"""
Data Preprocessing Pipeline for Olist E-commerce
=================================================
CLO4: Kiểm soát chất lượng dữ liệu, làm sạch, chuẩn hóa, giảm số chiều

Pipeline:
1. Data Quality Assessment (6 dimensions)
2. Missing Value Handling (multiple strategies)
3. Outlier Detection & Treatment (IQR, Z-score)
4. Normalization (Min-Max, Z-Score, Robust)
5. Dimensionality Reduction (PCA)
6. Feature Engineering (domain-specific)

Usage:
    python analytics/data_preprocessing.py --data-dir ./data/raw --output-dir ./data/processed
"""

import os
import sys
import json
import logging
import argparse
from datetime import datetime

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from sklearn.decomposition import PCA
from sklearn.impute import KNNImputer

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
logger = logging.getLogger(__name__)


# ==============================================================================
# 1. DATA QUALITY ASSESSMENT
# ==============================================================================

class DataQualityAssessor:
    """Đánh giá chất lượng dữ liệu theo 6 chiều."""

    def __init__(self):
        self.report = {}

    def assess(self, df: pd.DataFrame, name: str) -> dict:
        logger.info(f"[QUALITY] Assessing '{name}': {df.shape}")
        r = {'table': name, 'rows': len(df), 'columns': len(df.columns)}

        # 1. Completeness
        missing = df.isnull().sum()
        total_cells = df.shape[0] * df.shape[1]
        r['completeness_pct'] = round((1 - missing.sum() / total_cells) * 100, 2)
        r['missing_by_col'] = {c: int(v) for c, v in missing.items() if v > 0}

        # 2. Uniqueness
        r['duplicate_rows'] = int(df.duplicated().sum())

        # 3. Validity — negatives in numeric
        num_cols = df.select_dtypes(include=[np.number]).columns
        r['negative_values'] = {c: int((df[c] < 0).sum()) for c in num_cols if (df[c] < 0).sum() > 0}

        # 4. Consistency — mixed case, whitespace
        str_cols = df.select_dtypes(include=['object']).columns
        r['consistency_issues'] = {}
        for c in str_cols:
            vals = df[c].dropna().unique()
            lower_unique = set(v.strip().lower() for v in vals if isinstance(v, str))
            if len(lower_unique) < len(vals):
                r['consistency_issues'][c] = f"{len(vals)} unique → {len(lower_unique)} after normalization"

        # 5. Outliers (IQR) on key numeric columns
        r['outliers'] = {}
        for c in num_cols:
            data = df[c].dropna()
            if len(data) < 10:
                continue
            Q1, Q3 = data.quantile(0.25), data.quantile(0.75)
            IQR = Q3 - Q1
            out_count = int(((data < Q1 - 1.5 * IQR) | (data > Q3 + 1.5 * IQR)).sum())
            if out_count > 0:
                r['outliers'][c] = {'count': out_count, 'pct': round(out_count / len(data) * 100, 2),
                                    'Q1': round(Q1, 2), 'Q3': round(Q3, 2), 'IQR': round(IQR, 2)}

        self.report[name] = r
        return r

    def print_report(self):
        for name, r in self.report.items():
            print(f"\n{'='*70}")
            print(f"  DATA QUALITY: {name} ({r['rows']:,} rows × {r['columns']} cols)")
            print(f"{'='*70}")
            print(f"  Completeness: {r['completeness_pct']}%")
            print(f"  Duplicates: {r['duplicate_rows']}")
            if r['missing_by_col']:
                print(f"  Missing values:")
                for c, v in sorted(r['missing_by_col'].items(), key=lambda x: -x[1])[:10]:
                    print(f"    {c}: {v} ({v/r['rows']*100:.1f}%)")
            if r['negative_values']:
                print(f"  Negative values: {r['negative_values']}")
            if r['consistency_issues']:
                print(f"  Consistency issues: {r['consistency_issues']}")
            if r['outliers']:
                print(f"  Outliers (IQR):")
                for c, info in list(r['outliers'].items())[:8]:
                    print(f"    {c}: {info['count']} ({info['pct']}%) | Q1={info['Q1']}, Q3={info['Q3']}")


