
+     "start_time": "2024-10-05T02:30:28.082105900Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "new_data = Data(\n",
+    "    edge_index=edge_index,\n",
+    "    text_nodes=text_nodes,\n",
+    "    text_edges=text_edges,\n",
+    "    text_node_labels=text_node_labels,\n",
+    "    edge_score=edge_score\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0355133f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_data.edge_score = torch.tensor(edge_score, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d39601d90a0171c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-05T02:31:58.346932900Z",
+     "start_time": "2024-10-05T02:31:57.351248600Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data saved to ../processed/children.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "output_file_path = '../processed/children.pkl'\n",
+    "with open(output_file_path, 'wb') as output_file:\n",
+    "    pickle.dump(new_data, output_file)\n",
+    "\n",
+    "print(f\"Data saved to {output_file_path}\")"
+   ]
+  }
+ ],
+ "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": 5
+}