from function.optimize_manager import OptimizeManager
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os
import time
import json
from datetime import datetime

# 创建优化管理器实例
manager = OptimizeManager()

# 准备测试数据
print("--------------------------------------------准备测试数据---------------------------------------------------")
# 加载乳腺癌数据集
data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target, name='target')

# 数据预处理
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled = pd.DataFrame(X_scaled, columns=X.columns)

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 保存数据集
os.makedirs('dataset/dataset_processed/test_optimize', exist_ok=True)
train_data = pd.concat([X_train, y_train], axis=1)
train_data.to_csv('dataset/dataset_processed/test_optimize/train.csv', index=False)
test_data = pd.concat([X_test, y_test], axis=1)
test_data.to_csv('dataset/dataset_processed/test_optimize/test.csv', index=False)

print("测试数据准备完成")
print("--------------------------------------------准备测试数据 end---------------------------------------------------")

print("--------------------------------------------获取优化方法---------------------------------------------------")
# 获取所有优化方法
methods = manager.get_optimize_methods()
print("优化方法列表:")
print(methods)
print("--------------------------------------------获取优化方法 end---------------------------------------------------")

print("--------------------------------------------获取方法详细信息---------------------------------------------------")
# 获取特定方法的详细信息
method_details = manager.get_optimize_method_details('GridSearchCV')
print("\nGridSearchCV方法详情:")
print(method_details)
print("--------------------------------------------获取方法详细信息 end---------------------------------------------------")

print("---------------------------------------------测试优化模型-----------------------------------------------------------")
run_id = "bd3697dc238c4d1587e0f4f319d04448" # 运行id
method = "GridSearchCV"
parameters = {
        "max_depth": [3, 5, 7, 10, None],
        "n_estimators": [50, 100, 200],
        "min_samples_split": [2, 5, 10]
}
data_path = "dataset/dataset_processed/test_optimize/train.csv"
output_dir = None
experiment_name = "测试模型优化方法"
back = manager.run_optimization(run_id=run_id, method=method, parameters=parameters,
                         data_path=data_path,
                         output_dir=output_dir,
                         experiment_name=experiment_name)

print(back)

print("---------------------------------------------测试优化模型end--------------------------------------------------------")