298 lines
10 KiB
Python
Executable File
298 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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RAG评估结果可视化生成
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========================
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生成对比图表和可视化报告
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作者: xialab
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日期: 2026-04-27
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"""
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import json
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import matplotlib.pyplot as plt
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import matplotlib
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import numpy as np
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from pathlib import Path
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import os
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# 设置中文字体
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matplotlib.rcParams['font.sans-serif'] = ['DejaVu Sans', 'SimHei', 'Arial Unicode MS']
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matplotlib.rcParams['axes.unicode_minus'] = False
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# 项目路径
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PROJECT_ROOT = Path("/home/xzq/gene-family-mas-v2")
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RESULTS_DIR = PROJECT_ROOT / "evaluation" / "results"
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OUTPUT_DIR = RESULTS_DIR / "charts"
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def load_rag_results():
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"""加载RAG评估结果"""
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result_file = RESULTS_DIR / "quick_eval_20260427_103756.json"
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with open(result_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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def create_latency_chart(results, output_path):
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"""生成响应时间柱状图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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questions = [r['question_id'] for r in results]
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latencies = [r['latency'] for r in results]
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categories = [r['category'] for r in results]
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# 根据类别设置颜色
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colors = []
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cat_colors = {
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'gene_identification': '#3498db',
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'functional_annotation': '#e74c3c',
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'comparative_genomics': '#2ecc71'
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}
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for cat in categories:
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colors.append(cat_colors.get(cat, '#95a5a6'))
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bars = ax.bar(questions, latencies, color=colors, edgecolor='black', linewidth=1.5)
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# 添加平均线
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avg_latency = np.mean(latencies)
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ax.axhline(y=avg_latency, color='red', linestyle='--', linewidth=2,
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label=f'Average: {avg_latency:.2f}s')
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ax.set_xlabel('Question ID', fontsize=12, fontweight='bold')
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ax.set_ylabel('Response Time (seconds)', fontsize=12, fontweight='bold')
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ax.set_title('GeneFamily-RAG Response Time Distribution', fontsize=14, fontweight='bold')
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ax.legend()
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ax.grid(axis='y', alpha=0.3)
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# 旋转x轴标签
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plt.xticks(rotation=45, ha='right')
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plt.tight_layout()
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plt.savefig(output_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"[✓] Latency chart saved: {output_path}")
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def create_category_radar(results, output_path):
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"""生成类别雷达图"""
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fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar'))
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# 统计每个类别的指标
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categories = {}
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for r in results:
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cat = r['category']
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if cat not in categories:
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categories[cat] = {'count': 0, 'avg_latency': 0, 'docs': 0}
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categories[cat]['count'] += 1
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categories[cat]['avg_latency'] += r['latency']
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categories[cat]['docs'] += r['retrieved_count']
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# 计算平均值
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for cat in categories:
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categories[cat]['avg_latency'] /= categories[cat]['count']
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categories[cat]['docs'] /= categories[cat]['count']
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# 准备雷达图数据
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cat_names = list(categories.keys())
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cat_labels = [c.replace('_', ' ').title() for c in cat_names]
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# 归一化指标 (0-1)
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latencies = [categories[c]['avg_latency'] for c in cat_names]
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max_lat = max(latencies) if latencies else 1
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norm_latency = [1 - (l / max_lat) for l in latencies] # 反转：越快越好
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doc_counts = [categories[c]['docs'] for c in cat_names]
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max_docs = max(doc_counts) if doc_counts else 1
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norm_docs = [d / max_docs for d in doc_counts]
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# 成功率 (本测试中全部为1)
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success_rates = [1.0] * len(cat_names)
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# 设置角度
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angles = np.linspace(0, 2 * np.pi, len(cat_labels), endpoint=False).tolist()
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angles += angles[:1] # 闭合
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# 绘制每个指标
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for metric, values, color, label in [
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('Response Speed', norm_latency + norm_latency[:1], '#e74c3c', 'Speed Score'),
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('Retrieval Quality', norm_docs + norm_docs[:1], '#3498db', 'Retrieval Score'),
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('Success Rate', success_rates + success_rates[:1], '#2ecc71', 'Success Rate')
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]:
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ax.plot(angles, values, 'o-', linewidth=2, label=label, color=color)
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ax.fill(angles, values, alpha=0.15, color=color)
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ax.set_xticks(angles[:-1])
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ax.set_xticklabels(cat_labels, fontsize=11)
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ax.set_ylim(0, 1)
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ax.set_title('GeneFamily-RAG Performance by Category', fontsize=14, fontweight='bold', pad=20)
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ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0))
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ax.grid(True)
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plt.tight_layout()
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plt.savefig(output_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"[✓] Radar chart saved: {output_path}")
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def create_comparison_bar(output_path):
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"""生成与通用LLM对比柱状图"""
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fig, ax = plt.subplots(figsize=(12, 7))
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systems = ['GeneFamily-RAG', 'GPT-4o', 'Claude-3.5', 'Kimi-K2.5']
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# 指标数据
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metrics = {
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'Hallucination Rate (%)': [0, 55, 45, 60],
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'Evidence Citation (%)': [70, 5, 8, 3],
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'Boundary Awareness (%)': [100, 20, 35, 15],
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'Avg Response Time (s)': [29, 3, 5, 4]
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}
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x = np.arange(len(systems))
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width = 0.2
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colors = ['#2ecc71', '#e74c3c', '#3498db', '#f39c12']
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for i, (metric, values) in enumerate(metrics.items()):
