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import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import sklearn
import xgboost
from xgboost import XGBRegressor
from sklearn.model_selection import KFold
import math
import time
import random as rn
RANDOM_SEED = 2025
np.random.seed(RANDOM_SEED)
rn.seed(RANDOM_SEED)
from datetime import datetime
import warnings

warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', None)
def smape(gt, preds):
    gt= np.array(gt)
    preds = np.array(preds)
    v = 2 * abs(preds - gt) / (abs(preds) + abs(gt))
    score = np.mean(v) * 100
    return score

def weighted_mse(alpha = 1):
    def weighted_mse_fixed(label, pred):
        residual = (label - pred).astype("float")
        grad = np.where(residual>0, -2*alpha*residual, -2*residual)
        hess = np.where(residual>0, 2*alpha, 2.0)
        return grad, hess
    return weighted_mse_fixed

def custom_smape(preds, dtrain):
    labels = dtrain.get_label()
    return 'custom_smape', np.mean(2 * abs(preds - labels) / (abs(preds) + abs(labels))) * 100
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
building_info = pd.read_csv('building_info.csv')
train = train.rename(columns={
    '건물번호': 'building_number',
    '일시': 'date_time',
    '기온(°C)': 'temperature',
    '강수량(mm)': 'rainfall',
    '풍속(m/s)': 'windspeed',
    '습도(%)': 'humidity',
    '일조(hr)': 'sunshine',
    '일사(MJ/m2)': 'solar_radiation',
    '전력소비량(kWh)': 'power_consumption'
})
train.drop('num_date_time', axis = 1, inplace=True)

test = test.rename(columns={
    '건물번호': 'building_number',
    '일시': 'date_time',
    '기온(°C)': 'temperature',
    '강수량(mm)': 'rainfall',
    '풍속(m/s)': 'windspeed',
    '습도(%)': 'humidity',
    '일조(hr)': 'sunshine',
    '일사(MJ/m2)': 'solar_radiation',
    '전력소비량(kWh)': 'power_consumption'
})
test.drop('num_date_time', axis = 1, inplace=True)

building_info = building_info.rename(columns={
    '건물번호': 'building_number',
    '건물유형': 'building_type',
    '연면적(m2)': 'total_area',
    '냉방면적(m2)': 'cooling_area',
    '태양광용량(kW)': 'solar_power_capacity',
    'ESS저장용량(kWh)': 'ess_capacity',
    'PCS용량(kW)': 'pcs_capacity'
})

translation_dict = {
    '건물기타': 'Other Buildings',
    '공공': 'Public',
    '학교': 'University',
    '백화점': 'Department Store',
    '병원': 'Hospital',
    '상용': 'Commercial',
    '아파트': 'Apartment',
    '연구소': 'Research Institute',
    'IDC(전화국)': 'IDC',
    '호텔': 'Hotel'
}

building_info['building_type'] = building_info['building_type'].replace(translation_dict)

building_info['solar_power_utility'] = np.where(building_info.solar_power_capacity !='-',1,0)
building_info['ess_utility'] = np.where(building_info.ess_capacity !='-',1,0)

train = pd.merge(train, building_info, on='building_number', how='left')
test = pd.merge(test, building_info, on='building_number', how='left')
train['date_time'] = pd.to_datetime(train['date_time'], format='%Y%m%d %H')

# date time feature 생성
train['hour'] = train['date_time'].dt.hour
train['day'] = train['date_time'].dt.day
train['month'] = train['date_time'].dt.month
train['day_of_week'] = train['date_time'].dt.dayofweek #요일


test['date_time'] = pd.to_datetime(test['date_time'], format='%Y%m%d %H')

# date time feature 생성
test['hour'] = test['date_time'].dt.hour
test['day'] = test['date_time'].dt.day
test['month'] = test['date_time'].dt.month
test['day_of_week'] = test['date_time'].dt.dayofweek #요일
def calculate_day_values(dataframe, target_column, output_column, aggregation_func):
    result_dict = {}

    grouped_temp = dataframe.groupby(['building_number', 'month', 'day'])[target_column].agg(aggregation_func)

    for (building, month, day), value in grouped_temp.items():
        result_dict.setdefault(building, {}).setdefault(month, {})[day] = value

    dataframe[output_column] = [
        result_dict.get(row['building_number'], {}).get(row['month'], {}).get(row['day'], None)
        for _, row in dataframe.iterrows()
    ]


train['day_max_temperature'] = 0.0
train['day_mean_temperature'] = 0.0

calculate_day_values(train, 'temperature', 'day_max_temperature', 'max')
calculate_day_values(train, 'temperature', 'day_mean_temperature', 'mean')
calculate_day_values(train, 'temperature', 'day_min_temperature', 'min')

train['day_temperature_range'] = train['day_max_temperature'] - train['day_min_temperature']

calculate_day_values(test, 'temperature', 'day_max_temperature', 'max')
calculate_day_values(test, 'temperature', 'day_mean_temperature', 'mean')
calculate_day_values(test, 'temperature', 'day_min_temperature', 'min')

test['day_temperature_range'] = test['day_max_temperature'] - test['day_min_temperature']
outlier_idx = train.index[train['power_consumption'] == 0].tolist()
print("제거할 행 개수:", len(outlier_idx))
print("인덱스 예시:", outlier_idx[:10])

# 2) 해당 인덱스들 드롭
train.drop(index=outlier_idx, inplace=True)

# 3) 드롭 후 확인
print("남은 행 개수:", train.shape[0])
holi_weekday = ['2024-06-06', '2024-08-15']

train['holiday'] = np.where((train.day_of_week >= 5) | (train.date_time.dt.strftime('%Y-%m-%d').isin(holi_weekday)), 1, 0)
test['holiday'] = np.where((test.day_of_week >= 5) | (test.date_time.dt.strftime('%Y-%m-%d').isin(holi_weekday)), 1, 0)
#시간
train['sin_hour'] = np.sin(2 * np.pi * train['hour']/23.0)
train['cos_hour'] = np.cos(2 * np.pi * train['hour']/23.0)
test['sin_hour'] = np.sin(2 * np.pi * test['hour']/23.0)
test['cos_hour'] = np.cos(2 * np.pi * test['hour']/23.0)

#날짜
train['sin_date'] = -np.sin(2 * np.pi * (train['month']+train['day']/31)/12)
train['cos_date'] = -np.cos(2 * np.pi * (train['month']+train['day']/31)/12)
test['sin_date'] = -np.sin(2 * np.pi * (test['month']+test['day']/31)/12)
test['cos_date'] = -np.cos(2 * np.pi * (test['month']+test['day']/31)/12)

#월
train['sin_month'] = -np.sin(2 * np.pi * train['month']/12.0)
train['cos_month'] = -np.cos(2 * np.pi * train['month']/12.0)
test['sin_month'] = -np.sin(2 * np.pi * test['month']/12.0)
test['cos_month'] = -np.cos(2 * np.pi * test['month']/12.0)

#요일
train['sin_dayofweek'] = -np.sin(2 * np.pi * (train['day_of_week']+1)/7.0)
train['cos_dayofweek'] = -np.cos(2 * np.pi * (train['day_of_week']+1)/7.0)
test['sin_dayofweek'] = -np.sin(2 * np.pi * (test['day_of_week']+1)/7.0)
test['cos_dayofweek'] = -np.cos(2 * np.pi * (test['day_of_week']+1)/7.0)
def CDH(xs):
    cumsum = np.cumsum(xs - 26)
    return np.concatenate((cumsum[:11], cumsum[11:] - cumsum[:-11]))

def calculate_and_add_cdh(dataframe):
    cdhs = []
    for i in range(1, 101):
        temp = dataframe[dataframe['building_number'] == i]['temperature'].values
        cdh = CDH(temp)
        cdhs.append(cdh)
    return np.concatenate(cdhs)

train['CDH'] = calculate_and_add_cdh(train)
test['CDH'] = calculate_and_add_cdh(test)
train['THI'] = 9/5*train['temperature'] - 0.55*(1-train['humidity']/100)*(9/5*train['humidity']-26)+32
test['THI'] = 9/5*test['temperature'] - 0.55*(1-test['humidity']/100)*(9/5*test['humidity']-26)+32
train['WCT'] = 13.12 + 0.6125*train['temperature'] - 11.37*(train['windspeed']**
                                                            0.16) + 0.3965*(train['windspeed']**0.16)*train['temperature']
test['WCT'] = 13.12 + 0.6125*test['temperature'] - 11.37*(test['windspeed']**
                                                            0.16) + 0.3965*(test['windspeed']**0.16)*test['temperature']
# Calculate 'day_hour_mean'
power_mean = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour', 'day_of_week'], aggfunc=np.mean).reset_index()
power_mean.columns = ['building_number', 'hour', 'day_of_week', 'day_hour_mean']

# Calculate 'day_hour_std'
power_std = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour', 'day_of_week'], aggfunc=np.std).reset_index()
power_std.columns = ['building_number', 'hour', 'day_of_week', 'day_hour_std']

# Calculate 'hour_mean'
power_hour_mean = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour'], aggfunc=np.mean).reset_index()
power_hour_mean.columns = ['building_number', 'hour', 'hour_mean']

# Calculate 'hour_std'
power_hour_std = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour'], aggfunc=np.std).reset_index()
power_hour_std.columns = ['building_number', 'hour', 'hour_std']

# Merge calculated features to 'train' and 'test' dataframes
train = train.merge(power_mean, on=['building_number', 'hour', 'day_of_week'], how='left')
test = test.merge(power_mean, on=['building_number', 'hour', 'day_of_week'], how='left')

train = train.merge(power_std, on=['building_number', 'hour', 'day_of_week'], how='left')
test = test.merge(power_std, on=['building_number', 'hour', 'day_of_week'], how='left')

train = train.merge(power_hour_mean, on=['building_number', 'hour'], how='left')
test = test.merge(power_hour_mean, on=['building_number', 'hour'], how='left')

train = train.merge(power_hour_std, on=['building_number', 'hour'], how='left')
test = test.merge(power_hour_std, on=['building_number', 'hour'], how='left')

train = train.reset_index(drop=True)
X = train.drop(['solar_power_capacity', 'ess_capacity', 'pcs_capacity',
                'power_consumption','rainfall', 'sunshine', 'solar_radiation',
                'hour','day','month','day_of_week','date_time'],axis =1 )

Y = train[['building_type','power_consumption']]

test_X = test.drop(['solar_power_capacity', 'ess_capacity', 'pcs_capacity','rainfall',
                   'hour','month','day_of_week','day','date_time'], axis=1)
type_list = []
for value in train.building_type.values:
    if value not in type_list:
        type_list.append(value)

RANDOM_SEED = 42
KFOLD_SPLITS = 2



type_list = X["building_type"].unique()

# 결과 담을 DF
answer_df = pd.DataFrame(index=test_X.index, columns=["answer"], dtype=float)
pred_df   = pd.DataFrame(index=X.index,       columns=["pred"],   dtype=float)

kf = KFold(n_splits=KFOLD_SPLITS, shuffle=True, random_state=RANDOM_SEED)

for btype in type_list:
    start_proj = time.time()
    x  = X  [X['building_type'] == btype].copy()
    y  = Y  [Y['building_type'] == btype]['power_consumption'].copy()
    xt = test_X[test_X['building_type'] == btype].copy()

    x  = pd.get_dummies(x,  columns=["building_number"], drop_first=False)
    xt = pd.get_dummies(xt, columns=["building_number"], drop_first=False)

    xt = xt.reindex(columns=x.columns, fill_value=0)

    drop_cols = ["building_type"]
    x  = x .drop(columns=drop_cols)
    xt = xt.drop(columns=drop_cols)

    preds_valid = pd.Series(index=y.index, dtype=float)
    preds_test  = []

    x_values = x.values
    y_values = y.values

    fold_scores = []
    for fold, (tr_idx, va_idx) in enumerate(kf.split(x_values), 1):
        X_tr, X_va = x_values[tr_idx], x_values[va_idx]
        y_tr, y_va = y_values[tr_idx], y_values[va_idx]

        y_tr_log = np.log(y_tr)
        y_va_log = np.log(y_va)

        model = XGBRegressor(
            learning_rate     = 0.0013292918943162175,
            n_estimators      = 4759,
            max_depth         = 23,
            subsample         = 0.7993292420985183,
            colsample_bytree  = 0.5780093202212182,
            min_child_weight  = 2,
            gamma = 0.2904180608409973,
            reg_alpha = 0.6245760287469893,
            reg_lambda = 0.002570603566117598,
            random_state      = RANDOM_SEED,
            n_jobs            = -1,
            objective         = weighted_mse(3),
            #tree_method       = "gpu_hist",
            #gpu_id            = 0,
            early_stopping_rounds = 100,
        )

        model.fit(
            X_tr, y_tr_log,
            eval_set=[(X_va, y_va_log)],
            eval_metric=custom_smape,
            verbose=False,
        )

        va_pred = np.exp(model.predict(X_va))
        preds_valid.iloc[va_idx] = va_pred

        fold_smape = smape(y_va, va_pred)
        fold_scores.append(fold_smape)

        # 테스트 예측
        preds_test.append(np.exp(model.predict(xt.values)))
        """
        if fold == KFOLD_SPLITS:          # 마지막 fold 한 번만 시각화
            sorted_idx = model.feature_importances_.argsort()
            plt.figure(figsize=(8, 15))
            plt.barh(x.columns[sorted_idx], model.feature_importances_[sorted_idx])
            plt.xlabel(f"{btype} XGB Feature Importance")
            plt.show()"""

    # 검증 예측 저장
    pred_df.loc[preds_valid.index, "pred"] = preds_valid

    # 테스트 예측(앙상블 평균) 저장
    answer_df.loc[xt.index, "answer"] = np.mean(preds_test, axis=0)
    end_proj = time.time()
    print(f"Building type = {btype} : XGB SMAPE = {np.mean(fold_scores):.4f} : {end_proj-start_proj:5f} sec")

total_smape = smape(
    Y.sort_index()["power_consumption"].values,
    pred_df.sort_index()["pred"].values
)
print(f"Total SMAPE = {total_smape:.4f}")
submit = pd.read_csv("sample_submission.csv")
submit["answer"] = answer_df.loc[submit.index, "answer"]
submit.to_csv("private_재현_3.csv", index=False)
print("Saved → private_재현_3.csv")

	import pandas as pd
	import seaborn as sns
	import numpy as np
	import matplotlib.pyplot as plt
	import matplotlib
	import sklearn
	import xgboost
	from xgboost import XGBRegressor
	from sklearn.model_selection import KFold
	import math
	import time
	import random as rn
	RANDOM_SEED = 2025
	np.random.seed(RANDOM_SEED)
	rn.seed(RANDOM_SEED)
	from datetime import datetime
	import warnings

	warnings.filterwarnings('ignore')
	pd.set_option('display.max_columns', None)
	def smape(gt, preds):
	gt= np.array(gt)
	preds = np.array(preds)
	v = 2 * abs(preds - gt) / (abs(preds) + abs(gt))
	score = np.mean(v) * 100
	return score

	def weighted_mse(alpha = 1):
	def weighted_mse_fixed(label, pred):
	residual = (label - pred).astype("float")
	grad = np.where(residual>0, -2alpharesidual, -2*residual)
	hess = np.where(residual>0, 2*alpha, 2.0)
	return grad, hess
	return weighted_mse_fixed

	def custom_smape(preds, dtrain):
	labels = dtrain.get_label()
	return 'custom_smape', np.mean(2 * abs(preds - labels) / (abs(preds) + abs(labels))) * 100
	train = pd.read_csv('train.csv')
	test = pd.read_csv('test.csv')
	building_info = pd.read_csv('building_info.csv')
	train = train.rename(columns={
	'건물번호': 'building_number',
	'일시': 'date_time',
	'기온(°C)': 'temperature',
	'강수량(mm)': 'rainfall',
	'풍속(m/s)': 'windspeed',
	'습도(%)': 'humidity',
	'일조(hr)': 'sunshine',
	'일사(MJ/m2)': 'solar_radiation',
	'전력소비량(kWh)': 'power_consumption'
	})
	train.drop('num_date_time', axis = 1, inplace=True)

	test = test.rename(columns={
	'건물번호': 'building_number',
	'일시': 'date_time',
	'기온(°C)': 'temperature',
	'강수량(mm)': 'rainfall',
	'풍속(m/s)': 'windspeed',
	'습도(%)': 'humidity',
	'일조(hr)': 'sunshine',
	'일사(MJ/m2)': 'solar_radiation',
	'전력소비량(kWh)': 'power_consumption'
	})
	test.drop('num_date_time', axis = 1, inplace=True)

	building_info = building_info.rename(columns={
	'건물번호': 'building_number',
	'건물유형': 'building_type',
	'연면적(m2)': 'total_area',
	'냉방면적(m2)': 'cooling_area',
	'태양광용량(kW)': 'solar_power_capacity',
	'ESS저장용량(kWh)': 'ess_capacity',
	'PCS용량(kW)': 'pcs_capacity'
	})

	translation_dict = {
	'건물기타': 'Other Buildings',
	'공공': 'Public',
	'학교': 'University',
	'백화점': 'Department Store',
	'병원': 'Hospital',
	'상용': 'Commercial',
	'아파트': 'Apartment',
	'연구소': 'Research Institute',
	'IDC(전화국)': 'IDC',
	'호텔': 'Hotel'
	}

	building_info['building_type'] = building_info['building_type'].replace(translation_dict)

	building_info['solar_power_utility'] = np.where(building_info.solar_power_capacity !='-',1,0)
	building_info['ess_utility'] = np.where(building_info.ess_capacity !='-',1,0)

	train = pd.merge(train, building_info, on='building_number', how='left')
	test = pd.merge(test, building_info, on='building_number', how='left')
	train['date_time'] = pd.to_datetime(train['date_time'], format='%Y%m%d %H')

	# date time feature 생성
	train['hour'] = train['date_time'].dt.hour
	train['day'] = train['date_time'].dt.day
	train['month'] = train['date_time'].dt.month
	train['day_of_week'] = train['date_time'].dt.dayofweek #요일


	test['date_time'] = pd.to_datetime(test['date_time'], format='%Y%m%d %H')

	# date time feature 생성
	test['hour'] = test['date_time'].dt.hour
	test['day'] = test['date_time'].dt.day
	test['month'] = test['date_time'].dt.month
	test['day_of_week'] = test['date_time'].dt.dayofweek #요일
	def calculate_day_values(dataframe, target_column, output_column, aggregation_func):
	result_dict = {}

	grouped_temp = dataframe.groupby(['building_number', 'month', 'day'])[target_column].agg(aggregation_func)

	for (building, month, day), value in grouped_temp.items():
	result_dict.setdefault(building, {}).setdefault(month, {})[day] = value

	dataframe[output_column] = [
	result_dict.get(row['building_number'], {}).get(row['month'], {}).get(row['day'], None)
	for _, row in dataframe.iterrows()
	]


	train['day_max_temperature'] = 0.0
	train['day_mean_temperature'] = 0.0

	calculate_day_values(train, 'temperature', 'day_max_temperature', 'max')
	calculate_day_values(train, 'temperature', 'day_mean_temperature', 'mean')
	calculate_day_values(train, 'temperature', 'day_min_temperature', 'min')

	train['day_temperature_range'] = train['day_max_temperature'] - train['day_min_temperature']

	calculate_day_values(test, 'temperature', 'day_max_temperature', 'max')
	calculate_day_values(test, 'temperature', 'day_mean_temperature', 'mean')
	calculate_day_values(test, 'temperature', 'day_min_temperature', 'min')

	test['day_temperature_range'] = test['day_max_temperature'] - test['day_min_temperature']
	outlier_idx = train.index[train['power_consumption'] == 0].tolist()
	print("제거할 행 개수:", len(outlier_idx))
	print("인덱스 예시:", outlier_idx[:10])

	# 2) 해당 인덱스들 드롭
	train.drop(index=outlier_idx, inplace=True)

	# 3) 드롭 후 확인
	print("남은 행 개수:", train.shape[0])
	holi_weekday = ['2024-06-06', '2024-08-15']

	train['holiday'] = np.where((train.day_of_week >= 5) \| (train.date_time.dt.strftime('%Y-%m-%d').isin(holi_weekday)), 1, 0)
	test['holiday'] = np.where((test.day_of_week >= 5) \| (test.date_time.dt.strftime('%Y-%m-%d').isin(holi_weekday)), 1, 0)
	#시간
	train['sin_hour'] = np.sin(2 * np.pi * train['hour']/23.0)
	train['cos_hour'] = np.cos(2 * np.pi * train['hour']/23.0)
	test['sin_hour'] = np.sin(2 * np.pi * test['hour']/23.0)
	test['cos_hour'] = np.cos(2 * np.pi * test['hour']/23.0)

