import sys import pandas as pd import numpy as np from sklearn.model_selection import KFold from xgboost import XGBRegressor from lightgbm import LGBMRegressor from sklearn.linear_model import ( HuberRegressor, RANSACRegressor, TheilSenRegressor, Lasso, ElasticNet, Ridge ) from sklearn.cross_decomposition import PLSRegression from sklearn.preprocessing import StandardScaler, RobustScaler from sklearn.ensemble import RandomForestRegressor from scipy.stats import pearsonr import warnings import torch import matplotlib.pyplot as plt import seaborn as sns from concurrent.futures import ThreadPoolExecutor, as_completed from itertools import combinations import time warnings.filterwarnings('ignore') # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False # ===== Configuration ===== class Config: TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/train.parquet" TEST_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/test.parquet" # SUBMISSION_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/sample_submission_zmj.csv" # Original features plus additional market features FEATURES = [ "X863", "X856", "X598", "X862", "X385", "X852", "X603", "X860", "X674", "X415", "X345", "X855", "X174", "X302", "X178", "X168", "X612", "buy_qty", "sell_qty", "volume", "X888", "X421", "X333", "bid_qty", "ask_qty" ] LABEL_COLUMN = "label" N_FOLDS = 3 RANDOM_STATE = 42 # 相关系数分析配置 CORRELATION_THRESHOLD = 0.8 # 相关系数阈值，大于此值的因子将被聚合 IC_WEIGHT_METHOD = "abs" # IC权重计算方法: "abs", "square", "rank" SAVE_RESULTS = True # 是否保存分析结果 CREATE_VISUALIZATIONS = True # 是否创建可视化图表 REMOVE_ORIGINAL_FEATURES = True # 是否删除原始特征 # 特征筛选配置 IC_THRESHOLD = 0.04 # IC值阈值，只保留IC值>=此值的聚合特征 # 性能优化配置 MAX_WORKERS = 4 # 并行计算的工作线程数 USE_SAMPLING = False # 大数据集是否使用采样计算 SAMPLE_SIZE = 10000 # 采样大小 USE_GPU = True # 是否使用GPU加速（需要PyTorch） USE_MATRIX_MULTIPLICATION = True # 是否使用矩阵乘法优化 # ===== Feature Engineering ===== def feature_engineering(df): """Original features plus new robust features""" # Original features df['volume_weighted_sell'] = df['sell_qty'] * df['volume'] df['buy_sell_ratio'] = df['buy_qty'] / (df['sell_qty'] + 1e-8) df['selling_pressure'] = df['sell_qty'] / (df['volume'] + 1e-8) df['effective_spread_proxy'] = np.abs(df['buy_qty'] - df['sell_qty']) / (df['volume'] + 1e-8) # New robust features df['log_volume'] = np.log1p(df['volume']) df['bid_ask_imbalance'] = (df['bid_qty'] - df['ask_qty']) / (df['bid_qty'] + df['ask_qty'] + 1e-8) df['order_flow_imbalance'] = (df['buy_qty'] - df['sell_qty']) / (df['buy_qty'] + df['sell_qty'] + 1e-8) df['liquidity_ratio'] = (df['bid_qty'] + df['ask_qty']) / (df['volume'] + 1e-8) # Handle infinities and NaN df = df.replace([np.inf, -np.inf], np.nan) # For each column, replace NaN with median for robustness for col in df.columns: if df[col].isna().any(): median_val = df[col].median() df[col] = df[col].fillna(median_val if not pd.isna(median_val) else 0) return df train_df = pd.read_parquet(Config.TRAIN_PATH) test_df = pd.read_parquet(Config.TEST_PATH) train_df = feature_engineering(train_df) # ===== 相关系数矩阵计算和因子聚合 ===== def calculate_correlation_matrix_and_ic(train_df, features, label_col='label', correlation_threshold=0.8, max_workers=4, test_df=None): """ 计算特征间的相关系数矩阵和每个特征与标签的IC值（优化版本） 并对IC为负的特征先取反，使所有IC为正 """ # 确保特征列存在 available_features = [f for f in features if f in train_df.columns] print(f"可用特征数量: {len(available_features)}") # 1. 先计算IC值 ic_values = fast_ic_calculation(train_df, available_features, label_col, max_workers=max_workers) print("初始IC统计：") print(ic_values.describe()) # 2. 对IC为负的特征取反 neg_ic_features = ic_values[ic_values < 0].index.tolist() print(f"IC为负的特征数量: {len(neg_ic_features)}") for f in neg_ic_features: train_df[f] = -train_df[f] if test_df is not None and f in test_df.columns: test_df[f] = -test_df[f] # 3. 重新计算IC值（此时全为正） ic_values = fast_ic_calculation(train_df, available_features, label_col, max_workers=max_workers) print("IC取正后统计：") print(ic_values.describe()) # 4. 计算相关系数矩阵 corr_matrix = fast_correlation_matrix(train_df, available_features, method='pearson', max_workers=max_workers) # 5. 