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plt_hist

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haiyingjiang7@1price.io
2025-09-28 16:44:00 +08:00
parent 0597239207
commit fc085eb77e
4 changed files with 69 additions and 180 deletions
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82
cross_validation/cross_validation.py
View File

@@ -6,6 +6,7 @@ import sys
from .parse_data import XTaskDataParser
from shortdeck.gen_hist import ShortDeckHistGenerator
import matplotlib.pyplot as plt
class DataValidator:
@@ -23,13 +24,12 @@ class DataValidator:
try:
print(" 解析导出的river数据...")
print('='*60)
river_records = self.parser.parse_river_ehs_with_cards()
river_records = self.parser.parse_river_ehs_with_cards(max_records=max_samples)
if not river_records:
return {'error': '没有解析到river记录', 'success': False}
sample_records = np.random.choice(river_records, size=max_samples, replace=False)
sample_records = river_records
print(f" 选择 {len(sample_records)} 个样本进行验证")
matches = 0
@@ -95,6 +95,21 @@ class DataValidator:
except Exception as e:
print(f" River验证失败: {e}")
return {'error': str(e), 'success': False}
def print_sample_record(self, i, src_hist, cur_hist, player_cards, board_cards):
player_str = " ".join(str(c) for c in player_cards)
board_str = " ".join(str(c) for c in board_cards)
print("="*60)
print(f"样本 {i+1}: [{player_str}] + [{board_str}]")
print("bin src src_norm cur cur_norm")
src_hist = np.array(src_hist)
cur_hist = np.array(cur_hist)
src_hist_norm = src_hist / src_hist.sum() if src_hist.sum() > 0 else src_hist
cur_hist_norm = cur_hist / cur_hist.sum() if cur_hist.sum() > 0 else cur_hist
for i in range(min(len(src_hist), 30)):
if src_hist[i] > 0 or cur_hist[i] > 0:
print(f"bin[{i}], {src_hist[i]:8.3f}, {src_hist_norm[i]:8.3f}, {cur_hist[i]:8.3f}, {cur_hist_norm[i]:8.3f}")
def validate_turn_samples(self, max_samples: int = 10):
@@ -140,20 +155,24 @@ class DataValidator:
src_hist_norm,
cur_hist_norm
)
is_low_emd = emd_dist < 0.2
if is_low_emd:
low_emd_count += 1
# 显示详细信息（前3个样本）
if i < 3:
player_str = " ".join(str(c) for c in player_cards)
board_str = " ".join(str(c) for c in board_cards)
print(f" 样本 {i+1}: [{player_str}] + [{board_str}]")
print(f" 原始直方图: bins={len(src_hist)}, sum={src_hist.sum():.3f}, 非零bins={np.count_nonzero(src_hist)}")
print(f" 生成直方图: bins={len(cur_hist)}, sum={cur_hist.sum():.3f}, 非零bins={np.count_nonzero(cur_hist)}")
print(f" 归一化后EMD距离: {emd_dist:.6f}")
low_emd_count += 1 if emd_dist < 0.2 else 0
errors += 1 if emd_dist >= 0.2 else 0
emd_distances.append(emd_dist)
self.print_sample_record(i, src_hist, cur_hist, player_cards, board_cards)
# 画图显示
plt.plot(src_hist, label='src', marker='o')
plt.plot(cur_hist, label='cur', marker='x')
plt.title(f"turn_hist_emd={emd_dist:.6f}")
plt.xlabel("Bins")
plt.ylabel("Frequency")
plt.legend()
plt.show()
except Exception as e:
errors += 1
if errors <= 3:
@@ -228,11 +247,10 @@ class DataValidator:
src_hist_norm,
cur_hist_norm
)
# emd_distances.append(emd_dist)
# is_low_emd = emd_dist < 0.2 # EMD阈值
# if is_low_emd:
# low_emd_count += 1
emd_distances.append(emd_dist)
low_emd_count += 1 if emd_dist < 10 else 0
errors += 1 if emd_dist >= 10 else 0
# 显示详细信息
player_str = " ".join(str(c) for c in player_cards)
@@ -241,7 +259,22 @@ class DataValidator:
print(f" 原始直方图: bins={len(src_hist)}, sum={src_hist.sum():.3f}, 非零bins={np.count_nonzero(src_hist)}")
print(f" 生成直方图: bins={len(cur_hist)}, sum={cur_hist.sum():.3f}, 非零bins={np.count_nonzero(cur_hist)}")
print(f" 归一化后EMD距离: {emd_dist:.6f}")
print("bin src src_norm cur cur_norm")
for i in range(min(len(src_hist), 30)):
if src_hist[i] > 0 or cur_hist[i] > 0:
print(f"bin[{i}], {src_hist[i]:8.3f}, {src_hist_norm[i]:8.3f}, {cur_hist[i]:8.3f}, {cur_hist_norm[i]:8.3f}")
self.print_sample_record(i, src_hist, cur_hist, player_cards, board_cards)
# 画图显示
plt.plot(src_hist, label='src', marker='o')
plt.plot(cur_hist, label='cur', marker='x')
plt.title(f"flop_hist_emd={emd_dist:.6f}")
plt.xlabel("Bins")
plt.ylabel("Frequency")
plt.legend()
plt.show()
except Exception as e:
errors += 1
if errors <= 3:
@@ -276,7 +309,6 @@ class DataValidator:
print(" 导出数据EHS验证")
print("*"*60)
print("验证策略: 从xtask导出数据中抽取牌面 → 短牌型生成器重计算 → 比较一致性")
# 执行各阶段验证
results = {}
@@ -312,6 +344,7 @@ class DataValidator:
print(f" 样本数量: {results['turn']['total_samples']}")
print(f" 低EMD率: {results['turn']['low_emd_rate']:.1%}")
print(f" 平均EMD: {results['turn']['mean_emd_distance']:.6f}")
print(f" 抽样EMD: {[emd for emd in results['turn']['emd_distances'][:5]]}")
if results['turn']['success']:
passed_stages += 1
else:
@@ -325,6 +358,7 @@ class DataValidator:
print(f" 样本数量: {results['flop']['total_samples']}")
print(f" 低EMD率: {results['flop']['low_emd_rate']:.1%}")
print(f" 平均EMD: {results['flop']['mean_emd_distance']:.6f}")
print(f" 抽样EMD: {[emd for emd in results['flop']['emd_distances'][:5]]}")
if results['flop']['success']:
passed_stages += 1
else:

