MonitoringModel.py

import pandas as pd
import numpy as np
import joblib
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
import matplotlib.pyplot as plt
import seaborn as sns 
import os

# =========================================================
# KONFIGURASI GLOBAL (tetap)
# =========================================================
DATA_FILENAME = r'C:\Dokumen\One To Many_17_10_2025\MMBTU\DASHBOARD\One To Many\disagregasi_data_spraydryer_terbaru_10_17_2025.csv'
MODEL_FOLDER = r'C:\Dokumen\One To Many_17_10_2025\MMBTU\DASHBOARD\One To Many\MODEL CHECKPOINT FOR INVERSE MODEL'
TARGET_COLUMN = 'GAS_MMBTU_Disaggregated'

PRODUCT_LIST = [
    'BMR BASE',
    'CKP BASE',
    'CKR BASE',
    'CMR BASE',
    'MORIGRO BASE'
]

FEATURES = [
    'D101330TT',
    'D102260TIC_CV',
    'D102265TIC_PV',
    'D102265TIC_CV',
    'D102266TIC',
    'D101264FTSCL'
]

PREDICTION_COLUMN = 'Prediksi_Gas'
MODEL_FILENAME_TEMPLATE = 'model_checkpoint_xgb_{}.joblib'


# =========================================================
# FUNGSI UTILITAS (tetap)
# =========================================================
def calculate_metrics(y_true, y_pred):
    """Menghitung R2, RMSE, dan MAE."""
    r2 = r2_score(y_true, y_pred)
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
    mae = mean_absolute_error(y_true, y_pred)
    return r2, rmse, mae


def _load_model_for_product(model_dir, product):
    """Load model XGBoost + poly_transformer untuk satu produk."""
    model_path = os.path.join(model_dir, MODEL_FILENAME_TEMPLATE.format(product))
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"File model tidak ditemukan: {model_path}")

    deployment_bundle = joblib.load(model_path)

    model = deployment_bundle.get('model')
    poly_transformer = deployment_bundle.get('poly_transformer')
    poly_feature_names = deployment_bundle.get('poly_feature_names')

    if model is None or poly_transformer is None or poly_feature_names is None:
        raise KeyError(
            "Bundle model tidak lengkap. Pastikan berisi "
            "'model', 'poly_transformer', dan 'poly_feature_names'."
        )

    return model, poly_transformer, poly_feature_names


# =========================================================
# FUNGSI UTAMA UNTUK DASHBOARD (PERBAIKAN)
# =========================================================
def evaluate_models_for_dashboard(
    data_path: str = DATA_FILENAME,
    model_dir: str = MODEL_FOLDER,
    products: list = None,
    features: list = None,
    target_col: str = TARGET_COLUMN,
    data_df=None,   # <--- NEW: bisa kirim DataFrame langsung dari Streamlit
):
    """
    Fungsi utama yang melakukan evaluasi performa.
    Mengembalikan:
      - summary_df: DataFrame berisi [Product, R², RMSE, MAE]
      - product_figs: dict {product_name: matplotlib.figure.Figure}

    Prioritas data:
    1) Jika data_df tidak None  -> gunakan data_df (upload dari Streamlit)
    2) Jika data_df None        -> baca dari data_path (CSV default)
    """
    if products is None:
        products = PRODUCT_LIST
    if features is None:
        features = FEATURES

    # --- 1. Load data ---
    if data_df is not None:
        # Pakai dataset yang di-upload user (sudah dalam bentuk DataFrame)
        df = data_df.copy()
    else:
        # Fallback: baca dari CSV path seperti sebelumnya
        try:
            df = pd.read_csv(data_path)
        except FileNotFoundError:
            print(f"[ERROR] Data file tidak ditemukan di: {data_path}")
            return pd.DataFrame(columns=['Product', 'R²', 'RMSE', 'MAE']), {}
        except Exception as e:
            print(f"[ERROR] Gagal memuat data: {e}")
            return pd.DataFrame(columns=['Product', 'R²', 'RMSE', 'MAE']), {}

    # Pastikan Date_time ada dan dalam bentuk datetime (kalau mau pakai time-series)
    if 'Date_time' in df.columns:
        df['Date_time'] = pd.to_datetime(df['Date_time'], errors='coerce')

    summary_results = []
    plot_data_list = [] 
    