聚合 feature_groups = aggregate_correlated_features( corr_matrix, ic_values, correlation_threshold ) return corr_matrix, ic_values, feature_groups, train_df, test_df def aggregate_correlated_features(corr_matrix, ic_values, threshold=0.8): """ 基于相关系数和IC值对高相关因子进行聚合 Parameters: ----------- corr_matrix : pd.DataFrame 相关系数矩阵 ic_values : pd.Series 每个特征的IC值 threshold : float 相关系数阈值 Returns: -------- feature_groups : list 聚合后的特征组，每个组包含特征名和聚合权重 """ features = list(corr_matrix.columns) used_features = set() feature_groups = [] # 按IC值绝对值排序，优先选择IC值高的特征作为代表 ic_abs = ic_values.abs().sort_values(ascending=False) for feature in ic_abs.index: if feature in used_features: continue # 找到与当前特征高度相关的其他特征 correlated_features = [] for other_feature in features: if other_feature != feature and other_feature not in used_features: corr_value = abs(corr_matrix.loc[feature, other_feature]) if corr_value > threshold: correlated_features.append(other_feature) if correlated_features: # 创建特征组，包含主特征和相关特征 group_features = [feature] + correlated_features used_features.update(group_features) # 计算基于IC值的权重 group_ic_values = ic_values[group_features] weights = calculate_ic_weighted_weights(group_ic_values, Config.IC_WEIGHT_METHOD) feature_groups.append({ 'features': group_features, 'weights': weights, 'representative': feature, 'group_ic': group_ic_values.mean() }) print(f"特征组 {len(feature_groups)}: {feature} (IC={ic_values[feature]:.4f}) " f"与 {len(correlated_features)} 个特征聚合") print(f"组{len(feature_groups) - 1} 权重: {weights}, 特征: {group_features}") # if len(features) == 1: # print(f"单特征组: {features[0]}, 权重: {weights[0]}, 非零样本数: {(df[features[0]] != 0).sum()}") else: # 单独的特征 used_features.add(feature) feature_groups.append({ 'features': [feature], 'weights': [1.0], 'representative': feature, 'group_ic': ic_values[feature] }) return feature_groups def calculate_ic_weighted_weights(ic_values, method="abs"): """ 基于IC值计算特征权重 Parameters: ----------- ic_values : pd.Series 特征IC值 method : str 权重计算方法: "abs", "square", "rank" Returns: -------- weights : list 归一化的权重列表 """ if method == "abs": # 使用IC值的绝对值作为权重基础 weights_base = ic_values.abs() elif method == "square": # 使用IC值的平方作为权重基础 weights_base = ic_values ** 2 elif method == "rank": # 使用IC值排名作为权重基础 weights_base = ic_values.abs().rank(ascending=False) else: raise ValueError(f"不支持的权重计算方法: {method}") # 避免零权重 weights_base = weights_base + 1e-8 # 归一化权重 weights = weights_base / weights_base.sum() return weights.tolist() def calculate_optimal_ic_weights(df, features, label_col): """ 对于给定特征组，使用最大化IC合成法计算最优权重。 参数： df: pd.DataFrame，包含特征和标签 features: list，特征名 label_col: str，标签名 返回： weights: list，归一化权重 """ if len(features) == 1: return [1.0] Z = df[features].values Z = (Z - Z.mean(axis=0)) / (Z.std(axis=0) + 1e-8) # 标准化 R = df[label_col].values.reshape(-1, 1) # 协方差矩阵 cov_ZZ = np.cov(Z, rowvar=False) cov_ZR = np.cov(Z, R, rowvar=False)[:-1, -1] # 防止协方差矩阵奇异，加微小正则项 cov_ZZ += np.eye(cov_ZZ.shape[0]) * 1e-6 # 求解最优权重 try: w = np.linalg.solve(cov_ZZ, cov_ZR) except np.linalg.LinAlgError: w = np.linalg.lstsq(cov_ZZ, cov_ZR, rcond=None)[0] # 归一化（L1范数） if np.sum(np.abs(w)) > 1e-8: w = w / np.sum(np.abs(w)) else: w = np.ones_like(w) / len(w) return w.tolist() def create_aggregated_features(df, feature_groups, remove_original=True, label_col=None): """ 基于特征组创建聚合特征（只用最大化IC合成法计算权重，并输出与IC加权对比） """ aggregated_df = df.copy() aggregated_original_features = set() if label_col is None: label_col = Config.LABEL_COLUMN for i, group in enumerate(feature_groups): features = group['features'] representative = group['representative'] # 检查所有特征是否都存在 missing_features = [f for f in features if f not in df.columns] if missing_features: print(f"警告: 特征组 {i} 中缺少特征: {missing_features}") continue # 最大化IC合成法权重 weights = calculate_optimal_ic_weights(df, features, label_col) # IC加权（abs），每个特征和标签单独算皮尔逊相关系数 ic_vec = [] for f in features: try: ic = np.corrcoef(df[f], df[label_col])[0, 1] except Exception: ic = 0.0 ic_vec.append(ic) ic_weights = calculate_ic_weighted_weights(pd.Series(ic_vec, index=features), method='abs') print(f"组{i} features: {features}") print(f" 最大化IC权重: {weights}") print(f" IC加权权重: {ic_weights}") if len(features) == 1: agg_feature = df[features[0]] * weights[0] else: agg_feature = sum(df[features[j]] * weights[j] for j in range(len(features))) agg_feature_name = f"agg_group_{i}_{representative}" aggregated_df[agg_feature_name] = agg_feature print(f"创建聚合特征: {agg_feature_name} (包含 {len(features)} 