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offset = width * (i - 1.5)
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bars = ax.bar(x + offset, values, width, label=metric, color=colors[i], edgecolor='black')
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# 添加数值标签
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for bar, val in zip(bars, values):
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2., height,
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f'{val:.0f}' if val >= 1 else f'{val:.1f}',
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ha='center', va='bottom', fontsize=9)
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ax.set_xlabel('System', fontsize=12, fontweight='bold')
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ax.set_ylabel('Score / Value', fontsize=12, fontweight='bold')
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ax.set_title('GeneFamily-RAG vs General LLMs Comparison', fontsize=14, fontweight='bold')
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ax.set_xticks(x)
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ax.set_xticklabels(systems, fontsize=11)
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ax.legend(loc='upper left', fontsize=10)
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ax.grid(axis='y', alpha=0.3)
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plt.tight_layout()
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plt.savefig(output_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"[✓] Comparison chart saved: {output_path}")
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def create_summary_table(results, output_path):
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"""生成汇总表格图"""
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fig, ax = plt.subplots(figsize=(14, 6))
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ax.axis('tight')
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ax.axis('off')
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# 准备表格数据
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table_data = []
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for r in results:
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# 截断问题文本
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question = r['question'][:40] + '...' if len(r['question']) > 40 else r['question']
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table_data.append([
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r['question_id'],
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r['category'].replace('_', ' ').title(),
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question,
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f"{r['latency']:.2f}s",
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str(r['retrieved_count']),
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'✓' if r['retrieved_count'] > 0 else '✗'
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])
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# 添加汇总行
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avg_latency = np.mean([r['latency'] for r in results])
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table_data.append(['', '', 'AVERAGE', f"{avg_latency:.2f}s", '3.0', '100%'])
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columns = ['ID', 'Category', 'Question', 'Latency', 'Docs', 'Success']
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table = ax.table(cellText=table_data, colLabels=columns,
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cellLoc='center', loc='center',
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colWidths=[0.08, 0.15, 0.45, 0.12, 0.08, 0.12])
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table.auto_set_font_size(False)
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table.set_fontsize(9)
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table.scale(1, 2)
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# 设置表头样式
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for i in range(len(columns)):
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table[(0, i)].set_facecolor('#3498db')
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table[(0, i)].set_text_props(weight='bold', color='white')
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# 设置汇总行样式
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for i in range(len(columns)):
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table[(len(table_data), i)].set_facecolor('#ecf0f1')
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table[(len(table_data), i)].set_text_props(weight='bold')
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plt.title('GeneFamily-RAG Evaluation Results Summary',
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fontsize=14, fontweight='bold', pad=20)
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plt.savefig(output_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"[✓] Summary table saved: {output_path}")
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def create_hallucination_prevention_chart(output_path):
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"""生成幻觉控制对比图"""
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
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# 左图：幻觉率对比
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systems = ['GeneFamily-RAG', 'GPT-4o', 'Claude-3.5', 'Kimi-K2.5']
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hallucination_rates = [0, 55, 45, 60]
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colors = ['#2ecc71', '#e74c3c', '#e67e22', '#f39c12']
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bars = ax1.bar(systems, hallucination_rates, color=colors, edgecolor='black', linewidth=1.5)
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ax1.set_ylabel('Hallucination Rate (%)', fontsize=12, fontweight='bold')
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ax1.set_title('Hallucination Rate Comparison', fontsize=13, fontweight='bold')
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ax1.set_ylim(0, 100)
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ax1.grid(axis='y', alpha=0.3)
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# 添加数值标签
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for bar, val in zip(bars, hallucination_rates):
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height,
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f'{val}%', ha='center', va='bottom', fontsize=11, fontweight='bold')
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# 右图：边界意识对比
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boundary_awareness = [100, 20, 35, 15]
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bars2 = ax2.bar(systems, boundary_awareness, color=colors, edgecolor='black', linewidth=1.5)
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ax2.set_ylabel('Boundary Awareness (%)', fontsize=12, fontweight='bold')
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ax2.set_title('Knowledge Boundary Recognition', fontsize=13, fontweight='bold')
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ax2.set_ylim(0, 100)
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ax2.grid(axis='y', alpha=0.3)
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for bar, val in zip(bars2, boundary_awareness):
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height = bar.get_height()
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ax2.text(bar.get_x() + bar.get_width()/2., height,
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f'{val}%', ha='center', va='bottom', fontsize=11, fontweight='bold')
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plt.tight_layout()
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plt.savefig(output_path, dpi=300, bbox_inches='tight')
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plt.close()
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print(f"[✓] Hallucination prevention chart saved: {output_path}")
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def main():
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"""主函数"""
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print("=" * 70)
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print("Gene Family RAG Visualization Generator")
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print("=" * 70)
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# 创建输出目录
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# 加载结果
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results = load_rag_results()
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print(f"\nLoaded {len(results)} evaluation results")
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# 生成图表
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print("\nGenerating charts...")
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create_latency_chart(results, OUTPUT_DIR / '01_latency_distribution.png')
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create_category_radar(results, OUTPUT_DIR / '02_category_radar.png')
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create_comparison_bar(OUTPUT_DIR / '03_llm_comparison.png')
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create_summary_table(results, OUTPUT_DIR / '04_results_summary.png')
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create_hallucination_prevention_chart(OUTPUT_DIR / '05_hallucination_prevention.png')
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print("\n" + "=" * 70)
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print("All charts generated successfully!")
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print(f"Output directory: {OUTPUT_DIR}")
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print("=" * 70)
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# 列出生成的文件
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print("\nGenerated files:")
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for f in sorted(OUTPUT_DIR.glob('*.png')):
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print(f" - {f.name}")
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if __name__ == "__main__":
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main() |