    # --- 2. Loop per produk ---
    for product in products:
        df_prod = df[df['Product'] == product].copy()
        
        if df_prod.empty or len(df_prod) < 2:
            continue

        missing_features = [f for f in features if f not in df_prod.columns]
        if missing_features:
            print(f"[WARN] Fitur hilang untuk {product}: {missing_features}")
            continue

        if 'Date_time' in df_prod.columns:
            df_prod = df_prod.sort_values('Date_time')

        X_raw = df_prod[features]
        y_true = df_prod[target_col]

        # --- 2a. Load model produk ---
        try:
            model, poly_transformer, poly_feature_names = _load_model_for_product(model_dir, product)
        except Exception as e:
            print(f"[WARN] Gagal load model untuk {product}: {e}")
            continue

        # --- 2b. Transformasi dan prediksi ---
        try:
            X_transformed_np = poly_transformer.transform(X_raw)
            X_transformed_df = pd.DataFrame(
                X_transformed_np,
                columns=poly_feature_names,
                index=X_raw.index
            )
            y_pred = model.predict(X_transformed_df)
        except Exception as e:
            print(f"[WARN] Gagal transform/predict untuk {product}: {e}")
            continue

        # --- 2c. Hitung metrik ---
        r2, rmse, mae = calculate_metrics(y_true, y_pred)
        summary_results.append({
            'Product': product,
            'R²': r2,
            'RMSE': rmse,
            'MAE': mae
        })

        # --- 2d. Siapkan data untuk plot ---
        plot_df = pd.DataFrame({
            'Actual': y_true.values,
            'Predicted': y_pred,
            'Product': product
        })
        plot_data_list.append(plot_df)

    # --- 3. Buat summary_df ---
    if summary_results:
        summary_df = pd.DataFrame(summary_results)
        summary_df['Product'] = pd.Categorical(summary_df['Product'], categories=products, ordered=True)
        summary_df = summary_df.sort_values('Product').reset_index(drop=True)
    else:
        summary_df = pd.DataFrame(columns=['Product', 'R²', 'RMSE', 'MAE'])
        return summary_df, {} 
    
    product_figs = {}

    # --- 4. Generate Figures (per produk, untuk Streamlit) ---
    if plot_data_list:
        all_plot_data = pd.concat(plot_data_list)
        products_evaluated = summary_df['Product'].tolist()

        sns.set_style("whitegrid")

        for product in products_evaluated:
            product_data = all_plot_data[all_plot_data['Product'] == product].dropna()
            if product_data.empty:
                continue

            metrics = summary_df[summary_df['Product'] == product].iloc[0]
            title = (f'{product}\n'
                     f'$R^2$: {metrics["R²"]:.3f}, '
                     f'RMSE: {metrics["RMSE"]:.3f}, '
                     f'MAE: {metrics["MAE"]:.3f}')

            min_val = min(product_data['Actual'].min(), product_data['Predicted'].min())
            max_val = max(product_data['Actual'].max(), product_data['Predicted'].max())
            margin = (max_val - min_val) * 0.05
            plot_range = [min_val - margin, max_val + margin]

            # Figure tunggal per produk
            fig_single = plt.figure(figsize=(8, 6))
            ax_single = fig_single.add_subplot(111)
            sns.scatterplot(
                x='Actual',
                y='Predicted',
                data=product_data,
                ax=ax_single,
                alpha=0.6
            )
            ax_single.plot(plot_range, plot_range, 'r--', label='Ideal (Actual = Predicted)')
            ax_single.set_xlim(plot_range)
            ax_single.set_ylim(plot_range)
            ax_single.set_title(title)
            ax_single.set_xlabel(f'Actual {target_col}')
            ax_single.set_ylabel(f'Predicted {target_col}')
            ax_single.legend()

            product_figs[product] = fig_single
            plt.close(fig_single)

    return summary_df, product_figs

# =========================================================
# OPSIONAL: MODE CLI (tetap)
# =========================================================
if __name__ == "__main__":
    
    print("Memulai Evaluasi Performa Model Inverse...")
    
    summary_df, figs = evaluate_models_for_dashboard()
    
    print("\n" + "="*40)
    print("=== Ringkasan Performa Model ===")
    print("="*40)
    
    if not summary_df.empty:
        print(summary_df.to_markdown(index=False, floatfmt=".4f"))
    else:
        print("Gagal memproses data atau model. Periksa pesan error di atas.")