output/test_knn_2/eval.py

import json
import numpy as np
from metrics.graph_matching import (
    get_triple_match_f1, 
    get_graph_match_accuracy,
    get_bert_score,
    get_bleu_rouge,
    split_to_edges,
    get_tokens,
    get_ged
)

def load_data(gold_path, pred_path):
    '''
    数据加载处理：
    只评估在预测数据中出现的文本对应的三元组
    自动匹配真实数据和预测数据中的对应项
    多维度评估：
    Triple Match F1：评估三元组的精确匹配程度
    Graph Match Accuracy：评估图结构的匹配程度
    BERT Score：评估语义相似度
    BLEU & ROUGE：评估文本生成质量
    图编辑距离(GED)：评估图结构差异
    '''
    # 加载真实数据
    with open(gold_path, 'r', encoding='utf-8') as f:
        gold_data = json.load(f)
    
    # 加载预测数据
    with open(pred_path, 'r', encoding='utf-8') as f:
        pred_data = json.load(f)
    
    # 提取三元组列表
    gold_graphs = []
    pred_graphs = []
    
    # 确保只评估在预测数据中出现的文本对应的三元组
    for pred_item in pred_data:
        pred_text = pred_item['text']
        # 在gold_data中找到对应的文本
        for gold_item in gold_data:
            if gold_item['text'] == pred_text:
                gold_graphs.append(gold_item['triple_list'])
                pred_graphs.append(pred_item['triple_list'])
                break
    
    return gold_graphs, pred_graphs

def evaluate_triples(gold_graphs, pred_graphs):
    print("开始评估...")
    print("="*50)
    
    # 1. Triple Match F1
    precision, recall, f1 = get_triple_match_f1(gold_graphs, pred_graphs)
    print("Triple Match")
    print(f"精确率: {precision:.4f}, 召回率: {recall:.4f}, F1: {f1:.4f}")


        # # 2. Graph Match Accuracy
    # graph_acc = get_graph_match_accuracy(pred_graphs, gold_graphs)
    # print(f"图匹配准确率: {graph_acc:.10f}")
    
    # 3. BERT Score
    gold_edges = split_to_edges(gold_graphs)
    pred_edges = split_to_edges(pred_graphs)
    precisions_BS, recalls_BS, f1s_BS = get_bert_score(gold_edges, pred_edges)
    print(f"BERT Score:")
    print(f"- Precision: {precisions_BS.mean():.4f}")
    print(f"- Recall: {recalls_BS.mean():.4f}")
    print(f"- F1: {f1s_BS.mean():.4f}")
    
    # # 4. BLEU & ROUGE
    # gold_tokens, pred_tokens = get_tokens(gold_edges, pred_edges)
    # p_rouge, r_rouge, f1_rouge, p_bleu, r_bleu, f1_bleu = get_bleu_rouge(
    #     gold_tokens, pred_tokens, gold_edges, pred_edges
    # )
    # print(f"\nBLEU分数:")
    # print(f"- Precision: {p_bleu.mean():.4f}")
    # print(f"- Recall: {r_bleu.mean():.4f}")
    # print(f"- F1: {f1_bleu.mean():.4f}")
    
    # print(f"\nROUGE分数:")
    # print(f"- Precision: {p_rouge.mean():.4f}")
    # print(f"- Recall: {r_rouge.mean():.4f}")
    # print(f"- F1: {f1_rouge.mean():.4f}")
    
    # # 5. 图编辑距离(GED)
    # total_ged = 0
    # for gold, pred in zip(gold_graphs, pred_graphs):
    #     ged = get_ged(gold, pred)
    #     total_ged += ged
    # avg_ged = total_ged / len(gold_graphs)
    # print(f"\n平均图编辑距离: {avg_ged:.4f}")
    
    # 返回所有指标
    return {

        'triple_match': {
            'precision': precision,
            'recall': recall,
            'f1': f1
        },
        

        # 'graph_acc': graph_acc,

        'bert_score': {
            'precision': precisions_BS.mean(),
            'recall': recalls_BS.mean(),
            'f1': f1s_BS.mean()
        },
        # 'bleu': {
        #     'precision': p_bleu.mean(),
        #     'recall': r_bleu.mean(),
        #     'f1': f1_bleu.mean()
        # },
        # 'rouge': {
        #     'precision': p_rouge.mean(),
        #     'recall': r_rouge.mean(),
        #     'f1': f1_rouge.mean()
        # },
        # 'ged': avg_ged
    }

if __name__ == '__main__':
    # 设置文件路径
    gold_path = './data/train_triples.json'
    pred_path = './output/gpt.json'
    
    # 加载数据
    gold_graphs, pred_graphs = load_data(gold_path, pred_path)
    
    # 评估并打印结果
    results = evaluate_triples(gold_graphs, pred_graphs)