#!/usr/bin/env python3
"""
scaffold_split.py

Author: natelgrw
Last Edited: 11/02/2025

Computes Bemis-Murcko scaffolds for the retina dataset using RDKit 
and splits scaffolds into 5 distinct folds with approximately balanced 
compound counts across folds. Computes UMAP, scaffold assignments, and
method length distributions for visualizing scaffold splits.
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from collections import defaultdict, Counter
import random
from tqdm import tqdm
from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from rdkit.Chem import AllChem
import umap


# ===== Configuration ===== #


INPUT_CSV = "../retina_dataset.csv"
OUTPUT_DIR = "../scaffold_split"
N_FOLDS = 5
RANDOM_SEED = 42

random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)


# ===== Helper Functions ===== #


def get_murcko_scaffold(smiles):
    """
    Compute Bemis-Murcko scaffold from SMILES string.
    """
    try:
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            return "INVALID"
        scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol=mol)
        return scaffold if scaffold else "NO_SCAFFOLD"
    except Exception as e:
        return "INVALID"


def analyze_dataset(df):
    """
    Prints dataset statistics.
    """
    print("=" * 70)
    print("Dataset Analysis")
    print("=" * 70)
    print(f"Total rows: {len(df):,}")
    print(f"Columns: {df.columns.tolist()}")
    print(f"\nUnique compounds: {df['compound'].nunique():,}")
    if 'rt' in df.columns:
        print(f"\nRetention time statistics:")
        print(f"Min: {df['rt'].min():.2f} min")
        print(f"Max: {df['rt'].max():.2f} min")
        print(f"Mean: {df['rt'].mean():.2f} min")
        print(f"Median: {df['rt'].median():.2f} min")
    print()


def assign_scaffolds_to_folds(scaffold_sizes, n_folds):
    """
    Assign scaffolds to folds using a greedy algorithm to balance compound counts.
    """
    fold_assignments = defaultdict(list)
    fold_counts = [0] * n_folds
    
    sorted_scaffolds = sorted(scaffold_sizes.items(), key=lambda x: x[1], reverse=True)
    
    # greedy scaffold assignment
    for scaffold, size in sorted_scaffolds:
        min_fold = min(range(n_folds), key=lambda i: fold_counts[i])
        fold_assignments[min_fold].append(scaffold)
        fold_counts[min_fold] += size
    
    return fold_assignments, fold_counts


# ===== Analyzer Class ===== #


class ScaffoldAnalyzer:
    """
    Analyzer for scaffold splitting and visualization.
    """
    
    def __init__(self, data_path, output_dir):
        self.data_path = Path(data_path)
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True, parents=True)
        self.df = None
        self.scaffold_dict = {}
        self.scaffold_to_compounds = defaultdict(list)
        
    def load_data(self, sample_size=None):
        """
        Loads the retina dataset.
        """
        print("\n" + "=" * 70)
        print("LOADING DATA")
        print("=" * 70)
        
        if sample_size:
            print(f"Loading {sample_size:,} rows (sample)...")
            self.df = pd.read_csv(self.data_path, nrows=sample_size)
        else:
            print(f"Loading full dataset from {self.data_path}...")
            chunks = []
            chunk_size = 100000
            for chunk in tqdm(pd.read_csv(self.data_path, chunksize=chunk_size), 
                            desc="Reading chunks"):
                chunks.append(chunk)
            self.df = pd.concat(chunks, ignore_index=True)
        
        print(f"  Loaded {len(self.df):,} data points")
        print(f"  Columns: {list(self.df.columns)}")
        return self.df
    
    def extract_scaffolds(self):
        """
        Extracts Bemis-Murcko scaffolds from SMILES.
        """
        print("\n" + "=" * 70)
        print("EXTRACTING BEMIS-MURCKO SCAFFOLDS")
        print("=" * 70)
        
        print("Computing scaffolds for all compounds...")
        self.df['scaffold'] = self.df['compound'].apply(get_murcko_scaffold)
        
        invalid_count = (self.df['scaffold'] == "INVALID").sum()
        no_scaffold_count = (self.df['scaffold'] == "NO_SCAFFOLD").sum()
        valid_count = len(self.df) - invalid_count - no_scaffold_count
        
        if invalid_count > 0:
            print(f"Warning: {invalid_count:,} rows have invalid SMILES")
        if no_scaffold_count > 0:
            print(f"Info: {no_scaffold_count:,} rows have no scaffold (single atoms)")
        
