Initial commit
This commit is contained in:
yichuan520030910320
187
research/utils/analyze_top3_positions.py
Normal file
187
research/utils/analyze_top3_positions.py
Normal file
@@ -0,0 +1,187 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
# 设置风格
|
||||
plt.style.use('ggplot')
|
||||
sns.set(font_scale=1.2)
|
||||
|
||||
# 读取数据 - 修改为自定义读取逻辑
|
||||
log_file = './top3_positions_log.txt'
|
||||
|
||||
# 手动解析文件
|
||||
data = []
|
||||
header = None
|
||||
with open(log_file, 'r') as f:
|
||||
lines = f.readlines()
|
||||
header = lines[0].strip().split(',')
|
||||
|
||||
# 检查是否存在ThreadID列
|
||||
has_thread_id = 'ThreadID' in header
|
||||
|
||||
for line in lines[1:]:
|
||||
# 跳过非数据行，如"Search X results:"
|
||||
if 'results:' in line or not ',' in line:
|
||||
continue
|
||||
|
||||
# 分割并解析数据行
|
||||
parts = line.strip().split(',')
|
||||
|
||||
# 检查数据是否符合格式
|
||||
if len(parts) >= 7: # 至少需要7个字段
|
||||
# 对于旧格式(无ThreadID)的数据
|
||||
if not has_thread_id and len(parts) == 7:
|
||||
data.append([parts[0], 0, parts[1], parts[2], parts[3], parts[4], parts[5], parts[6]])
|
||||
# 对于新格式(有ThreadID)的数据
|
||||
elif has_thread_id and len(parts) == 8:
|
||||
data.append(parts)
|
||||
# 处理不一致的格式
|
||||
elif not has_thread_id and len(parts) == 8:
|
||||
# 假设第二列是ThreadID
|
||||
data.append(parts)
|
||||
if not has_thread_id:
|
||||
has_thread_id = True
|
||||
header.insert(1, 'ThreadID')
|
||||
|
||||
# 确保header正确
|
||||
if not has_thread_id:
|
||||
header.insert(1, 'ThreadID')
|
||||
|
||||
# 创建DataFrame并确保列名正确
|
||||
if len(header) == 8: # 确保有8列
|
||||
df = pd.DataFrame(data, columns=header)
|
||||
else:
|
||||
# 如果header不正确，则使用默认列名
|
||||
default_header = ['Search#', 'ThreadID', 'FullSetSize', 'Rank', 'ID', 'PQ_Rank', 'PQ_Distance', 'Exact_Distance']
|
||||
df = pd.DataFrame(data, columns=default_header)
|
||||
|
||||
# 转换数值列
|
||||
df['Search#'] = pd.to_numeric(df['Search#'], errors='coerce').fillna(0).astype(int)
|
||||
df['ThreadID'] = pd.to_numeric(df['ThreadID'], errors='coerce').fillna(0).astype(int)
|
||||
df['FullSetSize'] = pd.to_numeric(df['FullSetSize'], errors='coerce').fillna(0).astype(int)
|
||||
df['Rank'] = pd.to_numeric(df['Rank'], errors='coerce').fillna(0).astype(int)
|
||||
df['ID'] = pd.to_numeric(df['ID'], errors='coerce').fillna(0).astype(int)
|
||||
df['PQ_Rank'] = pd.to_numeric(df['PQ_Rank'], errors='coerce').fillna(0).astype(int)
|
||||
df['PQ_Distance'] = pd.to_numeric(df['PQ_Distance'], errors='coerce').fillna(0).astype(float)
|
||||
df['Exact_Distance'] = pd.to_numeric(df['Exact_Distance'], errors='coerce').fillna(0).astype(float)
|
||||
|
||||
print(f"读取了 {len(df)} 行数据")
|
||||
print(f"搜索次数: {df['Search#'].nunique()}")
|
||||
print(f"线程数: {df['ThreadID'].nunique()}")
|
||||
|
||||
# 提取前3名的结果
|
||||
top3_df = df[df['Rank'] <= 3].copy()
|
||||
|
||||
# 分析PQ Rank的分布
|
||||
pq_positions = []
|
||||
for rank in [1, 2, 3]:
|
||||
rank_df = top3_df[top3_df['Rank'] == rank]
|
||||
pq_positions.append(rank_df['PQ_Rank'].values)
|
||||
|
||||
# 创建结果目录
|
||||
result_dir = './analysis_results'
|
||||
os.makedirs(result_dir, exist_ok=True)
|
||||
|
||||
# 1. 箱型图：展示top-3结果在PQ排序中的位置分布
|
||||
plt.figure(figsize=(10, 6))
|
||||
box_data = [top3_df[top3_df['Rank'] == i]['PQ_Rank'].values for i in [1, 2, 3]]
|
||||
sns.boxplot(data=box_data)
|
||||
plt.xticks([0, 1, 2], ['Top 1', 'Top 2', 'Top 3'])
|
||||
plt.ylabel('PQ Rank Position')
|
||||
plt.title('Distribution of PQ Ranks for Top-3 Exact Results')
|
||||
plt.savefig(os.path.join(result_dir, 'pq_rank_boxplot.png'), dpi=300)
|
||||
|
||||
# 2. 直方图：每个排名在PQ结果中的位置分布
|
||||
fig, axs = plt.subplots(1, 3, figsize=(18, 6))
|
||||
for i, rank in enumerate([1, 2, 3]):
|
||||
rank_df = top3_df[top3_df['Rank'] == rank]
|
||||
sns.histplot(x=rank_df['PQ_Rank'].values, bins=20, ax=axs[i])
|
||||