# ==============================================================================
# 2. DATA CLEANER
# ==============================================================================

class OlistDataCleaner:
    """Làm sạch và chuẩn hóa dữ liệu Olist."""

    def __init__(self):
        self.log = []

    def _log(self, msg):
        self.log.append(msg)
        logger.info(f"[CLEAN] {msg}")

    def clean_orders(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Orders: start {len(df)} rows")
        df = df.drop_duplicates(subset=['order_id'])

        # Timestamp columns
        ts_cols = ['order_purchase_timestamp', 'order_approved_at',
                   'order_delivered_carrier_date', 'order_delivered_customer_date',
                   'order_estimated_delivery_date']
        for c in ts_cols:
            if c in df.columns:
                df[c] = pd.to_datetime(df[c], errors='coerce')

        # Standardize status
        df['order_status'] = df['order_status'].str.strip().str.lower()

        # Remove orders without purchase timestamp
        before = len(df)
        df = df.dropna(subset=['order_purchase_timestamp'])
        self._log(f"Orders: removed {before - len(df)} without purchase_ts → {len(df)} rows")

        # Derived: delivery_days
        mask = df['order_delivered_customer_date'].notna() & df['order_purchase_timestamp'].notna()
        df.loc[mask, 'delivery_days'] = (
            (df.loc[mask, 'order_delivered_customer_date'] - df.loc[mask, 'order_purchase_timestamp'])
            .dt.total_seconds() / 86400
        ).round(1)

        # delivery_delay
        mask2 = df['order_delivered_customer_date'].notna() & df['order_estimated_delivery_date'].notna()
        df.loc[mask2, 'delivery_delay_days'] = (
            (df.loc[mask2, 'order_delivered_customer_date'] - df.loc[mask2, 'order_estimated_delivery_date'])
            .dt.total_seconds() / 86400
        ).round(1)
        df['is_late_delivery'] = df['delivery_delay_days'] > 0

        return df

    def clean_order_items(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Order items: start {len(df)} rows")
        df = df.drop_duplicates(subset=['order_id', 'order_item_id'])
        for c in ['price', 'freight_value']:
            df[c] = pd.to_numeric(df[c], errors='coerce')
            neg = (df[c] < 0).sum()
            if neg > 0:
                df.loc[df[c] < 0, c] = np.nan
                self._log(f"  {c}: {neg} negatives → NaN")
        df['total_value'] = df['price'].fillna(0) + df['freight_value'].fillna(0)
        self._log(f"Order items: {len(df)} rows after clean")
        return df

    def clean_customers(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Customers: start {len(df)} rows")
        df = df.drop_duplicates(subset=['customer_id'])
        if 'customer_city' in df.columns:
            df['customer_city'] = df['customer_city'].str.strip().str.title()
        if 'customer_state' in df.columns:
            df['customer_state'] = df['customer_state'].str.strip().str.upper()
        self._log(f"Customers: {len(df)} rows after clean")
        return df

    def clean_products(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Products: start {len(df)} rows")
        df = df.drop_duplicates(subset=['product_id'])
        num_cols = ['product_weight_g', 'product_length_cm', 'product_height_cm', 'product_width_cm']
        for c in num_cols:
            if c in df.columns:
                df[c] = pd.to_numeric(df[c], errors='coerce')
                median_val = df[c].median()
                n_miss = df[c].isna().sum()
                if n_miss > 0:
                    df[c].fillna(median_val, inplace=True)
                    self._log(f"  {c}: {n_miss} missing → median ({median_val:.0f})")
        # Volume
        if all(c in df.columns for c in ['product_length_cm', 'product_height_cm', 'product_width_cm']):
            df['product_volume_cm3'] = df['product_length_cm'] * df['product_height_cm'] * df['product_width_cm']
        self._log(f"Products: {len(df)} rows after clean")
        return df