个原始特征)") aggregated_original_features.update(features) # 删除原始特征 if remove_original: features_to_remove = [f for f in aggregated_original_features if f in aggregated_df.columns] if features_to_remove: aggregated_df = aggregated_df.drop(columns=features_to_remove) print(f"删除了 {len(features_to_remove)} 个原始特征: {features_to_remove}") else: print("没有需要删除的原始特征") return aggregated_df def calculate_aggregated_features_ic_and_filter(df_aggregated, feature_groups, config_features, ic_threshold=0.04): """ 计算聚合特征的IC值，并基于IC值进行筛选，同时处理与Config.FEATURES的去重逻辑 Parameters: ----------- df_aggregated : pd.DataFrame 聚合后的数据框 feature_groups : list 特征组列表 config_features : list Config.FEATURES中的特征列表 ic_threshold : float IC值阈值 Returns: -------- df_final : pd.DataFrame 最终筛选后的数据框 selected_features : list 最终选择的特征列表 aggregated_ic_values : pd.Series 聚合特征的IC值 """ print(f"\n开始计算聚合特征IC值并筛选 (IC阈值: {ic_threshold})...") # 1. 获取所有聚合特征名称 aggregated_feature_names = [] for i, group in enumerate(feature_groups): representative = group['representative'] agg_feature_name = f"agg_group_{i}_{representative}" if agg_feature_name in df_aggregated.columns: aggregated_feature_names.append(agg_feature_name) print(f"聚合特征数量: {len(aggregated_feature_names)}") # 2. 计算聚合特征的IC值 print("计算聚合特征的IC值...") aggregated_ic_values = fast_ic_calculation( df_aggregated, aggregated_feature_names, Config.LABEL_COLUMN, Config.MAX_WORKERS ) print("聚合特征IC值统计:") print(f" 平均IC值: {aggregated_ic_values.mean():.4f}") print(f" 最大IC值: {aggregated_ic_values.max():.4f}") print(f" 最小IC值: {aggregated_ic_values.min():.4f}") print(f" IC值>=阈值的特征数量: {(aggregated_ic_values >= ic_threshold).sum()}") # 3. 筛选IC值>=阈值的聚合特征 high_ic_aggregated = aggregated_ic_values[aggregated_ic_values >= ic_threshold].index.tolist() print(f"IC值>=阈值的聚合特征数量: {len(high_ic_aggregated)}") # 4. 处理Config.FEATURES中的特征 config_features_available = [] for feature in config_features: if feature in df_aggregated.columns: config_features_available.append(feature) else: print(f"警告: Config.FEATURES中的特征 {feature} 不在聚合数据中") print(f"Config.FEATURES中可用的特征数量: {len(config_features_available)}") # 5. 去重逻辑：处理单因子聚合与Config.FEATURES的冲突 final_features = [] conflicts_resolved = [] # 先添加高IC值的聚合特征 for agg_feature in high_ic_aggregated: # 检查是否是单因子聚合 group_idx = int(agg_feature.split('_')[2]) # 提取组索引 group = feature_groups[group_idx] if len(group['features']) == 1: # 单因子聚合，检查是否与Config.FEATURES冲突 original_feature = group['features'][0] if original_feature in config_features_available: # 冲突：优先保留Config.FEATURES中的特征 conflicts_resolved.append({ 'aggregated_feature': agg_feature, 'original_feature': original_feature, 'aggregated_ic': aggregated_ic_values[agg_feature], 'reason': 'Single factor aggregation conflicts with Config.FEATURES' }) print(f"去重: 单因子聚合 {agg_feature} (IC={aggregated_ic_values[agg_feature]:.4f}) 与Config.FEATURES中的 {original_feature} 冲突，保留原始特征") continue final_features.append(agg_feature) # 再添加Config.FEATURES中的特征 for config_feature in config_features_available: # 检查是否已经被聚合特征包含（通过去重逻辑） is_conflict = False for conflict in conflicts_resolved: if conflict['original_feature'] == config_feature: is_conflict = True break if not is_conflict: final_features.append(config_feature) # 6. 确保label列存在 if Config.LABEL_COLUMN in df_aggregated.columns: final_features.append(Config.LABEL_COLUMN) # 7. 筛选最终数据框 available_features = [f for f in final_features if f in df_aggregated.columns] df_final = df_aggregated[available_features].copy() # 8. 打印筛选结果 print(f"\n特征筛选结果:") print(f" 原始聚合特征数量: {len(aggregated_feature_names)}") print(f" 高IC值聚合特征数量: {len(high_ic_aggregated)}") print(f" Config.FEATURES特征数量: {len(config_features_available)}") print(f" 冲突解决数量: {len(conflicts_resolved)}") print(f" 最终特征数量: {len(available_features)}") if conflicts_resolved: print(f"\n冲突解决详情:") for conflict in conflicts_resolved: print(f" - {conflict['aggregated_feature']} (IC={conflict['aggregated_ic']:.4f}) -> {conflict['original_feature']}: {conflict['reason']}") # 9. 