DRW/DRW-Crypto/optimize_params.py

# -*- coding: utf-8 -*-
# @Time : 2025/7
# @Author : Lukax
# @Email : Lukarxiang@gmail.com
# @File : optimize_params.py
# -*- presentd: PyCharm -*-


import os
import json
import argparse
import pandas as pd
from Utils import set_seed
from Settings import Config
from inplemental import load_data
from HyperparameterOptimizer import HyperparameterManager, quick_optimize_single_model



def parse_args():
    parser = argparse.ArgumentParser(description='超参数优化工具')
    
    parser.add_argument('--model', type = str, choices = ['xgb', 'lgb', 'cat', 'rf'], help = '选择要优化的模型')
    parser.add_argument('--all', action = 'store_true', help = '优化所有模型')
    parser.add_argument('--trials', type = int, default = 200, help = '搜参尝试次数')
    parser.add_argument('--cv-folds', type = int, default = 5, help = '交叉验证折数')
    parser.add_argument('--sample-ratio', type = float, default = None, help = '数据采样比例，用于快速测试 (默认全量)')
    parser.add_argument('--update-config', action = 'store_true', help = '是否自动更新Config文件')
    parser.add_argument('--output-dir', type = str, default = os.path.join('results', 'optimization_results'), help = '结果输出目录')
    return parser.parse_args()


def prepare_data(sample_ratio = None):
    train, test, submission = load_data()
    X, y = train[Config.FEATURES].fillna(0).values, train[Config.TARGET].values

    if sample_ratio and sample_ratio < 1:
        sample_size = int(len(X) * sample_ratio)
        print(f"sample ratio {sample_ratio}, num {sample_size}")
        indices = pd.Series(range(len(X))).sample(sample_size, random_state = Config.RANDOM_STATE)
        X, y = X[indices], y[indices]

    return X, y


def optimize_single_model(model_name, X, y, trials, cv_folds, output_dir):
    result = quick_optimize_single_model(model_name, X, y, n_trials = trials)
    result_path = os.path.join(output_dir, f'{model_name}_optimization_result.json')
    with open(result_path, 'w', encoding = 'utf-8') as f:
        json.dump({
            'model_name': model_name,
            'best_params': result['best_params'],
            'best_score': result['best_score'],
            'n_trials': result['n_trials'],
            'optimization_time': str(pd.Timestamp.now())
        }, f, indent = 2, ensure_ascii = False)
    print(f"{model_name} optimization completed!")
    print(f"Results saved to: {result_path}")

    return result


def optimize_all_models(X, y, trials, cv_folds, output_dir):
    manager = HyperparameterManager()
    results = manager.optimize_all_models(X, y, n_trials = trials, cv_folds = cv_folds)

    history_path = os.path.join(output_dir, 'optimization_history.png') # 绘制优化历史
    manager.plot_optimization_history(history_path)

    summary_path = os.path.join(output_dir, 'optimization_summary.json') # 保存所有结果摘要
    summary = {}
    for model_name, result in results.items():
        summary[model_name] = {
            'best_score': result['best_score'],
            'n_trials': result['n_trials'],
            'best_params': result['best_params']
        }

    with open(summary_path, 'w', encoding = 'utf-8') as f:
        json.dump(summary, f, indent = 2, ensure_ascii = False)

    print(f"Optimization summary saved to: {summary_path}")
    return manager, results


def print_optimization_summary(results):
    if not results:
        return
        
    print("\n" + "="*60)
    print("Optimization Results Summary")
    print("="*60)

    sorted_results = sorted(results.items(), key = lambda x: x[1]['best_score'], reverse = True)
    for model_name, result in sorted_results:
        print(f"\n{model_name.upper()}")
        print(f"   Best score: {result['best_score']:.6f}")
        print(f"   Trials: {result['n_trials']}")
        print(f"   Key parameters:")

        key_params = ['learning_rate', 'n_estimators', 'max_depth', 'reg_alpha', 'reg_lambda']
        for param in key_params:
            if param in result['best_params']:
                value = result['best_params'][param]
                if isinstance(value, float):
                    print(f"     {param}: {value:.5f}")
                else:
                    print(f"     {param}: {value}")


def flow():
    args = parse_args()
    set_seed(Config.RANDOM_STATE)
    os.makedirs(args.output_dir, exist_ok = True)
    print(f"Output directory: {args.output_dir}")

    X, y = prepare_data(getattr(args, 'sample_ratio', None))
    results, manager = None, None
    if args.model: # 单模型搜参
        result = optimize_single_model(args.model, X, y, args.trials, args.cv_folds, args.output_dir)
        if result:
            results = {args.model: result}
    elif args.all: # 全模型搜参
        manager, results = optimize_all_models(X, y, args.trials, args.cv_folds, args.output_dir)
    else:
        raise ValueError("Please specify --model or --all parameter")

    if results:
        print_optimization_summary(results)
        if args.update_config and manager: # 是否自动更新 Config中的参数
            try:
                manager.update_config()
                print("Config file automatically updated")
            except Exception as e:
                print(f"Config file update failed: {str(e)}")
                print("Please manually copy best parameters to Settings.py")
    
    print(f"\nHyperparameter optimization completed! Results saved in: {args.output_dir}")


if __name__ == "__main__":
    flow()