    def clean_reviews(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Reviews: start {len(df)} rows")
        df = df.drop_duplicates(subset=['review_id'])
        df['review_score'] = pd.to_numeric(df['review_score'], errors='coerce')
        df['has_comment'] = df['review_comment_message'].notna() & (df['review_comment_message'].str.len() > 0)
        df['comment_length'] = df['review_comment_message'].fillna('').str.len()
        for c in ['review_creation_date', 'review_answer_timestamp']:
            if c in df.columns:
                df[c] = pd.to_datetime(df[c], errors='coerce')
        self._log(f"Reviews: {len(df)} rows after clean")
        return df

    def clean_payments(self, df: pd.DataFrame) -> pd.DataFrame:
        self._log(f"Payments: start {len(df)} rows")
        df = df.drop_duplicates(subset=['order_id', 'payment_sequential'])
        df['payment_type'] = df['payment_type'].str.strip().str.lower()
        df['payment_value'] = pd.to_numeric(df['payment_value'], errors='coerce')
        self._log(f"Payments: {len(df)} rows after clean")
        return df


# ==============================================================================
# 3. OUTLIER TREATMENT
# ==============================================================================

def treat_outliers(df: pd.DataFrame, columns: list, method: str = 'cap') -> pd.DataFrame:
    """
    Xử lý outliers bằng capping (winsorizing) ở percentile 1% và 99%.

    Args:
        df: DataFrame
        columns: List of numeric columns
        method: 'cap' (winsorize) or 'remove'
    """
    df = df.copy()
    for col in columns:
        if col not in df.columns:
            continue
        p01, p99 = df[col].quantile(0.01), df[col].quantile(0.99)
        n_outliers = ((df[col] < p01) | (df[col] > p99)).sum()
        if method == 'cap':
            df[col] = df[col].clip(lower=p01, upper=p99)
        elif method == 'remove':
            df = df[(df[col] >= p01) & (df[col] <= p99)]
        logger.info(f"[OUTLIER] {col}: {n_outliers} outliers treated ({method}) [{p01:.2f}, {p99:.2f}]")
    return df


# ==============================================================================
# 4. NORMALIZATION
# ==============================================================================

def normalize_features(df: pd.DataFrame, columns: list, method: str = 'standard') -> pd.DataFrame:
    """Chuẩn hóa dữ liệu."""
    df = df.copy()
    scalers = {'minmax': MinMaxScaler(), 'standard': StandardScaler(), 'robust': RobustScaler()}
    scaler = scalers.get(method, StandardScaler())

    valid_cols = [c for c in columns if c in df.columns]
    df[valid_cols] = scaler.fit_transform(df[valid_cols].fillna(0))
    logger.info(f"[NORMALIZE] {method} applied to {len(valid_cols)} columns")
    return df


# ==============================================================================
# 5. PCA
# ==============================================================================

def apply_pca(df: pd.DataFrame, columns: list, n_components: float = 0.95):
    """PCA với giải thích variance ratio."""
    valid_cols = [c for c in columns if c in df.columns]
    data = df[valid_cols].dropna()

    scaler = StandardScaler()
    data_scaled = scaler.fit_transform(data)

    pca = PCA(n_components=n_components)
    result = pca.fit_transform(data_scaled)

    logger.info(f"[PCA] {len(valid_cols)} features → {pca.n_components_} components "
                f"({sum(pca.explained_variance_ratio_)*100:.1f}% variance)")

    for i, (var, cum) in enumerate(zip(pca.explained_variance_ratio_,
                                        np.cumsum(pca.explained_variance_ratio_))):
        logger.info(f"  PC{i+1}: {var:.4f} ({var*100:.1f}%) | Cumulative: {cum*100:.1f}%")

    # Loadings
    loadings = pd.DataFrame(pca.components_.T, index=valid_cols,
                            columns=[f'PC{i+1}' for i in range(pca.n_components_)])

    return result, pca, loadings


# ==============================================================================
# 6. FEATURE ENGINEERING
# ==============================================================================

def engineer_features(orders: pd.DataFrame, items: pd.DataFrame,
                      products: pd.DataFrame, customers: pd.DataFrame,
                      reviews: pd.DataFrame, payments: pd.DataFrame) -> pd.DataFrame:
    """Feature Engineering tổng hợp cho phân tích và ML."""
    logger.info("[FEATURE] Building feature table...")