显示最终特征列表 final_feature_names = [f for f in available_features if f != Config.LABEL_COLUMN] print(f"\n最终选择的特征 ({len(final_feature_names)} 个):") for i, feature in enumerate(final_feature_names, 1): if feature.startswith('agg_group_'): ic_val = aggregated_ic_values[feature] print(f" {i:3d}. {feature:30s} (IC={ic_val:.4f})") else: print(f" {i:3d}. {feature:30s} (Config.FEATURES)") return df_final, final_feature_names, aggregated_ic_values # ===== 可视化函数 ===== def visualize_correlation_and_ic(corr_matrix, ic_values, feature_groups, save_plots=True): """ Visualize correlation matrix, IC distribution, and feature aggregation results (English version) """ fig, axes = plt.subplots(2, 2, figsize=(20, 16)) fig.suptitle('Feature Correlation Analysis and IC Distribution', fontsize=16, fontweight='bold') # 1. Correlation matrix heatmap mask = np.triu(np.ones_like(corr_matrix, dtype=bool)) sns.heatmap(corr_matrix, mask=mask, annot=False, cmap='RdBu_r', center=0, square=True, linewidths=0.5, cbar_kws={"shrink": .8}, ax=axes[0,0]) axes[0,0].set_title('Feature Correlation Matrix', fontsize=14, fontweight='bold') # 2. IC distribution histogram axes[0,1].hist(ic_values.values, bins=30, alpha=0.7, color='skyblue', edgecolor='black') axes[0,1].axvline(ic_values.mean(), color='red', linestyle='--', label=f'Mean: {ic_values.mean():.4f}') axes[0,1].axvline(0, color='green', linestyle='-', alpha=0.5, label='IC=0') axes[0,1].set_xlabel('IC Value') axes[0,1].set_ylabel('Frequency') axes[0,1].set_title('Feature IC Value Distribution', fontsize=14, fontweight='bold') axes[0,1].legend() axes[0,1].grid(True, alpha=0.3) # 3. Top 20 highest IC features top_ic_features = ic_values.abs().sort_values(ascending=False).head(20) colors = ['red' if ic_values[feature] < 0 else 'blue' for feature in top_ic_features.index] axes[1,0].barh(range(len(top_ic_features)), top_ic_features.values, color=colors, alpha=0.7) axes[1,0].set_yticks(range(len(top_ic_features))) axes[1,0].set_yticklabels(top_ic_features.index, fontsize=8) axes[1,0].set_xlabel('|IC Value|') axes[1,0].set_title('Top 20 |IC Value| Features', fontsize=14, fontweight='bold') axes[1,0].grid(True, alpha=0.3) # 4. Feature aggregation results group_sizes = [len(group['features']) for group in feature_groups] group_ics = [group['group_ic'] for group in feature_groups] single_features = [i for i, size in enumerate(group_sizes) if size == 1] grouped_features = [i for i, size in enumerate(group_sizes) if size > 1] if single_features: axes[1,1].scatter([group_sizes[i] for i in single_features], [group_ics[i] for i in single_features], alpha=0.6, label='Single Feature', s=50) if grouped_features: axes[1,1].scatter([group_sizes[i] for i in grouped_features], [group_ics[i] for i in grouped_features], alpha=0.8, label='Aggregated Feature', s=100, color='red') axes[1,1].set_xlabel('Feature Group Size') axes[1,1].set_ylabel('Group Mean IC Value') axes[1,1].set_title('Feature Aggregation Result', fontsize=14, fontweight='bold') axes[1,1].legend() axes[1,1].grid(True, alpha=0.3) plt.tight_layout() if save_plots: plt.savefig('./threshold_6_29/feature_analysis.png', dpi=300, bbox_inches='tight') print("Saved feature analysis image: feature_analysis.png") plt.show() def create_feature_summary_report(corr_matrix, ic_values, feature_groups, selected_features=None): """ 创建特征分析报告 Parameters: ----------- corr_matrix : pd.DataFrame 相关系数矩阵 ic_values : pd.Series 特征IC值 feature_groups : list 特征组列表 selected_features : list, optional 最终选择的特征列表 """ report = [] report.append("=" * 60) report.append("Feature Analysis Report") report.append("=" * 60) # 基本统计 report.append(f"\n1. Basic Statistical Information:") report.append(f" Total Feature Count: {len(ic_values)}") report.append(f" Average IC Value: {ic_values.mean():.4f}") report.append(f" IC Value Standard Deviation: {ic_values.std():.4f}") report.append(f" Maximum IC Value: {ic_values.max():.4f}") report.append(f" Minimum IC Value: {ic_values.min():.4f}") report.append(f" Positive IC Value Feature Count: {(ic_values > 0).sum()}") report.append(f" Negative IC Value Feature Count: {(ic_values < 0).sum()}") # 高相关性分析 high_corr_count = 0 for i in range(len(corr_matrix.columns)): for j in range(i+1, len(corr_matrix.columns)): if abs(corr_matrix.iloc[i, j]) > Config.CORRELATION_THRESHOLD: high_corr_count += 1 report.append(f"\n2. High Correlation Analysis (|Correlation| > {Config.CORRELATION_THRESHOLD}):") report.append(f" High Correlation Feature Pair Count: {high_corr_count}") report.append(f" Correlation Matrix Density: {high_corr_count / (len(corr_matrix) * (len(corr_matrix) - 1) / 2):.4f}") # 特征聚合结果 report.append(f"\n3. Feature Aggregation Results:") report.append(f" Feature Group Count: {len(feature_groups)}") single_features = [g for g in feature_groups if len(g['features']) == 1] grouped_features = [g for g in feature_groups if len(g['features']) > 1] report.append(f" Single Feature Group Count: {len(single_features)}") report.append(f" Aggregated Feature Group Count: {len(grouped_features)}") if grouped_features: avg_group_size = np.mean([len(g['features']) for g in grouped_features]) report.append(f" Average Aggregated Group Size: {avg_group_size:.2f}") # 特征筛选结果 if selected_features is not None: report.append(f"\n4. Feature Selection Results (IC Threshold: {Config.IC_THRESHOLD}):") report.append(f" Final Selected Feature Count: {len(selected_features)}") # 统计聚合特征和Config.FEATURES的数量 aggregated_count = sum(1 for f in selected_features if f.startswith('agg_group_')) config_count = sum(1 for f in selected_features if not f.startswith('agg_group_')) report.append(f" Aggregated Features in Final Selection: {aggregated_count}") report.append(f" Config.FEATURES in Final Selection: {config_count}") # 显示最终选择的聚合特征 final_aggregated = [f for f in selected_features if f.startswith('agg_group_')] if final_aggregated: report.append(f"\n5. Final Selected Aggregated Features:") for i, feature in enumerate(final_aggregated, 1): group_idx = int(feature.split('_')[2]) group = feature_groups[group_idx] report.append(f" {i:2d}. {feature:25s} (Group IC={group['group_ic']:.4f})") # 前10个最高IC值特征 report.append(f"\n6. Top 10 Highest IC Value Features:") top_ic = ic_values.abs().sort_values(ascending=False).head(10) for i, (feature, ic_abs) in enumerate(top_ic.items(), 1): ic_original = ic_values[feature] report.append(f" {i:2d}. {feature:20s} |IC|={ic_abs:.4f} (IC={ic_original:.4f})") # 特征聚合详情 report.append(f"\n7. Feature Aggregation Details:") for i, group in enumerate(grouped_features, 1): report.append(f" Group {i}: {group['representative']} (IC={group['group_ic']:.4f})") report.append(f" Contains Features: {', '.join(group['features'])}") report.append(f" Weights: {[f'{w:.3f}' for w in group['weights']]}") # 保存报告 with open('./threshold_6_29/feature_analysis_report.txt', 'w', encoding='utf-8') as f: f.write('\n'.join(report)) print('\n'.join(report)) print(f"\nReport Saved to: feature_analysis_report.txt") # ===== 优化的相关系数计算 ===== def fast_correlation_matrix(df, features, method='pearson', max_workers=4): """ 快速计算相关系数矩阵，支持并行计算和多种优化策略 Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 method : str 相关系数计算方法: 'pearson', 'spearman' max_workers : int 并行计算的工作线程数 Returns: -------- corr_matrix : pd.DataFrame 相关系数矩阵 """ print(f"开始计算相关系数矩阵 (特征数量: {len(features)}, 方法: {method})") start_time = time.time() # 方法1: 使用矩阵乘法优化（最快） if method == 'pearson' and Config.USE_MATRIX_MULTIPLICATION: if Config.USE_GPU and torch.cuda.is_available(): corr_matrix = torch_correlation(df, features, use_gpu=True) print(f"GPU矩阵乘法优化耗时: {time.time() - start_time:.2f}秒") else: corr_matrix = matrix_correlation(df, features) print(f"CPU矩阵乘法优化耗时: {time.time() - start_time:.2f}秒") return corr_matrix # 方法2: 对于大数据集，使用采样计算 if Config.USE_SAMPLING and len(df) > Config.SAMPLE_SIZE: print(f"数据量较大，使用采样计算 (采样大小: {Config.SAMPLE_SIZE})...") sample_size = min(Config.SAMPLE_SIZE, len(df)) sample_df = df.sample(n=sample_size, random_state=42) feature_data = sample_df[features] corr_matrix = feature_data.corr(method=method) print(f"采样计算耗时: {time.time() - start_time:.2f}秒") return corr_matrix # 方法3: 并行计算（适用于中等规模数据） else: print(f"使用并行计算 (线程数: {max_workers})...") return parallel_correlation_matrix(df, features, method, max_workers) def matrix_correlation(df, features): """ 使用矩阵乘法计算相关系数矩阵 (A * A^T 方法) Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 Returns: -------- corr_matrix : pd.DataFrame 相关系数矩阵 """ # 提取特征数据 feature_data = df[features].values # 标准化数据 (z-score) feature_data_std = (feature_data - feature_data.mean(axis=0)) / feature_data.std(axis=0) # 处理NaN值 feature_data_std = np.nan_to_num(feature_data_std, nan=0.0) # 计算相关系数矩阵: (A * A^T) / (n-1) n = feature_data_std.shape[0] corr_matrix_np = np.dot(feature_data_std.T, feature_data_std) / (n - 1) # 确保对角线为1 