203 lines
9.0 KiB
Python
203 lines
9.0 KiB
Python
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import argparse
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import json
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import math
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import random
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import dgl
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import dgl.function as fn
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import django
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import numpy as np
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import torch
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from dgl.ops import edge_softmax
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from sklearn.metrics import ndcg_score
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from tqdm import tqdm
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from gnnrec.config import DATA_DIR
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from gnnrec.hge.utils import set_random_seed, add_reverse_edges
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from gnnrec.kgrec.data import OAGCSDataset
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from gnnrec.kgrec.utils import iter_json, precision_at_k, recall_at_k
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def build_ground_truth_valid(args):
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"""从AI 2000抓取的学者排名数据匹配学者id，作为学者排名ground truth验证集。"""
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field_map = {
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'AAAI/IJCAI': 'artificial intelligence',
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'Machine Learning': 'machine learning',
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'Computer Vision': 'computer vision',
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'Natural Language Processing': 'natural language processing',
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'Robotics': 'robotics',
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'Knowledge Engineering': 'knowledge engineering',
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'Speech Recognition': 'speech recognition',
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'Data Mining': 'data mining',
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'Information Retrieval and Recommendation': 'information retrieval',
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'Database': 'database',
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'Human-Computer Interaction': 'human computer interaction',
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'Computer Graphics': 'computer graphics',
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'Multimedia': 'multimedia',
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'Visualization': 'visualization',
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'Security and Privacy': 'security privacy',
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'Computer Networking': 'computer network',
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'Computer Systems': 'operating system',
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'Theory': 'theory',
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'Chip Technology': 'chip',
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'Internet of Things': 'internet of things',
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}
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with open(DATA_DIR / 'rank/ai2000.json', encoding='utf8') as f:
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ai2000_author_rank = json.load(f)
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django.setup()
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from rank.models import Author
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author_rank = {}
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for field, scholars in ai2000_author_rank.items():
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aid = []
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for s in scholars:
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qs = Author.objects.filter(name=s['name'], institution__name=s['org']).order_by('-n_citation')
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if qs.exists():
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aid.append(qs[0].id)
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else:
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qs = Author.objects.filter(name=s['name']).order_by('-n_citation')
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aid.append(qs[0].id if qs.exists() else -1)
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author_rank[field_map[field]] = aid
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if not args.use_field_name:
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field2id = {f['name']: i for i, f in enumerate(iter_json(DATA_DIR / 'oag/cs/mag_fields.txt'))}
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author_rank = {field2id[f]: aid for f, aid in author_rank.items()}
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with open(DATA_DIR / 'rank/author_rank_val.json', 'w') as f:
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json.dump(author_rank, f)
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print('结果已保存到', f.name)
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def build_ground_truth_train(args):
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"""根据某个领域的论文引用数加权求和构造学者排名，作为ground truth训练集。"""
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data = OAGCSDataset()
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g = data[0]
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g.nodes['paper'].data['citation'] = g.nodes['paper'].data['citation'].float().log1p()
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g.edges['writes'].data['order'] = g.edges['writes'].data['order'].float()
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apg = g['author', 'writes', 'paper']
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# 1.筛选论文数>=num_papers的领域
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field_in_degree, fid = g.in_degrees(g.nodes('field'), etype='has_field').sort(descending=True)
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fid = fid[field_in_degree >= args.num_papers].tolist()
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# 2.对每个领域召回论文，构造学者-论文子图，通过论文引用数之和对学者排名
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author_rank = {}
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for i in tqdm(fid):
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pid, _ = g.in_edges(i, etype='has_field')
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sg = add_reverse_edges(dgl.in_subgraph(apg, {'paper': pid}, relabel_nodes=True))
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# 第k作者的权重为1/k，最后一个视为通讯作者，权重为1/2
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sg.edges['writes'].data['w'] = 1.0 / sg.edges['writes'].data['order']
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sg.update_all(fn.copy_e('w', 'w'), fn.min('w', 'mw'), etype='writes')
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sg.apply_edges(fn.copy_u('mw', 'mw'), etype='writes_rev')
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w, mw = sg.edges['writes'].data.pop('w'), sg.edges['writes_rev'].data.pop('mw')
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w[w == mw] = 0.5
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# 每篇论文所有作者的权重归一化，每个学者所有论文的引用数加权求和
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p = edge_softmax(sg['author', 'writes', 'paper'], torch.log(w).unsqueeze(dim=1))
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sg.edges['writes_rev'].data['p'] = p.squeeze(dim=1)
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sg.update_all(fn.u_mul_e('citation', 'p', 'c'), fn.sum('c', 'c'), etype='writes_rev')
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author_citation = sg.nodes['author'].data['c']
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_, aid = author_citation.topk(args.num_authors)