    # Aggregate items per order
    order_items_agg = items.groupby('order_id').agg(
        item_count=('order_item_id', 'count'),
        total_price=('price', 'sum'),
        total_freight=('freight_value', 'sum'),
        avg_item_price=('price', 'mean'),
        max_item_price=('price', 'max'),
        n_sellers=('seller_id', 'nunique'),
        n_products=('product_id', 'nunique'),
    ).reset_index()

    # Aggregate payments per order
    order_pay_agg = payments.groupby('order_id').agg(
        total_payment=('payment_value', 'sum'),
        n_payment_methods=('payment_type', 'nunique'),
        max_installments=('payment_installments', 'max'),
        primary_payment=('payment_type', lambda x: x.mode()[0] if len(x) > 0 else 'unknown'),
    ).reset_index()

    # Merge
    feat = orders[['order_id', 'customer_id', 'order_status', 'order_purchase_timestamp',
                    'delivery_days', 'delivery_delay_days', 'is_late_delivery']].copy()

    feat = feat.merge(order_items_agg, on='order_id', how='left')
    feat = feat.merge(order_pay_agg, on='order_id', how='left')
    feat = feat.merge(reviews[['order_id', 'review_score', 'has_comment', 'comment_length']],
                      on='order_id', how='left')

    # Customer features
    cust_feats = customers[['customer_id', 'customer_city', 'customer_state']].drop_duplicates('customer_id')
    feat = feat.merge(cust_feats, on='customer_id', how='left')

    # Time features
    if 'order_purchase_timestamp' in feat.columns:
        ts = pd.to_datetime(feat['order_purchase_timestamp'])
        feat['purchase_hour'] = ts.dt.hour
        feat['purchase_dayofweek'] = ts.dt.dayofweek
        feat['purchase_month'] = ts.dt.month
        feat['is_weekend'] = (ts.dt.dayofweek >= 5).astype(int)

    # Price features
    feat['freight_ratio'] = (feat['total_freight'] / feat['total_price'].replace(0, np.nan)).round(4)
    feat['gmv'] = feat['total_price'].fillna(0) + feat['total_freight'].fillna(0)
    feat['is_free_shipping'] = (feat['total_freight'] == 0).astype(int)
    feat['is_multi_item'] = (feat['item_count'] > 1).astype(int)
    feat['is_multi_seller'] = (feat['n_sellers'] > 1).astype(int)

    # Satisfaction
    feat['is_satisfied'] = (feat['review_score'] >= 4).astype(int)

    # State region mapping
    region_map = {
        'SP': 'Southeast', 'RJ': 'Southeast', 'MG': 'Southeast', 'ES': 'Southeast',
        'PR': 'South', 'SC': 'South', 'RS': 'South',
        'BA': 'Northeast', 'PE': 'Northeast', 'CE': 'Northeast', 'MA': 'Northeast',
        'PB': 'Northeast', 'RN': 'Northeast', 'AL': 'Northeast', 'PI': 'Northeast', 'SE': 'Northeast',
        'DF': 'Central-West', 'GO': 'Central-West', 'MT': 'Central-West', 'MS': 'Central-West',
        'AM': 'North', 'PA': 'North', 'RO': 'North', 'AC': 'North',
        'AP': 'North', 'RR': 'North', 'TO': 'North',
    }
    feat['region'] = feat['customer_state'].map(region_map).fillna('Other')

    logger.info(f"[FEATURE] Feature table: {feat.shape[0]} rows × {feat.shape[1]} columns")
    return feat