np.fill_diagonal(corr_matrix_np, 1.0) # 转换为DataFrame corr_matrix = pd.DataFrame(corr_matrix_np, index=features, columns=features) return corr_matrix def torch_correlation(df, features, use_gpu=False): """ 使用PyTorch张量计算相关系数矩阵（可选GPU加速） Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 use_gpu : bool 是否使用GPU加速 Returns: -------- corr_matrix : pd.DataFrame 相关系数矩阵 """ # 提取特征数据 feature_data = df[features].values # 转换为PyTorch张量 if use_gpu and torch.cuda.is_available(): device = torch.device('cuda') print("使用GPU加速计算...") else: device = torch.device('cpu') print("使用CPU计算...") # 转换为张量并移动到设备 X = torch.tensor(feature_data, dtype=torch.float32, device=device) # 标准化数据 X_mean = torch.mean(X, dim=0, keepdim=True) X_std = torch.std(X, dim=0, keepdim=True, unbiased=True) X_std = torch.where(X_std == 0, torch.ones_like(X_std), X_std) # 避免除零 X_norm = (X - X_mean) / X_std # 处理NaN值 X_norm = torch.nan_to_num(X_norm, nan=0.0) # 计算相关系数矩阵: (X_norm^T * X_norm) / (n-1) n = X_norm.shape[0] corr_matrix_tensor = torch.mm(X_norm.T, X_norm) / (n - 1) # 确保对角线为1 torch.diagonal(corr_matrix_tensor)[:] = 1.0 # 移回CPU并转换为numpy corr_matrix_np = corr_matrix_tensor.cpu().numpy() # 转换为DataFrame corr_matrix = pd.DataFrame(corr_matrix_np, index=features, columns=features) return corr_matrix def parallel_correlation_matrix(df, features, method='pearson', max_workers=4): """ 并行计算相关系数矩阵 Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 method : str 相关系数计算方法 max_workers : int 并行计算的工作线程数 Returns: -------- corr_matrix : pd.DataFrame 相关系数矩阵 """ def calculate_correlation_pair(pair): """计算一对特征的相关系数""" feat1, feat2 = pair if method == 'pearson': corr, _ = pearsonr(df[feat1], df[feat2]) else: # spearman corr = df[feat1].corr(df[feat2], method='spearman') return (feat1, feat2, corr) # 生成所有特征对 feature_pairs = list(combinations(features, 2)) print(f"需要计算 {len(feature_pairs)} 个特征对的相关系数") # 并行计算 results = {} with ThreadPoolExecutor(max_workers=max_workers) as executor: future_to_pair = {executor.submit(calculate_correlation_pair, pair): pair for pair in feature_pairs} completed = 0 for future in as_completed(future_to_pair): feat1, feat2, corr = future.result() results[(feat1, feat2)] = corr results[(feat2, feat1)] = corr # 对称矩阵 completed += 1 if completed % 100 == 0: print(f"已完成: {completed}/{len(feature_pairs)} ({completed/len(feature_pairs)*100:.1f}%)") # 构建相关系数矩阵 corr_matrix = pd.DataFrame(index=features, columns=features) for feat1 in features: for feat2 in features: if feat1 == feat2: corr_matrix.loc[feat1, feat2] = 1.0 else: corr_matrix.loc[feat1, feat2] = results.get((feat1, feat2), 0.0) return corr_matrix def fast_ic_calculation(df, features, label_col, max_workers=4): """ 快速计算特征IC值，支持并行计算 Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 label_col : str 标签列名 max_workers : int 并行计算的工作线程数 Returns: -------- ic_values : pd.Series 特征IC值 """ print(f"开始计算特征IC值 (特征数量: {len(features)})") start_time = time.time() def calculate_ic(feature): """计算单个特征的IC值""" try: ic, _ = pearsonr(df[feature], df[label_col]) return feature, ic except: return feature, 0.0 # 并行计算IC值 ic_dict = {} with ThreadPoolExecutor(max_workers=max_workers) as executor: future_to_feature = {executor.submit(calculate_ic, feature): feature for feature in features} completed = 0 for future in as_completed(future_to_feature): feature, ic = future.result() ic_dict[feature] = ic completed += 1 if completed % 50 == 0: print(f"IC计算进度: {completed}/{len(features)} ({completed/len(features)*100:.1f}%)") ic_values = pd.Series(ic_dict) print(f"IC值计算耗时: {time.time() - start_time:.2f}秒") return ic_values def benchmark_correlation_methods(df, features, sample_size=1000): """ 比较不同相关系数计算方法的性能 Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 sample_size : int 用于测试的样本大小 Returns: -------- results : dict 各方法的性能结果 """ print("=" * 60) print("相关系数计算方法性能比较") print("=" * 60) # 采样数据用于测试 if len(df) > sample_size: test_df = df.sample(n=sample_size, random_state=42) else: test_df = df test_features = features[:min(50, len(features))] # 限制特征数量用于测试 print(f"测试数据: {len(test_df)} 行, {len(test_features)} 个特征") results = {} # 方法1: pandas corr() print("\n1. 