        print(f"Successfully extracted scaffolds for {valid_count:,} rows")
        
        scaffold_groups = self.df.groupby('scaffold')
        scaffold_sizes = scaffold_groups.size().to_dict()
        
        n_unique_scaffolds = len(scaffold_sizes)
        sizes_array = np.array(list(scaffold_sizes.values()))
        
        print(f"\nScaffold Statistics:")
        print(f"Unique scaffolds: {n_unique_scaffolds:,}")
        print(f"Scaffolds with 1 data point: {(sizes_array == 1).sum():,}")
        print(f"Scaffolds with >10 data points: {(sizes_array > 10).sum():,}")
        print(f"Scaffolds with >100 data points: {(sizes_array > 100).sum():,}")
        print(f"Mean data points per scaffold: {sizes_array.mean():.2f}")
        print(f"Median data points per scaffold: {np.median(sizes_array):.0f}")
        print(f"Max data points in a scaffold: {sizes_array.max():,}")
        
        return scaffold_sizes

    def create_scaffold_splits(self, scaffold_sizes, n_splits=5):
        """
        Creates scaffold-based cross-validation splits.
        
        Splits are created by grouping scaffolds to balance the number of data points
        in each fold while ensuring no scaffold appears in multiple folds.
        """
        print("\n" + "=" * 70)
        print(f"CREATING {n_splits}-FOLD SCAFFOLD SPLITS")
        print("=" * 70)
        
        print(f"Assigning {len(scaffold_sizes):,} scaffolds to {n_splits} folds...")
        fold_assignments, fold_counts = assign_scaffolds_to_folds(scaffold_sizes, n_splits)
        
        print("\nFold Statistics:")
        print("-" * 70)
        for fold_id in range(n_splits):
            scaffolds = fold_assignments[fold_id]
            count = fold_counts[fold_id]
            percentage = 100 * count / len(self.df)
            print(f"Fold {fold_id + 1}: {count:,} data points ({percentage:.2f}%) | "
                  f"{len(scaffolds):,} scaffolds")
        print("-" * 70)
        print(f"Total: {sum(fold_counts):,} data points")
        
        scaffold_to_fold = {}
        for fold_idx in range(n_splits):
            for scaffold in fold_assignments[fold_idx]:
                scaffold_to_fold[scaffold] = fold_idx + 1
        
        self.df['fold'] = self.df['scaffold'].map(scaffold_to_fold)
        
        print(f"\nSaving fold CSV files...")
        fold_dataframes = {}
        
        for fold_id in range(n_splits):
            fold_df = self.df[self.df['fold'] == fold_id + 1].copy()
            fold_df_output = fold_df.drop(columns=['scaffold', 'fold'])
            
            output_file = self.output_dir / f"fold_{fold_id + 1}.csv"
            fold_df_output.to_csv(output_file, index=False)
            fold_dataframes[fold_id] = fold_df
            
            print(f"Saved fold_{fold_id + 1}.csv: {len(fold_df):,} rows")
        
        scaffold_assignments_data = []
        for fold_id in range(n_splits):
            for scaffold in fold_assignments[fold_id]:
                scaffold_assignments_data.append({
                    'scaffold': scaffold,
                    'fold': fold_id + 1,
                    'datapoint_count': scaffold_sizes[scaffold]
                })
        
        scaffold_assignments_df = pd.DataFrame(scaffold_assignments_data)
        scaffold_assignments_df = scaffold_assignments_df.sort_values(
            ['fold', 'datapoint_count'], ascending=[True, False]
        )
        
        scaffold_assignments_file = self.output_dir / "figures/scaffold_assignments.csv"
        scaffold_assignments_df.to_csv(scaffold_assignments_file, index=False)
        print(f"\nSaved scaffold assignments to: scaffold_assignments.csv")
        print(f"Total scaffolds: {len(scaffold_assignments_df):,}")
        print(f"Columns: scaffold, fold, datapoint_count")
        
        print("\nVerifying scaffold separation...")
        all_fold_scaffolds = [set(fold_assignments[i]) for i in range(n_splits)]
        has_overlap = False
        for i in range(n_splits):
            for j in range(i + 1, n_splits):
                overlap = all_fold_scaffolds[i] & all_fold_scaffolds[j]
                if overlap:
                    print(f"ERROR: Overlap between fold {i+1} and fold {j+1}: {len(overlap)} scaffolds")
                    has_overlap = True
        
        if not has_overlap:
            print(f"No overlap - all scaffolds are uniquely assigned")
        