# ==============================================================================
# 7. VISUALIZATION
# ==============================================================================

def create_preprocessing_report(feat: pd.DataFrame, output_dir: str):
    """Tạo visualization cho preprocessing report."""
    os.makedirs(output_dir, exist_ok=True)

    fig, axes = plt.subplots(2, 3, figsize=(18, 10))

    # 1. Missing values
    missing = feat.isnull().sum().sort_values(ascending=False).head(15)
    missing = missing[missing > 0]
    if len(missing) > 0:
        axes[0, 0].barh(range(len(missing)), missing.values, color='#e74c3c')
        axes[0, 0].set_yticks(range(len(missing)))
        axes[0, 0].set_yticklabels(missing.index, fontsize=8)
        axes[0, 0].set_title('Missing Values (Top 15)')
        axes[0, 0].set_xlabel('Count')
    else:
        axes[0, 0].text(0.5, 0.5, 'No missing values', ha='center', va='center', fontsize=14)
        axes[0, 0].set_title('Missing Values')

    # 2. Price distribution
    if 'total_price' in feat.columns:
        data = feat['total_price'].dropna()
        axes[0, 1].hist(data.clip(upper=data.quantile(0.99)), bins=50, color='#3498db', alpha=0.7)
        axes[0, 1].axvline(data.median(), color='red', linestyle='--', label=f'Median={data.median():.0f}')
        axes[0, 1].set_title('Price Distribution')
        axes[0, 1].legend()

    # 3. Delivery days distribution
    if 'delivery_days' in feat.columns:
        data = feat['delivery_days'].dropna()
        axes[0, 2].hist(data.clip(upper=data.quantile(0.99)), bins=50, color='#2ecc71', alpha=0.7)
        axes[0, 2].axvline(data.median(), color='red', linestyle='--', label=f'Median={data.median():.0f}')
        axes[0, 2].set_title('Delivery Days Distribution')
        axes[0, 2].legend()

    # 4. Review score distribution
    if 'review_score' in feat.columns:
        feat['review_score'].dropna().value_counts().sort_index().plot(
            kind='bar', ax=axes[1, 0], color=['#e74c3c', '#f39c12', '#f1c40f', '#2ecc71', '#27ae60'])
        axes[1, 0].set_title('Review Score Distribution')
        axes[1, 0].set_xlabel('Score')

    # 5. Region distribution
    if 'region' in feat.columns:
        feat['region'].value_counts().plot(kind='bar', ax=axes[1, 1], color='#9b59b6', alpha=0.7)
        axes[1, 1].set_title('Orders by Region')
        axes[1, 1].tick_params(axis='x', rotation=45)

    # 6. Correlation heatmap (top features)
    num_cols = ['total_price', 'total_freight', 'delivery_days', 'review_score',
                'item_count', 'freight_ratio']
    valid_cols = [c for c in num_cols if c in feat.columns]
    if len(valid_cols) >= 3:
        corr = feat[valid_cols].corr()
        im = axes[1, 2].imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
        axes[1, 2].set_xticks(range(len(valid_cols)))
        axes[1, 2].set_yticks(range(len(valid_cols)))
        axes[1, 2].set_xticklabels(valid_cols, fontsize=7, rotation=45, ha='right')
        axes[1, 2].set_yticklabels(valid_cols, fontsize=7)
        axes[1, 2].set_title('Correlation Matrix')
        for i in range(len(valid_cols)):
            for j in range(len(valid_cols)):
                axes[1, 2].text(j, i, f'{corr.iloc[i, j]:.2f}', ha='center', va='center', fontsize=7)

    plt.suptitle('Data Preprocessing Report - Olist E-commerce', fontsize=14, fontweight='bold')
    plt.tight_layout()
    path = os.path.join(output_dir, 'preprocessing_report.png')
    plt.savefig(path, dpi=150, bbox_inches='tight')
    plt.close()
    logger.info(f"[VIZ] Saved: {path}")