axs[i].set_title(f'Exact Rank {rank}')
|
||||
axs[i].set_xlabel('PQ Rank')
|
||||
axs[i].set_ylabel('Frequency')
|
||||
plt.tight_layout()
|
||||
plt.savefig(os.path.join(result_dir, 'pq_rank_histogram.png'), dpi=300)
|
||||
|
||||
# 3. 热力图：PQ排名与精确排名的关系
|
||||
plt.figure(figsize=(10, 8))
|
||||
# 只关注Top 20的排名
|
||||
bins = list(range(0, 22))
|
||||
pq_rank_bins = pd.cut(top3_df['PQ_Rank'], bins=bins)
|
||||
heatmap_data = pd.crosstab(pq_rank_bins, top3_df['Rank'])
|
||||
sns.heatmap(heatmap_data, cmap='YlGnBu', annot=True, fmt='d')
|
||||
plt.title('Heatmap of Exact Rank vs PQ Rank (Top 20)')
|
||||
plt.xlabel('Exact Rank')
|
||||
plt.ylabel('PQ Rank Range')
|
||||
plt.savefig(os.path.join(result_dir, 'rank_heatmap.png'), dpi=300)
|
||||
|
||||
# 4. 散点图：比较PQ距离和精确距离的关系
|
||||
plt.figure(figsize=(10, 8))
|
||||
sns.scatterplot(x=top3_df['Exact_Distance'], y=top3_df['PQ_Distance'], hue=top3_df['Rank'], palette='viridis')
|
||||
plt.title('PQ Distance vs Exact Distance')
|
||||
plt.xlabel('Exact Distance')
|
||||
plt.ylabel('PQ Distance')
|
||||
plt.legend(title='Exact Rank')
|
||||
# 添加对角线表示完美匹配
|
||||
min_val = min(top3_df['Exact_Distance'].min(), top3_df['PQ_Distance'].min())
|
||||
max_val = max(top3_df['Exact_Distance'].max(), top3_df['PQ_Distance'].max())
|
||||
plt.plot([min_val, max_val], [min_val, max_val], 'r--', alpha=0.5)
|
||||
plt.savefig(os.path.join(result_dir, 'distance_scatter.png'), dpi=300)
|
||||
|
||||
# 5. 折线图：PQ Rank随结果集大小的变化
|
||||
plt.figure(figsize=(12, 6))
|
||||
size_grouped = top3_df.groupby(['FullSetSize', 'Rank'])['PQ_Rank'].mean().reset_index()
|
||||
for rank in [1, 2, 3]:
|
||||
rank_data = size_grouped[size_grouped['Rank'] == rank]
|
||||
plt.plot(rank_data['FullSetSize'], rank_data['PQ_Rank'], marker='o', label=f'Rank {rank}')
|
||||
plt.xlabel('Result Set Size')
|
||||
plt.ylabel('Average PQ Rank')
|
||||
plt.title('Average PQ Rank by Result Set Size')
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
plt.savefig(os.path.join(result_dir, 'pq_rank_by_size.png'), dpi=300)
|
||||
|
||||
# 6. 百分比热力图：在PQ排名前K的概率
|
||||
top_k_values = [1, 5, 10, 20, 50, 100, 200, 300, 500, 700, 800, 900]
|
||||
top_k_probs = []
|
||||
|
||||
for rank in [1, 2, 3]:
|
||||
rank_df = top3_df[top3_df['Rank'] == rank]
|
||||
probs = []
|
||||
for k in top_k_values:
|
||||
prob = (rank_df['PQ_Rank'] <= k).mean() * 100
|
||||
probs.append(prob)
|
||||
top_k_probs.append(probs)
|
||||
|
||||
plt.figure(figsize=(10, 6))
|
||||
sns.heatmap(top_k_probs, annot=True, fmt='.1f', cmap='YlGnBu',
|
||||
xticklabels=[f'Top-{k}' for k in top_k_values],
|
||||
yticklabels=['Rank 1', 'Rank 2', 'Rank 3'])
|
||||
plt.title('Probability (%) of Finding Exact Top-K Results in PQ Top-K')
|
||||
plt.xlabel('PQ Top-K')
|
||||
plt.ylabel('Exact Rank')
|
||||
plt.savefig(os.path.join(result_dir, 'topk_probability.png'), dpi=300)
|
||||
|
||||
# 7. 生成统计摘要报告
|
||||
with open(os.path.join(result_dir, 'summary_report.txt'), 'w') as f:
|
||||
f.write(f"数据分析摘要\n")
|
||||
f.write(f"=================\n")
|
||||
f.write(f"总搜索次数: {df['Search#'].nunique()}\n")
|
||||
f.write(f"使用线程数: {df['ThreadID'].nunique()}\n\n")
|
||||
|
||||
f.write("精确排名前3的结果在PQ排序中的平均位置:\n")
|
||||
for rank in [1, 2, 3]:
|
||||
avg_pq_rank = top3_df[top3_df['Rank'] == rank]['PQ_Rank'].mean()
|
||||
median_pq_rank = top3_df[top3_df['Rank'] == rank]['PQ_Rank'].median()
|
||||
f.write(f" 排名 {rank}: 平均位置 = {avg_pq_rank:.2f}, 中位数位置 = {median_pq_rank:.1f}\n")
|
||||
|
||||
f.write("\n各排名结果在PQ排序前K的命中率:\n")
|
||||
for rank in [1, 2, 3]:
|
||||
f.write(f" 排名 {rank}:\n")
|
||||
for k in top_k_values:
|
||||
hit_rate = (top3_df[top3_df['Rank'] == rank]['PQ_Rank'] <= k).mean() * 100
|
||||
f.write(f" 在PQ前 {k} 中的命中率: {hit_rate:.2f}%\n")
|
||||
|
||||
print(f"分析完成! 结果已保存到 {result_dir} 目录")
|
||||
Reference in New Issue
Block a user