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import pandas as pd
import numpy as np
from imblearn.over_sampling import SMOTENC
import optuna
from ctgan import CTGAN
import torch
import warnings

# 지역별 데이터 파일 경로
regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
output_paths = [f'../../data/data_oversampled/ctgan7000/ctgan7000_1_{region}.csv' for region in regions]

# GPU 사용 설정
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# 경고 무시
warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")

# 지역별 처리
for file_path, output_path in zip(file_paths, output_paths):
    # 데이터 로드
    data = pd.read_csv(file_path, index_col=0)
    data= data.loc[data['year'].isin([2018,2019]),:]
    data['cloudcover'] = data['cloudcover'].astype('int')
    data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
    X = data.drop(columns=['multi_class', 'binary_class'])
    y = data['multi_class']

    # 불필요한 열 제거
    X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)

    # SMOTENC에서 사용할 범주형 변수 열 번호 설정
    categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']

    # sampling_strategy 설정
    count_class_0 = (y == 0).sum()
    count_class_1 = (y == 1).sum()
    count_class_2 = (y == 2).sum()
    sampling_strategy = {
        0: 500 if count_class_0 <= 500 else 1000,
        1: int(np.ceil(count_class_1 / 100) * 100),  # 백의 자리로 올림
        2: count_class_2
    }

    # SMOTENC 적용
    smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
    X_resampled, y_resampled = smotenc.fit_resample(X, y)

    # Resampled 데이터 생성
    lerp_data = X_resampled.copy()
    lerp_data['multi_class'] = y_resampled

    # CTGAN에서 사용할 범주형 변수 열 이름 설정
    categorical_features = [
        col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
    ]

    # Optuna 목적 함수 정의
    def objective(trial):
        # 하이퍼파라미터 탐색 범위 설정
        embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
        generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
        pac = trial.suggest_categorical("pac", [4, 8])
        batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)

        # CTGAN 모델 생성
        ctgan = CTGAN(
            embedding_dim=embedding_dim,
            generator_dim=generator_dim,
            discriminator_dim=discriminator_dim,
            batch_size=batch_size,
            discriminator_steps=discriminator_steps,
            pac=pac
        )

        # 범주 0 데이터 필터링
        data_0 = lerp_data[lerp_data['multi_class'] == 0]

        # 모델 학습
        ctgan.fit(data_0, discrete_columns=categorical_features)

        # 샘플 생성
        generated_data = ctgan.sample(len(data_0) * 2)

        # 평가: 샘플의 연속형 변수 분포 비교
        real_visi = data_0['visi']
        generated_visi = generated_data['visi']

        # 분포 간 차이(MSE) 계산
        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
        return -mse

    # Optuna로 최적화 수행
    study = optuna.create_study(direction="maximize")
    study.optimize(objective, n_trials=50)

    # 최적 하이퍼파라미터 출력
    best_params = study.best_params

    # 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
    ctgan = CTGAN(
        embedding_dim=best_params["embedding_dim"],
        generator_dim=best_params["generator_dim"],
        discriminator_dim=best_params["discriminator_dim"],
        batch_size=best_params["batch_size"],
        discriminator_steps=best_params["discriminator_steps"],
        pac=best_params["pac"]
    )

    # 범주 0 데이터로 최종 학습
    ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
    generated_0 = ctgan.sample(7000)

    # 범주 1 데이터 최적화 및 생성
    def objective_class1(trial):
        embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
        generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
        discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
        pac = trial.suggest_categorical("pac", [4, 8])
        batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
        discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)

        ctgan = CTGAN(
            embedding_dim=embedding_dim,
            generator_dim=generator_dim,
            discriminator_dim=discriminator_dim,
            batch_size=batch_size,
            discriminator_steps=discriminator_steps,
            pac=pac
        )

        data_1 = lerp_data[lerp_data['multi_class'] == 1]
        ctgan.fit(data_1, discrete_columns=categorical_features)
        generated_data = ctgan.sample(len(data_1) * 2)

        real_visi = data_1['visi']
        generated_visi = generated_data['visi']
        mse = ((real_visi.mean() - generated_visi.mean())**2 + (real_visi.std() - generated_visi.std())**2)
        return -mse

    study_class1 = optuna.create_study(direction="maximize")
    study_class1.optimize(objective_class1, n_trials=30)

    best_params_class1 = study_class1.best_params
    ctgan = CTGAN(
        embedding_dim=best_params_class1["embedding_dim"],
        generator_dim=best_params_class1["generator_dim"],
        discriminator_dim=best_params_class1["discriminator_dim"],
        batch_size=best_params_class1["batch_size"],
        discriminator_steps=best_params_class1["discriminator_steps"],
        pac=best_params_class1["pac"]
    )

    ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
    generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))

    # 데이터 병합 및 저장
    well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
    well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
    smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
    # 제거변수 복구
    smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
    smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
    smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
    smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
    smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
    smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']

    filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
    original_class2 = data[data['multi_class'] == 2]
    final_data = pd.concat([filtered_data, original_class2], axis=0)
    final_data.reset_index(drop=True, inplace=True)

    # 결과 저장
    final_data.to_csv(output_path, index = False)
    print(len(final_data[final_data['multi_class']==0]),'|',len(final_data[final_data['multi_class']==1]),'|',len(final_data[final_data['multi_class']==2]))

	import pandas as pd
	import numpy as np
	from imblearn.over_sampling import SMOTENC
	import optuna
	from ctgan import CTGAN
	import torch
	import warnings

	# 지역별 데이터 파일 경로
	regions = ['incheon', 'seoul','busan', 'daegu', 'daejeon', 'gwangju']
	file_paths = [f'../../data/data_for_modeling/{region}_train.csv' for region in regions]
	output_paths = [f'../../data/data_oversampled/ctgan7000/ctgan7000_1_{region}.csv' for region in regions]

	# GPU 사용 설정
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	print(f"Using device: {device}")

	# 경고 무시
	warnings.filterwarnings("ignore", category=UserWarning, module="optuna.distributions")

	# 지역별 처리
	for file_path, output_path in zip(file_paths, output_paths):
	# 데이터 로드
	data = pd.read_csv(file_path, index_col=0)
	data= data.loc[data['year'].isin([2018,2019]),:]
	data['cloudcover'] = data['cloudcover'].astype('int')
	data['lm_cloudcover'] = data['lm_cloudcover'].astype('int')
	X = data.drop(columns=['multi_class', 'binary_class'])
	y = data['multi_class']

	# 불필요한 열 제거
	X.drop(columns=['ground_temp - temp_C', 'hour_sin', 'hour_cos', 'month_sin', 'month_cos'], inplace=True)

	# SMOTENC에서 사용할 범주형 변수 열 번호 설정
	categorical_features_indices = [i for i, dtype in enumerate(X.dtypes) if dtype != 'float64']

	# sampling_strategy 설정
	count_class_0 = (y == 0).sum()
	count_class_1 = (y == 1).sum()
	count_class_2 = (y == 2).sum()
	sampling_strategy = {
	0: 500 if count_class_0 <= 500 else 1000,
	1: int(np.ceil(count_class_1 / 100) * 100), # 백의 자리로 올림
	2: count_class_2
	}

	# SMOTENC 적용
	smotenc = SMOTENC(categorical_features=categorical_features_indices, sampling_strategy=sampling_strategy, random_state=42)
	X_resampled, y_resampled = smotenc.fit_resample(X, y)

	# Resampled 데이터 생성
	lerp_data = X_resampled.copy()
	lerp_data['multi_class'] = y_resampled

	# CTGAN에서 사용할 범주형 변수 열 이름 설정
	categorical_features = [
	col for col, dtype in zip(lerp_data.columns, lerp_data.dtypes) if dtype != 'float64'
	]

	# Optuna 목적 함수 정의
	def objective(trial):
	# 하이퍼파라미터 탐색 범위 설정
	embedding_dim = trial.suggest_int("embedding_dim", 64, 128)
	generator_dim = trial.suggest_categorical("generator_dim", [(64, 64), (128, 128)])
	discriminator_dim = trial.suggest_categorical("discriminator_dim", [(64, 64), (128, 128)])
	pac = trial.suggest_categorical("pac", [4, 8])
	batch_size = trial.suggest_categorical("batch_size", [64, 128, 256])
	discriminator_steps = trial.suggest_int("discriminator_steps", 1, 3)

	# CTGAN 모델 생성
	ctgan = CTGAN(
	embedding_dim=embedding_dim,
	generator_dim=generator_dim,
	discriminator_dim=discriminator_dim,
	batch_size=batch_size,
	discriminator_steps=discriminator_steps,
	pac=pac
	)

	# 범주 0 데이터 필터링
	data_0 = lerp_data[lerp_data['multi_class'] == 0]

	# 모델 학습
	ctgan.fit(data_0, discrete_columns=categorical_features)

	# 샘플 생성
	generated_data = ctgan.sample(len(data_0) * 2)

	# 평가: 샘플의 연속형 변수 분포 비교
	real_visi = data_0['visi']
	generated_visi = generated_data['visi']

	# 분포 간 차이(MSE) 계산
	mse = ((real_visi.mean() - generated_visi.mean())2 + (real_visi.std() - generated_visi.std())2)
	return -mse

	# Optuna로 최적화 수행
	study = optuna.create_study(direction="maximize")
	study.optimize(objective, n_trials=50)

	# 최적 하이퍼파라미터 출력
	best_params = study.best_params

	# 최적 하이퍼파라미터로 CTGAN 학습 및 샘플 생성
	ctgan = CTGAN(
	embedding_dim=best_params["embedding_dim"],
	generator_dim=best_params["generator_dim"],
	discriminator_dim=best_params["discriminator_dim"],
	batch_size=best_params["batch_size"],
	discriminator_steps=best_params["discriminator_steps"],
	pac=best_params["pac"]
	)

	# 범주 0 데이터로 최종 학습
	ctgan.fit(lerp_data[lerp_data['multi_class'] == 0], discrete_columns=categorical_features)
	generated_0 = ctgan.sample(7000)

	# 범주 1 데이터 최적화 및 생성
	def objective_class1(trial):
	embedding_dim = trial.suggest_int("embedding_dim", 128, 512)
	generator_dim = trial.suggest_categorical("generator_dim", [(128, 128), (256, 256)])
	discriminator_dim = trial.suggest_categorical("discriminator_dim", [(128, 128), (256, 256)])
	pac = trial.suggest_categorical("pac", [4, 8])
	batch_size = trial.suggest_categorical("batch_size", [256, 512, 1024])
	discriminator_steps = trial.suggest_int("discriminator_steps", 1, 5)

	ctgan = CTGAN(
	embedding_dim=embedding_dim,
	generator_dim=generator_dim,
	discriminator_dim=discriminator_dim,
	batch_size=batch_size,
	discriminator_steps=discriminator_steps,
	pac=pac
	)

	data_1 = lerp_data[lerp_data['multi_class'] == 1]
	ctgan.fit(data_1, discrete_columns=categorical_features)
	generated_data = ctgan.sample(len(data_1) * 2)

	real_visi = data_1['visi']
	generated_visi = generated_data['visi']
	mse = ((real_visi.mean() - generated_visi.mean())2 + (real_visi.std() - generated_visi.std())2)
	return -mse

	study_class1 = optuna.create_study(direction="maximize")
	study_class1.optimize(objective_class1, n_trials=30)

	best_params_class1 = study_class1.best_params
	ctgan = CTGAN(
	embedding_dim=best_params_class1["embedding_dim"],
	generator_dim=best_params_class1["generator_dim"],
	discriminator_dim=best_params_class1["discriminator_dim"],
	batch_size=best_params_class1["batch_size"],
	discriminator_steps=best_params_class1["discriminator_steps"],
	pac=best_params_class1["pac"]
	)

	ctgan.fit(lerp_data[lerp_data['multi_class'] == 1], discrete_columns=categorical_features)
	generated_1 = ctgan.sample(7000 - int(np.ceil(count_class_1 / 100) * 100))

	# 데이터 병합 및 저장
	well_generated0 = generated_0[(generated_0['visi'] >= 0) & (generated_0['visi'] < 100)]
	well_generated1 = generated_1[(generated_1['visi'] >= 100) & (generated_1['visi'] < 500)]
	smote_gan_data = pd.concat([lerp_data, well_generated0, well_generated1], axis=0)
	# 제거변수 복구
	smote_gan_data['binary_class'] = smote_gan_data['multi_class'].apply(lambda x: 0 if x == 2 else 1)
	smote_gan_data['hour_sin'] = np.sin(2 * np.pi * smote_gan_data['hour'] / 24)
	smote_gan_data['hour_cos'] = np.cos(2 * np.pi * smote_gan_data['hour'] / 24)
	smote_gan_data['month_sin'] = np.sin(2 * np.pi * smote_gan_data['month'] / 12)
	smote_gan_data['month_cos'] = np.cos(2 * np.pi * smote_gan_data['month'] / 12)
	smote_gan_data['ground_temp - temp_C'] = smote_gan_data['groundtemp'] - smote_gan_data['temp_C']

	filtered_data = smote_gan_data[smote_gan_data['multi_class'] != 2]
	original_class2 = data[data['multi_class'] == 2]
	final_data = pd.concat([filtered_data, original_class2], axis=0)
	final_data.reset_index(drop=True, inplace=True)

	# 결과 저장
	final_data.to_csv(output_path, index = False)
	print(len(final_data[final_data['multi_class']==0]),'\|',len(final_data[final_data['multi_class']==1]),'\|',len(final_data[final_data['multi_class']==2]))