"""Enhanced Feature Engineering: Add domain-specific DNA features."""

import pandas as pd
import numpy as np
from pathlib import Path
import json
import warnings

warnings.filterwarnings('ignore')

ROOT = Path(__file__).parent.parent.parent
RECONSTRUCTED_DIR = ROOT / "data/reconstructed"
RAW_DATA_DIR = ROOT / "data/PROVEDIt_1-5-Person CSVs UnFiltered"
OUTPUT_DIR = ROOT / "data/reconstructed/production"
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

def load_all_sample_data():
    """Load all sample labels from both phases."""
    print("📖 Loading all sample labels...")

    labels_df = pd.read_csv(RECONSTRUCTED_DIR / "sample_labels.csv")
    print(f"✅ Loaded {len(labels_df)} total samples")

    # Split by study
    rd14_samples = labels_df[labels_df['study_id'] == 'RD14-0003'].copy()
    rd12_samples = labels_df[labels_df['study_id'] == 'RD12-0002'].copy()

    print(f"   RD14: {len(rd14_samples)} samples")
    print(f"   RD12: {len(rd12_samples)} samples")

    return labels_df, rd14_samples, rd12_samples

def extract_peak_features_enhanced(sample_file, raw_data_dir):
    """Extract enhanced peak features from raw CSV."""
    features = {}

    try:
        # Find the CSV file
        found_files = list(raw_data_dir.rglob(f"{sample_file}"))
        if not found_files:
            return features

        csv_path = found_files[0]
        df = pd.read_csv(csv_path)

        # Extract statistics per marker
        for _, row in df.iterrows():
            marker = row['Marker']

            # Extract all heights and sizes
            heights = []
            sizes = []
            alleles = []

            for i in range(1, 101):
                height_col = f'Height {i}'
                size_col = f'Size {i}'
                allele_col = f'Allele {i}'

                if height_col in df.columns:
                    height = row[height_col]
                    if pd.notna(height) and height != '' and str(height).upper() != 'NAN':
                        try:
                            h = float(height)
                            if h > 0:
                                heights.append(h)
                        except:
                            pass

                if size_col in df.columns and len(heights) > 0:
                    size = row[size_col]
                    if pd.notna(size) and size != '':
                        try:
                            sizes.append(float(size))
                        except:
                            pass

                if allele_col in df.columns:
                    allele = str(row[allele_col]).strip()
                    if allele and allele.upper() != 'OL' and allele != 'nan':
                        alleles.append(allele)

            if heights:
                # Basic features
                features[f"{marker}_peak_count"] = len(heights)
                features[f"{marker}_max_height"] = max(heights)
                features[f"{marker}_sum_height"] = sum(heights)
                features[f"{marker}_mean_height"] = np.mean(heights)
                features[f"{marker}_std_height"] = np.std(heights) if len(heights) > 1 else 0

                # Domain-specific: Peak height ratio (peak 1 / peak 2)
                if len(heights) >= 2:
                    sorted_heights = sorted(heights, reverse=True)
                    features[f"{marker}_peak_ratio"] = sorted_heights[0] / sorted_heights[1] if sorted_heights[1] > 0 else 0
                    # Allele balance: ratio of two highest peaks
                    features[f"{marker}_allele_balance"] = min(sorted_heights[0], sorted_heights[1]) / max(sorted_heights[0], sorted_heights[1]) if sorted_heights[0] > 0 else 0

                # Domain-specific: Homozygosity indicator
                if len(alleles) == 2:
                    features[f"{marker}_is_homozygous"] = 1 if alleles[0] == alleles[1] else 0
                else:
                    features[f"{marker}_is_homozygous"] = 0

                # Domain-specific: Peak distribution (coefficient of variation)
                if len(heights) > 1:
                    features[f"{marker}_cv_heights"] = np.std(heights) / np.mean(heights) if np.mean(heights) > 0 else 0

    except Exception as e:
        pass

    return features

def build_enhanced_feature_matrix(labels_df):
    """Build feature matrix with domain-specific features."""
    print("\n🛠️  Building enhanced feature matrix with domain features...")

    features_list = []

    for idx, row in labels_df.iterrows():
        if idx % 100 == 0:
            print(f"   Processing {idx}/{len(labels_df)}...", end='\r')

        sample_file = row['sample_file']