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aid = sg.nodes['author'].data[dgl.NID][aid]
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author_rank[i] = aid.tolist()
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if args.use_field_name:
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fields = [f['name'] for f in iter_json(DATA_DIR / 'oag/cs/mag_fields.txt')]
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author_rank = {fields[i]: aid for i, aid in author_rank.items()}
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with open(DATA_DIR / 'rank/author_rank_train.json', 'w') as f:
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json.dump(author_rank, f)
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print('结果已保存到', f.name)
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def evaluate_ground_truth(args):
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"""评估ground truth训练集的质量。"""
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with open(DATA_DIR / 'rank/author_rank_val.json') as f:
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author_rank_val = json.load(f)
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with open(DATA_DIR / 'rank/author_rank_train.json') as f:
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author_rank_train = json.load(f)
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fields = list(set(author_rank_val) & set(author_rank_train))
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author_rank_val = {k: v for k, v in author_rank_val.items() if k in fields}
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author_rank_train = {k: v for k, v in author_rank_train.items() if k in fields}
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num_authors = OAGCSDataset()[0].num_nodes('author')
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true_relevance = np.zeros((len(fields), num_authors), dtype=np.int32)
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scores = np.zeros_like(true_relevance)
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for i, f in enumerate(fields):
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for r, a in enumerate(author_rank_val[f]):
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if a != -1:
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true_relevance[i, a] = math.ceil((100 - r) / 10)
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for r, a in enumerate(author_rank_train[f]):
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scores[i, a] = len(author_rank_train[f]) - r
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for k in (100, 50, 20, 10, 5):
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print('nDGC@{0}={1:.4f}\tPrecision@{0}={2:.4f}\tRecall@{0}={3:.4f}'.format(
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k, ndcg_score(true_relevance, scores, k=k, ignore_ties=True),
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sum(precision_at_k(author_rank_val[f], author_rank_train[f], k) for f in fields) / len(fields),
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sum(recall_at_k(author_rank_val[f], author_rank_train[f], k) for f in fields) / len(fields)
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))
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def sample_triplets(args):
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set_random_seed(args.seed)
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with open(DATA_DIR / 'rank/author_rank_train.json') as f:
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author_rank = json.load(f)
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# 三元组：(t, ap, an)，表示对于领域t，学者ap的排名在an之前
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triplets = []
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for fid, aid in author_rank.items():
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fid = int(fid)
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n = len(aid)
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easy_margin, hard_margin = int(n * args.easy_margin), int(n * args.hard_margin)
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num_triplets = min(args.max_num, 2 * n - easy_margin - hard_margin)
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num_hard = int(num_triplets * args.hard_ratio)
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num_easy = num_triplets - num_hard
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triplets.extend(
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(fid, aid[i], aid[i + easy_margin])
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for i in random.sample(range(n - easy_margin), num_easy)
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)
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triplets.extend(
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(fid, aid[i], aid[i + hard_margin])
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for i in random.sample(range(n - hard_margin), num_hard)
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)
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with open(DATA_DIR / 'rank/author_rank_triplets.txt', 'w') as f:
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for t, ap, an in triplets:
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f.write(f'{t} {ap} {an}\n')
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print('结果已保存到', f.name)
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def main():
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parser = argparse.ArgumentParser(description='基于oag-cs数据集构造学者排名数据集')
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subparsers = parser.add_subparsers()
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build_val_parser = subparsers.add_parser('build-val', help='构造学者排名验证集')
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build_val_parser.add_argument('--use-field-name', action='store_true', help='使用领域名称（用于调试）')
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build_val_parser.set_defaults(func=build_ground_truth_valid)
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build_train_parser = subparsers.add_parser('build-train', help='构造学者排名训练集')
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build_train_parser.add_argument('--num-papers', type=int, default=5000, help='筛选领域的论文数阈值')
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build_train_parser.add_argument('--num-authors', type=int, default=100, help='每个领域取top k的学者数量')
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build_train_parser.add_argument('--use-field-name', action='store_true', help='使用领域名称（用于调试）')
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build_train_parser.set_defaults(func=build_ground_truth_train)
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evaluate_parser = subparsers.add_parser('eval', help='评估ground truth训练集的质量')
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evaluate_parser.set_defaults(func=evaluate_ground_truth)
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sample_parser = subparsers.add_parser('sample', help='采样三元组')
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sample_parser.add_argument('--seed', type=int, default=0, help='随机数种子')
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sample_parser.add_argument('--max-num', type=int, default=100, help='每个领域采样三元组最大数量')
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sample_parser.add_argument('--easy-margin', type=float, default=0.2, help='简单样本间隔（百分比）')
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sample_parser.add_argument('--hard-margin', type=float, default=0.05, help='困难样本间隔（百分比）')
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sample_parser.add_argument('--hard-ratio', type=float, default=0.5, help='困难样本比例')
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sample_parser.set_defaults(func=sample_triplets)
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args = parser.parse_args()
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print(args)
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args.func(args)
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if __name__ == '__main__':
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main()
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