# ==============================================================================
# MAIN PIPELINE
# ==============================================================================

def main():
    parser = argparse.ArgumentParser(description='Olist Data Preprocessing Pipeline')
    parser.add_argument('--data-dir', type=str, default='./data/raw')
    parser.add_argument('--output-dir', type=str, default='./data/processed')
    args = parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    # ---- Load data ----
    logger.info("Loading Olist CSV files...")
    tables = {}
    files = {
        'orders': 'olist_orders_dataset.csv',
        'items': 'olist_order_items_dataset.csv',
        'customers': 'olist_customers_dataset.csv',
        'products': 'olist_products_dataset.csv',
        'sellers': 'olist_sellers_dataset.csv',
        'payments': 'olist_order_payments_dataset.csv',
        'reviews': 'olist_order_reviews_dataset.csv',
    }
    for name, fname in files.items():
        path = os.path.join(args.data_dir, fname)
        if os.path.exists(path):
            tables[name] = pd.read_csv(path)
            logger.info(f"  Loaded {name}: {tables[name].shape}")
        else:
            logger.warning(f"  File not found: {path}")

    if not tables:
        logger.error("No data files found. Please download Olist dataset from Kaggle.")
        sys.exit(1)

    # ---- 1. Quality Assessment ----
    qa = DataQualityAssessor()
    for name, df in tables.items():
        qa.assess(df, name)
    qa.print_report()

    # Save quality report
    with open(os.path.join(args.output_dir, 'quality_report.json'), 'w') as f:
        json.dump(qa.report, f, indent=2, default=str)

    # ---- 2. Clean data ----
    cleaner = OlistDataCleaner()
    orders = cleaner.clean_orders(tables['orders'])
    items = cleaner.clean_order_items(tables['items'])
    customers = cleaner.clean_customers(tables['customers'])
    products = cleaner.clean_products(tables['products'])
    reviews = cleaner.clean_reviews(tables['reviews'])
    payments = cleaner.clean_payments(tables['payments'])

    # ---- 3. Outlier treatment ----
    items = treat_outliers(items, ['price', 'freight_value'], method='cap')
    orders = treat_outliers(orders, ['delivery_days'], method='cap')

    # ---- 4. Feature Engineering ----
    features = engineer_features(orders, items, products, customers, reviews, payments)

    # ---- 5. PCA on product features ----
    pca_cols = ['total_price', 'total_freight', 'delivery_days', 'item_count', 'freight_ratio']
    pca_result, pca_model, loadings = apply_pca(features, pca_cols)
    logger.info(f"PCA Loadings:\n{loadings}")

    # ---- 6. Save cleaned data ----
    orders.to_parquet(os.path.join(args.output_dir, 'clean_orders.parquet'), index=False)
    items.to_parquet(os.path.join(args.output_dir, 'clean_order_items.parquet'), index=False)
    customers.to_parquet(os.path.join(args.output_dir, 'clean_customers.parquet'), index=False)
    products.to_parquet(os.path.join(args.output_dir, 'clean_products.parquet'), index=False)
    reviews.to_parquet(os.path.join(args.output_dir, 'clean_reviews.parquet'), index=False)
    payments.to_parquet(os.path.join(args.output_dir, 'clean_payments.parquet'), index=False)
    features.to_parquet(os.path.join(args.output_dir, 'feature_table.parquet'), index=False)

    logger.info(f"All cleaned data saved to {args.output_dir}/")

    # ---- 7. Visualization ----
    create_preprocessing_report(features, args.output_dir)

    # ---- Summary ----
    print(f"\n{'='*70}")
    print(f"  PREPROCESSING PIPELINE COMPLETE")
    print(f"{'='*70}")
    print(f"  Cleaned tables: {', '.join(['orders', 'items', 'customers', 'products', 'reviews', 'payments'])}")
    print(f"  Feature table: {features.shape[0]:,} rows × {features.shape[1]} columns")
    print(f"  Output: {args.output_dir}/")
    print(f"{'='*70}")


if __name__ == '__main__':
    main()