        # Basic info
        feature_dict = {
            'sample_file': sample_file,
            'study_id': row['study_id'],
            'kit': row['kit'],
            'num_contributors': row['num_contributors'],
            'num_known': row['num_known'],
            'num_unknown': row['num_unknown'],
            'unknown_present': row['unknown_present'],
        }

        # Extract enhanced peak features
        peak_features = extract_peak_features_enhanced(sample_file, RAW_DATA_DIR)
        feature_dict.update(peak_features)

        features_list.append(feature_dict)

    feature_df = pd.DataFrame(features_list)

    # Fill NaN with 0
    feature_df = feature_df.fillna(0)

    # Filter: Keep only samples with minimum markers
    marker_threshold = 15
    marker_cols = [c for c in feature_df.columns if '_peak_count' in c]
    feature_df['num_markers_detected'] = (feature_df[marker_cols] > 0).sum(axis=1)

    print(f"\n   Total features before filtering: {len(feature_df)}")
    print(f"   Min marker threshold: {marker_threshold}")

    filtered_df = feature_df[feature_df['num_markers_detected'] >= marker_threshold].copy()

    print(f"   Total features after filtering: {len(filtered_df)}")
    print(f"   Feature columns: {len(filtered_df.columns)}")

    return filtered_df

def split_by_study(feature_df):
    """Split dataset by study."""
    print("\n📊 Splitting by study...")

    rd14_df = feature_df[feature_df['study_id'] == 'RD14-0003'].copy()
    rd12_df = feature_df[feature_df['study_id'] == 'RD12-0002'].copy()

    print(f"   RD14: {len(rd14_df)} samples")
    print(f"   RD12: {len(rd12_df)} samples")
    print(f"   Combined: {len(feature_df)} samples")

    return rd14_df, rd12_df, feature_df

def save_enhanced_features(rd14_df, rd12_df, combined_df):
    """Save enhanced feature matrices."""
    print("\n💾 Saving enhanced feature matrices...")

    # Save by study
    rd14_df.to_csv(OUTPUT_DIR / "rd14_enhanced_features.csv", index=False)
    rd12_df.to_csv(OUTPUT_DIR / "rd12_enhanced_features.csv", index=False)

    # Save combined
    combined_df.to_csv(OUTPUT_DIR / "combined_enhanced_features.csv", index=False)

    # Save summary (convert numpy types to python native types)
    summary = {
        "rd14": {
            "total_samples": int(len(rd14_df)),
            "num_features": int(len(rd14_df.columns)),
            "unknown_present_0": int((rd14_df['unknown_present'] == 0).sum()),
            "unknown_present_1": int((rd14_df['unknown_present'] == 1).sum()),
        },
        "rd12": {
            "total_samples": int(len(rd12_df)),
            "num_features": int(len(rd12_df.columns)),
            "unknown_present_0": int((rd12_df['unknown_present'] == 0).sum()),
            "unknown_present_1": int((rd12_df['unknown_present'] == 1).sum()),
        },
        "combined": {
            "total_samples": int(len(combined_df)),
            "num_features": int(len(combined_df.columns)),
            "unknown_present_0": int((combined_df['unknown_present'] == 0).sum()),
            "unknown_present_1": int((combined_df['unknown_present'] == 1).sum()),
        }
    }

    with open(OUTPUT_DIR / "enhanced_features_summary.json", "w") as f:
        json.dump(summary, f, indent=2)

    print(f"   ✅ RD14: {OUTPUT_DIR}/rd14_enhanced_features.csv")
    print(f"   ✅ RD12: {OUTPUT_DIR}/rd12_enhanced_features.csv")
    print(f"   ✅ Combined: {OUTPUT_DIR}/combined_enhanced_features.csv")

    return summary

def main():
    print("=" * 80)
    print("🔧 STEP 5: Enhanced Feature Engineering")
    print("=" * 80)

    # Load data
    labels_df, rd14_samples, rd12_samples = load_all_sample_data()

    # Build enhanced features
    feature_df = build_enhanced_feature_matrix(labels_df)

    # Split by study
    rd14_df, rd12_df, combined_df = split_by_study(feature_df)

    # Save
    summary = save_enhanced_features(rd14_df, rd12_df, combined_df)

    print("\n" + "=" * 80)
    print("✅ STEP 5 COMPLETE!")
    print("=" * 80)
    print("\n📊 Feature Engineering Summary:")
    print(json.dumps(summary, indent=2))
    print(f"\nNext step: Light hyperparameter tuning with 5-fold CV")

if __name__ == "__main__":
    main()