测试 pandas corr() 方法...") start_time = time.time() try: feature_data = test_df[test_features] corr_pandas = feature_data.corr() pandas_time = time.time() - start_time results['pandas_corr'] = {'time': pandas_time, 'success': True} print(f" 耗时: {pandas_time:.3f}秒") except Exception as e: results['pandas_corr'] = {'time': float('inf'), 'success': False, 'error': str(e)} print(f" 失败: {e}") # 方法2: 矩阵乘法 (CPU) print("\n2. 测试矩阵乘法 (CPU)...") start_time = time.time() try: corr_matrix = matrix_correlation(test_df, test_features) matrix_time = time.time() - start_time results['matrix_cpu'] = {'time': matrix_time, 'success': True} print(f" 耗时: {matrix_time:.3f}秒") except Exception as e: results['matrix_cpu'] = {'time': float('inf'), 'success': False, 'error': str(e)} print(f" 失败: {e}") # 方法3: PyTorch (CPU) print("\n3. 测试 PyTorch (CPU)...") start_time = time.time() try: corr_torch_cpu = torch_correlation(test_df, test_features, use_gpu=False) torch_cpu_time = time.time() - start_time results['torch_cpu'] = {'time': torch_cpu_time, 'success': True} print(f" 耗时: {torch_cpu_time:.3f}秒") except Exception as e: results['torch_cpu'] = {'time': float('inf'), 'success': False, 'error': str(e)} print(f" 失败: {e}") # 方法4: PyTorch (GPU) if torch.cuda.is_available(): print("\n4. 测试 PyTorch (GPU)...") start_time = time.time() try: corr_torch_gpu = torch_correlation(test_df, test_features, use_gpu=True) torch_gpu_time = time.time() - start_time results['torch_gpu'] = {'time': torch_gpu_time, 'success': True} print(f" 耗时: {torch_gpu_time:.3f}秒") except Exception as e: results['torch_gpu'] = {'time': float('inf'), 'success': False, 'error': str(e)} print(f" 失败: {e}") else: print("\n4. GPU不可用，跳过GPU测试") results['torch_gpu'] = {'time': float('inf'), 'success': False, 'error': 'GPU not available'} # 方法5: 并行计算 print("\n5. 测试并行计算...") start_time = time.time() try: corr_parallel = parallel_correlation_matrix(test_df, test_features, method='pearson', max_workers=4) parallel_time = time.time() - start_time results['parallel'] = {'time': parallel_time, 'success': True} print(f" 耗时: {parallel_time:.3f}秒") except Exception as e: results['parallel'] = {'time': float('inf'), 'success': False, 'error': str(e)} print(f" 失败: {e}") # 显示比较结果 print(f"\n=== 性能比较结果 ===") successful_methods = {k: v for k, v in results.items() if v['success']} if successful_methods: fastest_method = min(successful_methods.items(), key=lambda x: x[1]['time']) print(f"最快方法: {fastest_method[0]} ({fastest_method[1]['time']:.3f}秒)") print(f"\n详细结果:") for method, result in sorted(successful_methods.items(), key=lambda x: x[1]['time']): speedup = fastest_method[1]['time'] / result['time'] print(f" {method:12s}: {result['time']:6.3f}秒 (相对速度: {speedup:.2f}x)") # 显示失败的方法 failed_methods = {k: v for k, v in results.items() if not v['success']} if failed_methods: print(f"\n失败的方法:") for method, result in failed_methods.items(): print(f" {method}: {result.get('error', 'Unknown error')}") return results if __name__ == "__main__": # ===== 主执行流程 ===== # 检查是否运行性能测试 if len(sys.argv) > 1 and sys.argv[1] == '--benchmark': print("=" * 60) print("运行相关系数计算方法性能测试") print("=" * 60) # 加载数据 train_df = pd.read_parquet(Config.TRAIN_PATH) all_features = [col for col in train_df.columns if col != Config.LABEL_COLUMN] # 运行性能测试 benchmark_correlation_methods(train_df, all_features) sys.exit(0) print("=" * 60) print("开始特征相关性分析和因子聚合") print("=" * 60) # 1. 加载数据 print("\n1. 加载数据...") train_df = pd.read_parquet(Config.TRAIN_PATH) test_df = pd.read_parquet(Config.TEST_PATH) print(f"训练数据形状: {train_df.shape}") print(f"测试数据形状: {test_df.shape}") # 2. 特征工程 print("\n2. 执行特征工程...") train_df = feature_engineering(train_df) test_df = feature_engineering(test_df) print(f"特征工程后训练数据形状: {train_df.shape}") print(f"特征工程后测试数据形状: {test_df.shape}") engineered_features = [ "volume_weighted_sell", "buy_sell_ratio", "selling_pressure", "effective_spread_proxy", "log_volume", "bid_ask_imbalance", "order_flow_imbalance", "liquidity_ratio" ] Config.FEATURES = list(set(Config.FEATURES + engineered_features)) # 2.5 剔除恒定特征 print("\n2.5 Remove constant features...") feature_cols = [col for col in train_df.columns if col != Config.LABEL_COLUMN] constant_features = [col for col in feature_cols if train_df[col].std() == 0] if constant_features: print(f"Remove {len(constant_features)} constant features: {constant_features}") train_df = train_df.drop(columns=constant_features) test_df = test_df.drop(columns=[col for col in constant_features if col in test_df.columns]) else: print("No constant features found.") # 3. 