import pandas as pd import numpy as np from scipy.stats import pearsonr import warnings from concurrent.futures import ThreadPoolExecutor, as_completed import time warnings.filterwarnings('ignore') # ===== Configuration ===== class Config: # 数据路径配置 TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/train.parquet" TEST_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/test.parquet" # 如果使用聚合后的数据 AGGREGATED_TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/train_aggregated.parquet" AGGREGATED_TEST_PATH = "/AI4M/users/mjzhang/workspace/DRW/ZMJ/max_IC_mixed/test_aggregated.parquet" LABEL_COLUMN = "label" # 性能配置 MAX_WORKERS = 4 # 并行计算的工作线程数 USE_AGGREGATED_DATA = True # 是否使用聚合后的数据 # 输出配置 OUTPUT_DIR = "./ic_analysis_results" SAVE_DETAILED_RESULTS = True # 是否保存详细结果 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, p_value = pearsonr(df[feature], df[label_col]) return feature, ic, p_value except Exception as e: print(f"计算特征 {feature} 的IC值时出错: {e}") return feature, 0.0, 1.0 # 并行计算IC值 ic_dict = {} p_value_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, p_value = future.result() ic_dict[feature] = ic p_value_dict[feature] = p_value 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) p_values = pd.Series(p_value_dict) print(f"IC值计算耗时: {time.time() - start_time:.2f}秒") return ic_values, p_values def calculate_feature_statistics(df, features, label_col): """ 计算特征的统计信息 Parameters: ----------- df : pd.DataFrame 数据框 features : list 特征列表 label_col : str 标签列名 Returns: -------- stats_df : pd.DataFrame 特征统计信息 """ print("计算特征统计信息...") stats_data = [] for feature in features: try: feature_data = df[feature] label_data = df[label_col] # 基本统计 mean_val = feature_data.mean() std_val = feature_data.std() min_val = feature_data.min() max_val = feature_data.max() # 缺失值统计 missing_count = feature_data.isna().sum() missing_ratio = missing_count / len(feature_data) # 零值统计 zero_count = (feature_data == 0).sum() zero_ratio = zero_count / len(feature_data) # 异常值统计（超过3个标准差） outlier_count = ((feature_data - mean_val).abs() > 3 * std_val).sum() outlier_ratio = outlier_count / len(feature_data) stats_data.append({ 'feature': feature, 'mean': mean_val, 'std': std_val, 'min': min_val, 'max': max_val, 'missing_count': missing_count, 'missing_ratio': missing_ratio, 'zero_count': zero_count, 'zero_ratio': zero_ratio, 'outlier_count': outlier_count, 'outlier_ratio': outlier_ratio }) except Exception as e: print(f"计算特征 {feature} 统计信息时出错: {e}") stats_data.append({ 'feature': feature, 'mean': np.nan, 'std': np.nan, 'min': np.nan, 'max': np.nan, 'missing_count': np.nan, 'missing_ratio': np.nan, 'zero_count': np.nan, 'zero_ratio': np.nan, 'outlier_count': np.nan, 'outlier_ratio': np.nan }) return pd.DataFrame(stats_data) def create_ic_analysis_report(ic_values, p_values, stats_df, output_dir): """ 创建IC分析报告 Parameters: ----------- ic_values : pd.Series IC值 p_values : pd.Series P值 stats_df : pd.DataFrame 统计信息 output_dir : str 输出目录 """ print("创建IC分析报告...") # 创建输出目录 import os os.makedirs(output_dir, exist_ok=True) # 1. 合并所有信息 report_df = pd.DataFrame({ 'feature': ic_values.index, 'ic_value': ic_values.values, 'ic_abs': ic_values.abs().values, 'p_value': p_values.values, 'is_significant': p_values < 0.05 }) # 添加统计信息 report_df = report_df.merge(stats_df, on='feature', how='left') # 2. 按IC绝对值排序 report_df = report_df.sort_values('ic_abs', ascending=False) # 3. 添加排名 report_df['ic_rank'] = report_df['ic_abs'].rank(ascending=False, method='min') # 4. 保存详细报告 if Config.SAVE_DETAILED_RESULTS: detailed_path = os.path.join(output_dir, 'detailed_ic_analysis.csv') report_df.to_csv(detailed_path, index=False) print(f"详细IC分析报告已保存: {detailed_path}") # 5. 保存简化报告（只包含重要信息） simple_df = report_df[['feature', 'ic_value', 'ic_abs', 'ic_rank', 'p_value', 'is_significant']].copy() simple_path = os.path.join(output_dir, 'ic_analysis_summary.csv') simple_df.to_csv(simple_path, index=False) print(f"IC分析摘要已保存: {simple_path}") # 6. 保存统计信息 stats_path = os.path.join(output_dir, 'feature_statistics.csv') stats_df.to_csv(stats_path, index=False) print(f"特征统计信息已保存: {stats_path}") # 7. 打印摘要信息 print("\n" + "="*60) print("IC分析摘要") print("="*60) print(f"总特征数量: {len(ic_values)}") print(f"平均IC值: {ic_values.mean():.4f}") print(f"IC值标准差: {ic_values.std():.4f}") print(f"最大IC值: {ic_values.max():.4f}") print(f"最小IC值: {ic_values.min():.4f}") print(f"显著特征数量 (p < 0.05): {(p_values < 0.05).sum()}") print(f"正IC值特征数量: {(ic_values > 0).sum()}") print(f"负IC值特征数量: {(ic_values < 0).sum()}") print(f"\nTop 10 最高IC值特征:") top_10 = report_df.head(10) for _, row in top_10.iterrows(): significance = "***" if row['is_significant'] else "" print(f" {row['ic_rank']:2.0f}. {row['feature']:20s} IC={row['ic_value']:6.4f} (p={row['p_value']:.4f}) {significance}") print(f"\nBottom 10 最低IC值特征:") bottom_10 = report_df.tail(10) for _, row in bottom_10.iterrows(): significance = "***" if row['is_significant'] else "" print(f" {row['ic_rank']:2.0f}. {row['feature']:20s} IC={row['ic_value']:6.4f} (p={row['p_value']:.4f}) {significance}") return report_df def main(): """主函数""" print("="*60) print("开始IC值分析") print("="*60) # 1. 加载数据 print("\n1. 加载数据...") if Config.USE_AGGREGATED_DATA: try: train_df = pd.read_parquet(Config.AGGREGATED_TRAIN_PATH) print(f"使用聚合后的训练数据: {train_df.shape}") except FileNotFoundError: print("聚合数据文件不存在，使用原始数据...") train_df = pd.read_parquet(Config.TRAIN_PATH) print(f"使用原始训练数据: {train_df.shape}") else: train_df = pd.read_parquet(Config.TRAIN_PATH) print(f"使用原始训练数据: {train_df.shape}") # 2. 获取特征列表 print("\n2. 获取特征列表...") features = [col for col in train_df.columns if col != Config.LABEL_COLUMN] print(f"特征数量: {len(features)}") # 3. 数据预处理 print("\n3. 数据预处理...") # 处理缺失值 for col in features + [Config.LABEL_COLUMN]: if train_df[col].isna().any(): median_val = train_df[col].median() train_df[col] = train_df[col].fillna(median_val if not pd.isna(median_val) else 0) # 处理无穷值 train_df = train_df.replace([np.inf, -np.inf], np.nan) for col in features + [Config.LABEL_COLUMN]: if train_df[col].isna().any(): median_val = train_df[col].median() train_df[col] = train_df[col].fillna(median_val if not pd.isna(median_val) else 0) print(f"预处理后数据形状: {train_df.shape}") # 4. 计算IC值 print("\n4. 计算IC值...") ic_values, p_values = fast_ic_calculation(train_df, features, Config.LABEL_COLUMN, Config.MAX_WORKERS) # 5. 计算特征统计信息 print("\n5. 计算特征统计信息...") stats_df = calculate_feature_statistics(train_df, features, Config.LABEL_COLUMN) # 6. 创建分析报告 print("\n6. 创建分析报告...") report_df = create_ic_analysis_report(ic_values, p_values, stats_df, Config.OUTPUT_DIR) # 7. 保存原始IC值 print("\n7. 保存原始IC值...") ic_df = pd.DataFrame({ 'feature': ic_values.index, 'ic_value': ic_values.values, 'p_value': p_values.values }) ic_path = f"{Config.OUTPUT_DIR}/ic_values.csv" ic_df.to_csv(ic_path, index=False) print(f"IC值已保存: {ic_path}") print("\n" + "="*60) print("IC值分析完成！") print("="*60) print(f"所有结果已保存到目录: {Config.OUTPUT_DIR}") print("生成的文件:") print("- ic_values.csv: 原始IC值") print("- ic_analysis_summary.csv: IC分析摘要") print("- detailed_ic_analysis.csv: 详细IC分析报告") print("- feature_statistics.csv: 特征统计信息") if __name__ == "__main__": main()