ZMJ/test.py

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')

# ===== 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
    
    # 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

origin_train_df = pd.read_parquet(Config.TRAIN_PATH)
origin_test_df = pd.read_parquet(Config.TEST_PATH)
train_df = pd.read_parquet("/AI4M/users/mjzhang/workspace/DRW/ZMJ/threshold_6_29/train_final.parquet")
test_df = pd.read_parquet("/AI4M/users/mjzhang/workspace/DRW/ZMJ/threshold_6_29/test_final.parquet")
breakpoint()