获取所有特征 all_features = [col for col in train_df.columns if col != Config.LABEL_COLUMN] print(f"\n特征数量: {len(all_features)}") # 4. 计算相关系数矩阵和IC值，并自动IC取正 print(f"\n3. 计算相关系数矩阵 (阈值: {Config.CORRELATION_THRESHOLD})...") corr_matrix, ic_values, feature_groups, train_df, test_df = calculate_correlation_matrix_and_ic( train_df, all_features, Config.LABEL_COLUMN, Config.CORRELATION_THRESHOLD, Config.MAX_WORKERS, test_df ) # 5. 显示基本统计 print(f"\n4. 基本统计信息:") print(f" 相关系数矩阵形状: {corr_matrix.shape}") print(f" 平均IC值: {ic_values.mean():.4f}") print(f" 最大IC值: {ic_values.max():.4f}") print(f" 最小IC值: {ic_values.min():.4f}") print(f" IC值标准差: {ic_values.std():.4f}") # 6. 显示特征聚合结果 print(f"\n5. 特征聚合结果:") print(f" 特征组数量: {len(feature_groups)}") single_features = [g for g in feature_groups if len(g['features']) == 1] grouped_features = [g for g in feature_groups if len(g['features']) > 1] print(f" 单独特征组: {len(single_features)}") print(f" 聚合特征组: {len(grouped_features)}") # 7. 创建聚合特征 print(f"\n6. 创建聚合特征...") train_df_aggregated = create_aggregated_features(train_df, feature_groups, Config.REMOVE_ORIGINAL_FEATURES) test_df_aggregated = create_aggregated_features(test_df, feature_groups, Config.REMOVE_ORIGINAL_FEATURES) print(f" 聚合前训练特征数量: {len(all_features)}") print(f" 聚合后训练特征数量: {len([col for col in train_df_aggregated.columns if col != Config.LABEL_COLUMN])}") print(f" 聚合后测试特征数量: {len([col for col in test_df_aggregated.columns])}") # 7.5. 计算聚合特征IC值并筛选 print(f"\n7. 计算聚合特征IC值并筛选...") train_df_final, selected_features, aggregated_ic_values = calculate_aggregated_features_ic_and_filter( train_df_aggregated, feature_groups, Config.FEATURES, Config.IC_THRESHOLD ) # 对测试数据应用相同的特征筛选 test_features_available = [f for f in selected_features if f in test_df_aggregated.columns] if Config.LABEL_COLUMN in test_df_aggregated.columns: test_features_available.append(Config.LABEL_COLUMN) test_df_final = test_df_aggregated[test_features_available].copy() print(f" 筛选后训练特征数量: {len([col for col in train_df_final.columns if col != Config.LABEL_COLUMN])}") print(f" 筛选后测试特征数量: {len([col for col in test_df_final.columns if col != Config.LABEL_COLUMN])}") # 8. 保存结果 if Config.SAVE_RESULTS: print(f"\n8. 保存结果...") corr_matrix.to_csv('./threshold_6_29/correlation_matrix.csv') ic_values.to_csv('./threshold_6_29/ic_values.csv') train_df_aggregated.to_parquet('./threshold_6_29/train_aggregated.parquet') test_df_aggregated.to_parquet('./threshold_6_29/test_aggregated.parquet') # 保存最终筛选后的数据 train_df_final.to_parquet('./threshold_6_29/train_final.parquet') test_df_final.to_parquet('./threshold_6_29/test_final.parquet') # 保存聚合特征的IC值 aggregated_ic_values.to_csv('./threshold_6_29/aggregated_ic_values.csv') print(" 相关系数矩阵已保存: correlation_matrix.csv") print(" 特征IC值已保存: ic_values.csv") print(" 聚合后训练数据已保存: train_aggregated.parquet") print(" 聚合后测试数据已保存: test_aggregated.parquet") print(" 最终筛选后训练数据已保存: train_final.parquet") print(" 最终筛选后测试数据已保存: test_final.parquet") print(" 聚合特征IC值已保存: aggregated_ic_values.csv") # 9. 显示高IC值特征 print(f"\n9. Top 10 highest IC features:") print(ic_values.abs().sort_values(ascending=False).head(10)) # 10. 显示高相关性特征对 print(f"\n10. Highly correlated feature pairs (|correlation| > {Config.CORRELATION_THRESHOLD}):") high_corr_pairs = [] for i in range(len(corr_matrix.columns)): for j in range(i+1, len(corr_matrix.columns)): corr_val = corr_matrix.iloc[i, j] if abs(corr_val) > Config.CORRELATION_THRESHOLD: high_corr_pairs.append((corr_matrix.columns[i], corr_matrix.columns[j], corr_val)) for pair in sorted(high_corr_pairs, key=lambda x: abs(x[2]), reverse=True)[:10]: print(f" {pair[0]} <-> {pair[1]}: {pair[2]:.4f}") # 11. 生成可视化 if Config.CREATE_VISUALIZATIONS: print(f"\n11. Generate visualization...") visualize_correlation_and_ic(corr_matrix, ic_values, feature_groups, Config.SAVE_RESULTS) # 12. 生成报告 if Config.SAVE_RESULTS: print(f"\n12. Generate feature analysis report...") create_feature_summary_report(corr_matrix, ic_values, feature_groups, selected_features) print(f"\n" + "=" * 60) print("Feature correlation analysis and factor aggregation completed!") print("=" * 60)