52
cross_validation/debug_emd.py
View File

@@ -1,52 +0,0 @@
#!/usr/bin/env python3
"""
调试
turn/flop阶段EMD在导出的数据与生成的数据间的差异
"""
import numpy as np
from cross_validation import DataValidator
validator = DataValidator()
print("解析Turn样本...")
turn_records = validator.parser.parse_turn_hist_with_cards(max_records=1)
if turn_records:
record = turn_records[0]
player_cards = record.player_cards
board_cards = record.board_cards
src_hist = record.bins
cur_hist = validator.generator.generate_turn_histogram(
player_cards, board_cards, num_bins=len(src_hist)
)
src_hist_norm = src_hist / src_hist.sum() if src_hist.sum() > 0 else src_hist
print(f"\n牌面: {[str(c) for c in player_cards]} + {[str(c) for c in board_cards]}")
print(f"bin数量: {len(src_hist)} vs {len(cur_hist)}")
print(f"原始直方图 - 和: {src_hist.sum():.3f}, 归一化后: {src_hist_norm.sum():.3f}")
print(f"生成直方图 - 和: {sum(cur_hist):.3f}")
print("\n前10个bin对比:")
print("Bin 原始值 归一化 生成值")
for i in range(min(10, len(src_hist))):
print(f"{i:3d} {src_hist[i]:8.3f} {src_hist_norm[i]:8.3f} {cur_hist[i]:8.3f}")
# 查看非零bin的分布
src_nonzero = np.nonzero(src_hist_norm)[0]
cur_nonzero = np.nonzero(cur_hist)[0]
print(f"\n非零bins位置:")
print(f"原始: {src_nonzero[:10]}...")
print(f"生成: {cur_nonzero[:10]}...")
# 计算分布的统计特征
src_mean = np.average(range(len(src_hist_norm)), weights=src_hist_norm)
cur_mean = np.average(range(len(cur_hist)), weights=cur_hist)
print(f"\n分布特征:")
print(f"原始分布重心: {src_mean:.2f}")
print(f"生成分布重心: {cur_mean:.2f}")
print(f"重心差异: {abs(src_mean - cur_mean):.2f}")

20
cross_validation/parse_data.py
View File

@@ -1,4 +1,5 @@
import numpy as np
import random
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
from poker.card import Card, ShortDeckRank, Suit
@@ -164,17 +165,19 @@ class XTaskDataParser:
self.data_path = data_path
self.decoder = OpenPQLDecoder()
def parse_river_ehs_with_cards(self, filename: str = "river_ehs.npy") -> List[RiverEHSRecord]:
def parse_river_ehs_with_cards(self, filename: str = "river_ehs.npy", max_records: int = 1000) -> List[RiverEHSRecord]:
filepath = f"{self.data_path}/{filename}"
try:
raw_data = np.load(filepath)
print(f"加载river_EHS: {raw_data.shape} 条记录,数据类型: {raw_data.dtype}")
# 抽样
data_to_process = random.sample(list(raw_data), min(max_records, len(raw_data)))
records = []
decode_errors = 0
for i, row in enumerate(raw_data):
for i, row in enumerate(data_to_process):
try:
board_id = int(row['board'])
player_id = int(row['player'])
@@ -245,10 +248,8 @@ class XTaskDataParser:
records = []
decode_errors = 0
# 限制处理数量以避免内存问题
# todo:抽样优化
data_to_process = raw_data[:max_records] if len(raw_data) > max_records else raw_data
# 抽样
data_to_process = random.sample(list(raw_data), min(max_records, len(raw_data)))
for i, row in enumerate(data_to_process):
try:
@@ -305,8 +306,7 @@ class XTaskDataParser:
print(f"加载flop_hist: {raw_data.shape} 条记录,限制处理: {max_records:,} 条记录")
records = []
decode_errors = 0
data_to_process = raw_data[:max_records] if len(raw_data) > max_records else raw_data
data_to_process = random.sample(list(raw_data), min(max_records, len(raw_data)))
for i, row in enumerate(data_to_process):
try: