import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error
from sklearn.model_selection import GridSearchCV
import joblib
import sys
import io
import warnings
warnings.filterwarnings('ignore')

# Set standard output and standard error to UTF-8 to prevent Unicode encoding errors on Windows
if sys.platform == 'win32':
    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
    sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')

print("🚀 MEMULAI PROSES TRAINING AI LEVEL ADVANCED (ECO-TWIN PRO)...\n")

# ==========================================
# 1. LOAD LOCALIZED DATA
# ==========================================
print("1. Menarik & Memproses Data Historis Lokal...")
df = pd.read_csv('dataset_local_2026.csv')
df['Tanggal'] = pd.to_datetime(df['Tanggal'])

# Baseline Sampah (Diambil dari SIPSN DKI 2025)
base_sampah = 8020.0 
mrt_harian_avg = 85000 
hujan_mean = 10.5

# Data Event 
data_event_csv = """Tanggal,Nama_Event,Ada_Event
2023-01-01,Tahun Baru 2023,1
2023-03-11,Konser BLACKPINK,1
2023-03-12,Konser BLACKPINK,1
2023-05-26,Java Jazz,1
2023-06-19,Timnas Argentina,1
2023-11-15,Coldplay,1
2023-12-31,Tahun Baru 2024,1
2024-01-01,Tahun Baru 2024,1
2024-03-02,Ed Sheeran,1
2024-05-24,Java Jazz 2024,1
2024-12-31,Malam Tahun Baru 2025,1"""
df_event = pd.read_csv(io.StringIO(data_event_csv))
df_event['Tanggal'] = pd.to_datetime(df_event['Tanggal'])

# Bikin Master Kalender 2 Tahun (Lebih banyak data, AI makin pintar)
df = pd.DataFrame({'Tanggal': pd.date_range(start="2023-01-01", end="2024-12-31")})
df = pd.merge(df, df_event[['Tanggal', 'Ada_Event']], on='Tanggal', how='left').fillna({'Ada_Event': 0})

# Simulasi Pola Realistis
df['Penumpang_MRT'] = np.random.normal(loc=mrt_harian_avg, scale=mrt_harian_avg*0.15, size=len(df)).astype(int)
df['Curah_Hujan_mm'] = np.random.exponential(scale=hujan_mean, size=len(df))
df.loc[df['Curah_Hujan_mm'] < 2, 'Curah_Hujan_mm'] = 0

# ==========================================
# 2. FEATURE ENGINEERING (LOCAL BINDING)
# ==========================================
print("2. Melakukan One-Hot Encoding Lokasi & Verifikasi Fitur...")

# Defensive manual one-hot encoding to guarantee column names and order
locations = ['JIS', 'GBK', 'Pasar Senen', 'Gang Sempit Tambora']
for loc in locations:
    df[f'Loc_{loc}'] = (df['Location'] == loc).astype(int)

# Fitur yang dipakai AI buat berpikir
fitur = [
    'Loc_JIS', 'Loc_GBK', 'Loc_Pasar Senen', 'Loc_Gang Sempit Tambora',
    'RR', 'Rain_Lag_1', 'Rain_Lag_2', 'Is_Holiday', 'Ada_Event', 'Crowd_Scale',
    'Hari_Ke', 'Is_Weekend', 'Hari_Dalam_Minggu', 'Bulan'
]

X = df[fitur]
y = df['Volume_Ton']

# ==========================================
# 3. CHRONOLOGICAL SPLIT & TRAINING
# ==========================================
print("3. Membagi Data secara Kronologis (75/25) & Melatih Model...")

# 75% days for training, 25% for test. 
# Since we have 4 locations per day, we split at index: (len(df) // 4 * 0.75) * 4
num_days = len(df) // 4
train_days = int(num_days * 0.75)
train_size = train_days * 4

X_train, X_test = X.iloc[:train_size], X.iloc[train_size:]
y_train, y_test = y.iloc[:train_size], y.iloc[train_size:]

# Menggunakan Gradient Boosting Regressor (Baseline)
print("⚙️ Melatih model Baseline...")
base_model = GradientBoostingRegressor(
    n_estimators=200, 
    learning_rate=0.1, 
    max_depth=4, 
    random_state=42
)
base_model.fit(X_train, y_train)
pred_base = base_model.predict(X_test)

# Hitung Metrics Baseline
mae_base = mean_absolute_error(y_test, pred_base)
rmse_base = mean_squared_error(y_test, pred_base) ** 0.5
r2_base = r2_score(y_test, pred_base)
mape_base = mean_absolute_percentage_error(y_test, pred_base) * 100

# ==========================================
# 4. HYPERPARAMETER TUNING (UPGRADE MODEL)
# ==========================================
print("\n⚙️ Melakukan Hyperparameter Tuning menggunakan GridSearchCV...")
param_grid = {
    'n_estimators': [100, 200, 300],
    'learning_rate': [0.03, 0.05, 0.1, 0.15],
    'max_depth': [3, 4, 5],
    'subsample': [0.8, 0.9, 1.0]
}

grid_search = GridSearchCV(
    estimator=GradientBoostingRegressor(random_state=42),
    param_grid=param_grid,
    cv=3,
    scoring='neg_mean_absolute_error',
    n_jobs=-1,
    verbose=1
)
grid_search.fit(X_train, y_train)

best_model = grid_search.best_estimator_
pred_best = best_model.predict(X_test)

# Hitung Metrics Upgraded Model
mae_best = mean_absolute_error(y_test, pred_best)
rmse_best = mean_squared_error(y_test, pred_best) ** 0.5
r2_best = r2_score(y_test, pred_best)
mape_best = mean_absolute_percentage_error(y_test, pred_best) * 100

# ==========================================
# 5. PERBANDINGAN METRICS (BUAT DIPAMERIN KE JURI)
# ==========================================
print("\n📊 HASIL EVALUASI & PERBANDINGAN METRICS:")
print(f"┌─────────────────────────┬──────────────────────┬──────────────────────┬──────────────────────┐")
print(f"│ Metric                  │ Baseline Model       │ Upgraded Model       │ Status               │")
print(f"├─────────────────────────┼──────────────────────┼──────────────────────┼──────────────────────┤")
print(f"│ Mean Absolute Error     │ {mae_base:16.2f} Ton │ {mae_best:16.2f} Ton │ {'Semakin Baik (⬇️)' if mae_best < mae_base else 'Sama/Stabil'}  │")
print(f"│ Root Mean Squared Error │ {rmse_base:16.2f} Ton │ {rmse_best:16.2f} Ton │ {'Semakin Baik (⬇️)' if rmse_best < rmse_base else 'Sama/Stabil'}  │")
print(f"│ R-Squared (R² Score)    │ {r2_base*100:15.2f}% │ {r2_best*100:15.2f}% │ {'Semakin Baik (⬆️)' if r2_best > r2_base else 'Sama/Stabil'}  │")
print(f"│ MAPE (Error Persentase) │ {mape_base:15.2f}% │ {mape_best:15.2f}% │ {'Semakin Baik (⬇️)' if mape_best < mape_base else 'Sama/Stabil'}  │")
print(f"└─────────────────────────┴──────────────────────┴──────────────────────┴──────────────────────┘")

print(f"\n⚙️ Hyperparameter Terbaik hasil tuning:")
print(f"   - n_estimators : {grid_search.best_params_['n_estimators']}")
print(f"   - learning_rate: {grid_search.best_params_['learning_rate']}")
print(f"   - max_depth    : {grid_search.best_params_['max_depth']}")
print(f"   - subsample    : {grid_search.best_params_['subsample']}")

# Cek Fitur Paling Berpengaruh
importances = best_model.feature_importances_
print("\n🌟 FITUR PALING BERPENGARUH PADA TIMBULAN SAMPAH (UPGRADED):")
for name, importance in zip(fitur, importances):
    print(f"   - {name}: {importance*100:.1f}%")

# Simpan Model Terbaik
joblib.dump(best_model, 'model_sampah_advanced.pkl')
print("\n💾 SUCCESS! 'model_sampah_advanced.pkl' berhasil di-generate menggunakan model hasil upgrade!")