task5
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357
cross_validation/cross_validation.py
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357
cross_validation/cross_validation.py
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#!/usr/bin/env python3
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import numpy as np
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from typing import List, Dict, Tuple
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from scipy.stats import wasserstein_distance
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import sys
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from .parse_data import XTaskDataParser
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from shortdeck.gen_hist import ShortDeckHistGenerator
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class DataValidator:
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def __init__(self, data_path: str = "ehs_data"):
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self.parser = XTaskDataParser(data_path)
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self.generator = ShortDeckHistGenerator()
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print(" DataValidator初始化完成")
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print(f" 生成器短牌型大小: {len(self.generator.full_deck)}")
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def validate_river_samples(self, max_samples: int = 20) :
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# print(f"\n 验证river_EHS样本 (最大样本数: {max_samples})")
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try:
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print(" 解析导出的river数据...")
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print('='*60)
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river_records = self.parser.parse_river_ehs_with_cards()
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if not river_records:
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return {'error': '没有解析到river记录', 'success': False}
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sample_records = np.random.choice(river_records, size=max_samples, replace=False)
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print(f" 选择 {len(sample_records)} 个样本进行验证")
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matches = 0
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errors = 0
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differences = []
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for i, record in enumerate(sample_records):
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try:
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player_cards = record.player_cards
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board_cards = record.board_cards
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src_river_ehs = record.ehs
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cur_river_ehs = self.generator.generate_river_ehs(player_cards, board_cards)
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ehs_difference = abs(src_river_ehs - cur_river_ehs)
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player_str = " ".join(str(c) for c in player_cards)
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board_str = " ".join(str(c) for c in board_cards)
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print(f" 样本 {i+1}: [{player_str}] + [{board_str}]")
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print(f" 原始EHS: {src_river_ehs:.6f}")
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print(f" 重算EHS: {cur_river_ehs:.6f}")
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print(f" 差异: {ehs_difference:.6f}")
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# 判断匹配 (允许小的数值差异)
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tolerance = 1e-6
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if ehs_difference < tolerance:
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matches += 1
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else:
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differences.append(ehs_difference)
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except Exception as e:
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errors += 1
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if errors <= 3:
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print(f" 样本 {i+1} 计算失败: {e}")
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# 统计结果
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total_samples = len(sample_records)
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match_rate = matches / total_samples if total_samples > 0 else 0
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mean_diff = np.mean(differences) if differences else 0
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max_diff = np.max(differences) if differences else 0
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result = {
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'total_samples': total_samples,
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'matches': matches,
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'match_rate': match_rate,
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'mean_difference': mean_diff,
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'max_difference': max_diff,
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'errors': errors,
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'success': match_rate > 0.8 and mean_diff < 0.05
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}
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print(f" River验证完成:")
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print('='*60)
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print(f" 匹配数: {matches}/{total_samples} ({match_rate:.1%})")
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print(f" 平均差异: {mean_diff:.6f}")
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print(f" 最大差异: {max_diff:.6f}")
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return result
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except Exception as e:
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print(f" River验证失败: {e}")
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return {'error': str(e), 'success': False}
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def validate_turn_samples(self, max_samples: int = 10):
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# print(f"\n 验证turn_HIST样本 (最大样本数: {max_samples})")
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try:
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print(" 解析导出的Turn数据...")