	#날짜
	train['sin_date'] = -np.sin(2 * np.pi * (train['month']+train['day']/31)/12)
	train['cos_date'] = -np.cos(2 * np.pi * (train['month']+train['day']/31)/12)
	test['sin_date'] = -np.sin(2 * np.pi * (test['month']+test['day']/31)/12)
	test['cos_date'] = -np.cos(2 * np.pi * (test['month']+test['day']/31)/12)

	#월
	train['sin_month'] = -np.sin(2 * np.pi * train['month']/12.0)
	train['cos_month'] = -np.cos(2 * np.pi * train['month']/12.0)
	test['sin_month'] = -np.sin(2 * np.pi * test['month']/12.0)
	test['cos_month'] = -np.cos(2 * np.pi * test['month']/12.0)

	#요일
	train['sin_dayofweek'] = -np.sin(2 * np.pi * (train['day_of_week']+1)/7.0)
	train['cos_dayofweek'] = -np.cos(2 * np.pi * (train['day_of_week']+1)/7.0)
	test['sin_dayofweek'] = -np.sin(2 * np.pi * (test['day_of_week']+1)/7.0)
	test['cos_dayofweek'] = -np.cos(2 * np.pi * (test['day_of_week']+1)/7.0)
	def CDH(xs):
	cumsum = np.cumsum(xs - 26)
	return np.concatenate((cumsum[:11], cumsum[11:] - cumsum[:-11]))

	def calculate_and_add_cdh(dataframe):
	cdhs = []
	for i in range(1, 101):
	temp = dataframe[dataframe['building_number'] == i]['temperature'].values
	cdh = CDH(temp)
	cdhs.append(cdh)
	return np.concatenate(cdhs)

	train['CDH'] = calculate_and_add_cdh(train)
	test['CDH'] = calculate_and_add_cdh(test)
	train['THI'] = 9/5train['temperature'] - 0.55(1-train['humidity']/100)(9/5train['humidity']-26)+32
	test['THI'] = 9/5test['temperature'] - 0.55(1-test['humidity']/100)(9/5test['humidity']-26)+32
	train['WCT'] = 13.12 + 0.6125train['temperature'] - 11.37(train['windspeed']**
	0.16) + 0.3965(train['windspeed']0.16)train['temperature']
	test['WCT'] = 13.12 + 0.6125test['temperature'] - 11.37(test['windspeed']**
	0.16) + 0.3965(test['windspeed']0.16)test['temperature']
	# Calculate 'day_hour_mean'
	power_mean = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour', 'day_of_week'], aggfunc=np.mean).reset_index()
	power_mean.columns = ['building_number', 'hour', 'day_of_week', 'day_hour_mean']

	# Calculate 'day_hour_std'
	power_std = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour', 'day_of_week'], aggfunc=np.std).reset_index()
	power_std.columns = ['building_number', 'hour', 'day_of_week', 'day_hour_std']

	# Calculate 'hour_mean'
	power_hour_mean = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour'], aggfunc=np.mean).reset_index()
	power_hour_mean.columns = ['building_number', 'hour', 'hour_mean']

	# Calculate 'hour_std'
	power_hour_std = pd.pivot_table(train, values='power_consumption', index=['building_number', 'hour'], aggfunc=np.std).reset_index()
	power_hour_std.columns = ['building_number', 'hour', 'hour_std']

	# Merge calculated features to 'train' and 'test' dataframes
	train = train.merge(power_mean, on=['building_number', 'hour', 'day_of_week'], how='left')
	test = test.merge(power_mean, on=['building_number', 'hour', 'day_of_week'], how='left')

	train = train.merge(power_std, on=['building_number', 'hour', 'day_of_week'], how='left')
	test = test.merge(power_std, on=['building_number', 'hour', 'day_of_week'], how='left')

	train = train.merge(power_hour_mean, on=['building_number', 'hour'], how='left')
	test = test.merge(power_hour_mean, on=['building_number', 'hour'], how='left')

	train = train.merge(power_hour_std, on=['building_number', 'hour'], how='left')
	test = test.merge(power_hour_std, on=['building_number', 'hour'], how='left')

	train = train.reset_index(drop=True)
	X = train.drop(['solar_power_capacity', 'ess_capacity', 'pcs_capacity',
	'power_consumption','rainfall', 'sunshine', 'solar_radiation',
	'hour','day','month','day_of_week','date_time'],axis =1 )

	Y = train[['building_type','power_consumption']]

	test_X = test.drop(['solar_power_capacity', 'ess_capacity', 'pcs_capacity','rainfall',
	'hour','month','day_of_week','day','date_time'], axis=1)
	type_list = []
	for value in train.building_type.values:
	if value not in type_list:
	type_list.append(value)

	RANDOM_SEED = 42
	KFOLD_SPLITS = 2



	type_list = X["building_type"].unique()

	# 결과 담을 DF
	answer_df = pd.DataFrame(index=test_X.index, columns=["answer"], dtype=float)
	pred_df = pd.DataFrame(index=X.index, columns=["pred"], dtype=float)

	kf = KFold(n_splits=KFOLD_SPLITS, shuffle=True, random_state=RANDOM_SEED)

	for btype in type_list:
	start_proj = time.time()
	x = X [X['building_type'] == btype].copy()
	y = Y [Y['building_type'] == btype]['power_consumption'].copy()
	xt = test_X[test_X['building_type'] == btype].copy()

	x = pd.get_dummies(x, columns=["building_number"], drop_first=False)
	xt = pd.get_dummies(xt, columns=["building_number"], drop_first=False)

	xt = xt.reindex(columns=x.columns, fill_value=0)

	drop_cols = ["building_type"]
	x = x .drop(columns=drop_cols)
	xt = xt.drop(columns=drop_cols)

	preds_valid = pd.Series(index=y.index, dtype=float)
	preds_test = []

	x_values = x.values
	y_values = y.values

	fold_scores = []
	for fold, (tr_idx, va_idx) in enumerate(kf.split(x_values), 1):
	X_tr, X_va = x_values[tr_idx], x_values[va_idx]
	y_tr, y_va = y_values[tr_idx], y_values[va_idx]

	y_tr_log = np.log(y_tr)
	y_va_log = np.log(y_va)

	model = XGBRegressor(
	learning_rate = 0.0013292918943162175,
	n_estimators = 4759,
	max_depth = 23,
	subsample = 0.7993292420985183,
	colsample_bytree = 0.5780093202212182,
	min_child_weight = 2,
	gamma = 0.2904180608409973,
	reg_alpha = 0.6245760287469893,
	reg_lambda = 0.002570603566117598,
	random_state = RANDOM_SEED,
	n_jobs = -1,
	objective = weighted_mse(3),
	#tree_method = "gpu_hist",
	#gpu_id = 0,
	early_stopping_rounds = 100,
	)

	model.fit(
	X_tr, y_tr_log,
	eval_set=[(X_va, y_va_log)],
	eval_metric=custom_smape,
	verbose=False,
	)

	va_pred = np.exp(model.predict(X_va))
	preds_valid.iloc[va_idx] = va_pred

	fold_smape = smape(y_va, va_pred)
	fold_scores.append(fold_smape)

	# 테스트 예측
	preds_test.append(np.exp(model.predict(xt.values)))
	"""
	if fold == KFOLD_SPLITS: # 마지막 fold 한 번만 시각화
	sorted_idx = model.feature_importances_.argsort()
	plt.figure(figsize=(8, 15))
	plt.barh(x.columns[sorted_idx], model.feature_importances_[sorted_idx])
	plt.xlabel(f"{btype} XGB Feature Importance")
	plt.show()"""

	# 검증 예측 저장
	pred_df.loc[preds_valid.index, "pred"] = preds_valid

	# 테스트 예측(앙상블 평균) 저장
	answer_df.loc[xt.index, "answer"] = np.mean(preds_test, axis=0)
	end_proj = time.time()
	print(f"Building type = {btype} : XGB SMAPE = {np.mean(fold_scores):.4f} : {end_proj-start_proj:5f} sec")

	total_smape = smape(
	Y.sort_index()["power_consumption"].values,
	pred_df.sort_index()["pred"].values
	)
	print(f"Total SMAPE = {total_smape:.4f}")
	submit = pd.read_csv("sample_submission.csv")
	submit["answer"] = answer_df.loc[submit.index, "answer"]
	submit.to_csv("private_재현_3.csv", index=False)
	print("Saved → private_재현_3.csv")