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print('='*60)
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print('='*60)
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turn_records = self.parser.parse_turn_hist_with_cards(max_records=max_samples)
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if not turn_records:
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return {'error': '没有解析到Turn记录', 'success': False}
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print(f" 解析到 {len(turn_records)} 个Turn样本")
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low_emd_count = 0
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emd_distances = []
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errors = 0
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for i, record in enumerate(turn_records):
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try:
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player_cards = record.player_cards
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board_cards = record.board_cards
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src_hist = np.array(record.bins)
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cur_hist = self.generator.generate_turn_histogram(
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player_cards, board_cards, num_bins=len(src_hist)
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)
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cur_hist = np.array(cur_hist)
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# 归一化
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src_hist_norm = src_hist / src_hist.sum() if src_hist.sum() > 0 else src_hist
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cur_hist_norm = cur_hist / cur_hist.sum() if cur_hist.sum() > 0 else cur_hist
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# 计算EMD距离
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emd_dist = wasserstein_distance(
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range(len(src_hist_norm)),
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range(len(cur_hist_norm)),
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src_hist_norm,
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cur_hist_norm
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)
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is_low_emd = emd_dist < 0.2
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if is_low_emd:
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low_emd_count += 1
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# 显示详细信息（前3个样本）
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if i < 3:
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player_str = " ".join(str(c) for c in player_cards)
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board_str = " ".join(str(c) for c in board_cards)
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print(f" 样本 {i+1}: [{player_str}] + [{board_str}]")
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print(f" 原始直方图: bins={len(src_hist)}, sum={src_hist.sum():.3f}, 非零bins={np.count_nonzero(src_hist)}")
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print(f" 生成直方图: bins={len(cur_hist)}, sum={cur_hist.sum():.3f}, 非零bins={np.count_nonzero(cur_hist)}")
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print(f" 归一化后EMD距离: {emd_dist:.6f}")
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except Exception as e:
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errors += 1
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if errors <= 3:
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print(f" 样本 {i+1} 计算失败: {e}")
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# 统计结果
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total_samples = len(turn_records)
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low_emd_rate = low_emd_count / total_samples if total_samples > 0 else 0
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mean_emd = np.mean(emd_distances) if emd_distances else float('inf')
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result = {
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'total_samples': total_samples,
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'low_emd_count': low_emd_count,
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'low_emd_rate': low_emd_rate,
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'mean_emd_distance': mean_emd,
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'emd_distances': emd_distances,
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'errors': errors,
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'success': low_emd_rate > 0.6 and mean_emd < 0.5
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}
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print(f" Turn验证完成:")
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print(f" 低EMD数: {low_emd_count}/{total_samples} ({low_emd_rate:.1%})")
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print(f" 平均EMD: {mean_emd:.6f}")
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return result
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except Exception as e:
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print(f" Turn验证失败: {e}")
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return {'error': str(e), 'success': False}
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def validate_flop_samples(self, max_samples: int = 5):
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# print(f"\n 验证Flop直方图样本 (最大样本数: {max_samples})")
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try:
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print(" 解析导出Flop数据...")
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print('='*60)
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print('='*60)
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print('='*60)
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flop_records = self.parser.parse_flop_hist_with_cards(max_records=max_samples)
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if not flop_records:
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return {'error': '没有解析到Flop记录', 'success': False}
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print(f" 解析到 {len(flop_records)} 个Flop样本")
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low_emd_count = 0
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emd_distances = []
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errors = 0
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for i, record in enumerate(flop_records):
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try:
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print(f" 处理样本 {i+1}/{len(flop_records)}...")
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player_cards = record.player_cards
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board_cards = record.board_cards
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src_hist = np.array(record.bins)
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cur_hist = self.generator.generate_flop_histogram(
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player_cards, board_cards, num_bins=len(src_hist)
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)
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cur_hist = np.array(cur_hist)
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src_hist_norm = src_hist / src_hist.sum() if src_hist.sum() > 0 else src_hist
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cur_hist_norm = cur_hist / cur_hist.sum() if cur_hist.sum() > 0 else cur_hist
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# 计算EMD距离
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emd_dist = wasserstein_distance(
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range(len(src_hist_norm)),
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range(len(cur_hist_norm)),
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src_hist_norm,
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cur_hist_norm
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)
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# emd_distances.append(emd_dist)
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# is_low_emd = emd_dist < 0.2 # EMD阈值
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# if is_low_emd:
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# low_emd_count += 1
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# 显示详细信息
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player_str = " ".join(str(c) for c in player_cards)
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board_str = " ".join(str(c) for c in board_cards)
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print(f" 样本 {i+1}: [{player_str}] + [{board_str}]")
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print(f" 原始直方图: bins={len(src_hist)}, sum={src_hist.sum():.3f}, 非零bins={np.count_nonzero(src_hist)}")
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print(f" 生成直方图: bins={len(cur_hist)}, sum={cur_hist.sum():.3f}, 非零bins={np.count_nonzero(cur_hist)}")
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print(f" 归一化后EMD距离: {emd_dist:.6f}")
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except Exception as e:
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errors += 1
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if errors <= 3:
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print(f" 样本 {i+1} 计算失败: {e}")
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# 统计结果
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total_samples = len(flop_records)
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low_emd_rate = low_emd_count / total_samples if total_samples > 0 else 0
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mean_emd = np.mean(emd_distances) if emd_distances else float('inf')
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result = {
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'total_samples': total_samples,
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'low_emd_count': low_emd_count,
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'low_emd_rate': low_emd_rate,
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'mean_emd_distance': mean_emd,
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'emd_distances': emd_distances,
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'errors': errors,
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'success': low_emd_rate > 0.4 and mean_emd < 0.5
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}
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print(f" Flop验证完成:")
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print(f" 低EMD数: {low_emd_count}/{total_samples} ({low_emd_rate:.1%})")
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print(f" 平均EMD: {mean_emd:.6f}")
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return result
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except Exception as e:
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print(f" Flop验证失败: {e}")
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return {'error': str(e), 'success': False}
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def run_full_validation(self, river_samples: int = 20, turn_samples: int = 10, flop_samples: int = 5) -> Dict:
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print(" 导出数据EHS验证")
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print("*"*60)
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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")
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# 执行各阶段验证
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results = {}
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results['river'] = self.validate_river_samples(river_samples)
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results['turn'] = self.validate_turn_samples(turn_samples)
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results['flop'] = self.validate_flop_samples(flop_samples)
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print(f"\n{'='*60}")
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print(" 验证完毕")
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print(f"{'='*60}")
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passed_stages = 0
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total_stages = 3
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# River结果
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print(f"\n RIVER阶段:")
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if 'error' not in results['river']:
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status = " 通过" if results['river']['success'] else " 失败"
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print(f" 验证结果: {status}")
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print(f" 样本数量: {results['river']['total_samples']}")
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print(f" 匹配率: {results['river']['match_rate']:.1%}")
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print(f" 平均差异: {results['river']['mean_difference']:.6f}")
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if results['river']['success']:
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passed_stages += 1
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else:
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print(f" 错误: {results['river']['error']}")
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# Turn结果
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print(f"\n TURN阶段:")
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if 'error' not in results['turn']:
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status = " 通过" if results['turn']['success'] else " 失败"
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print(f" 验证结果: {status}")
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print(f" 样本数量: {results['turn']['total_samples']}")
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print(f" 低EMD率: {results['turn']['low_emd_rate']:.1%}")
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print(f" 平均EMD: {results['turn']['mean_emd_distance']:.6f}")
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if results['turn']['success']:
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passed_stages += 1
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else:
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print(f" 错误: {results['turn']['error']}")
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# Flop结果
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print(f"\n FLOP阶段:")
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if 'error' not in results['flop']:
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status = " 通过" if results['flop']['success'] else " 失败"
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print(f" 验证结果: {status}")
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print(f" 样本数量: {results['flop']['total_samples']}")
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print(f" 低EMD率: {results['flop']['low_emd_rate']:.1%}")
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print(f" 平均EMD: {results['flop']['mean_emd_distance']:.6f}")
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if results['flop']['success']:
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passed_stages += 1
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else:
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print(f" 错误: {results['flop']['error']}")
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# 总体结果
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passed_stages = 0
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total_stages = 3
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if results.get('river') and results['river'].get('success', False):
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passed_stages += 1
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if results.get('turn') and results['turn'].get('success', False):
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passed_stages += 1
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if results.get('flop') and results['flop'].get('success', False):
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passed_stages += 1
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overall_rate = passed_stages / total_stages
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# print(f"\n 总体验证通过率: {passed_stages}/{total_stages} ({overall_rate:.1%})")
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# if overall_rate >= 0.7:
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# print(" 数据验证成功！短牌型生成器与解析数据基本一致。")
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# else:
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# print(" 验证存在问题，生成器可能与实际数据不匹配，需要调试。")
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return {
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'results': results,
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'passed_stages': passed_stages,
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'total_stages': total_stages,
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'overall_success': overall_rate >= 0.7
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}
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