        all_assigned = set()
        for fold_id in range(n_splits):
            all_assigned.update(fold_assignments[fold_id])
        
        if len(all_assigned) == len(scaffold_sizes):
            print(f"All {len(scaffold_sizes):,} scaffolds assigned to folds")
        else:
            missing = set(scaffold_sizes.keys()) - all_assigned
            print(f"WARNING: {len(missing)} scaffolds not assigned to any fold")
        
        return fold_assignments, scaffold_to_fold, fold_dataframes
    
    def visualize_rt_distributions(self, fold_dataframes):
        """
        Generates an RT (retention time) KDE plot across folds.
        """
        print("\n" + "=" * 70)
        print("GENERATING RT DISTRIBUTION VISUALIZATION")
        print("=" * 70)
        
        # converting RT from seconds to minutes
        valid_rt = self.df['rt'].dropna() / 60.0
        print(f"Data points with valid RT: {len(valid_rt):,}")

        print(f"\nRT Statistics (in minutes):")
        print(f"  Mean: {valid_rt.mean():.2f} minutes")
        print(f"  Median: {valid_rt.median():.2f} minutes")
        print(f"  Min: {valid_rt.min():.2f} minutes")
        print(f"  Max: {valid_rt.max():.2f} minutes")
        print(f"  Std: {valid_rt.std():.2f} minutes")
        
        fig, ax = plt.subplots(figsize=(12, 6))
        
        if fold_dataframes:
            colors = sns.color_palette("husl", len(fold_dataframes))
            
            for fold_id in range(len(fold_dataframes)):
                fold_df = fold_dataframes[fold_id]
                fold_rt = fold_df['rt'].dropna() / 60.0
                
                if len(fold_rt) > 0:
                    sns.kdeplot(data=fold_rt, ax=ax,
                               label=f'Fold {fold_id + 1} (n={len(fold_rt):,})',
                               linewidth=2.5, color=colors[fold_id])
            
            sns.kdeplot(data=valid_rt, ax=ax,
                       label=f'Overall (n={len(valid_rt):,})',
                       linewidth=2, linestyle='--', color='black', alpha=0.7)
        else:
            sns.kdeplot(data=valid_rt, ax=ax, linewidth=2.5, color='blue')
        
        ax.set_xlabel('Retention Time (min)', fontsize=12, fontweight='bold')
        ax.set_ylabel('Density', fontsize=12, fontweight='bold')
        ax.set_title('Retention Time Distribution Across Scaffold Splits', fontsize=14, fontweight='bold')
        ax.set_xlim(0, 135)
        ax.legend(loc='best', frameon=True, fancybox=True, shadow=True)
        ax.grid(alpha=0.3)
        
        plt.tight_layout()
        
        # creating figures subdirectory
        figures_dir = self.output_dir / 'figures'
        figures_dir.mkdir(exist_ok=True)
        
        output_file = figures_dir / 'scaffold_rt.png'
        plt.savefig(output_file, dpi=300, bbox_inches='tight')
        print(f"\nSaved RT distribution visualization to figures/scaffold_rt.png")
        plt.close()
    
    def generate_umap(self, n_samples=None):
        """
        Generates a UMAP visualization of chemical space.
        Uses Morgan fingerprints for molecular representation.
        """
        print("\n" + "=" * 70)
        print("GENERATING UMAP VISUALIZATION")
        print("=" * 70)
        
        if n_samples is not None and len(self.df) > n_samples:
            print(f"Sampling {n_samples:,} datapoints for UMAP...")
            df_sample = self.df.sample(n=n_samples, random_state=42)
        else:
            print(f"Using all {len(self.df):,} datapoints for UMAP...")
            df_sample = self.df
        
        print(f"Generating Morgan fingerprints for {len(df_sample):,} datapoints...")
        
        fps = []
        valid_indices = []
        
        for idx, smiles in tqdm(enumerate(df_sample['compound']), 
                               total=len(df_sample),
                               desc="Generating fingerprints"):
            try:
                mol = Chem.MolFromSmiles(smiles)
                if mol is not None:
                    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
                    fps.append(fp)
                    valid_indices.append(idx)
            except:
                pass
        
        print(f"Generated {len(fps):,} valid fingerprints")
        
        fp_array = np.array([list(fp) for fp in fps])
        df_valid = df_sample.iloc[valid_indices].reset_index(drop=True)
        
        print(f"\nFitting UMAP (this may take a while)...")
        print(f"n_samples: {len(fp_array):,}")
        print(f"n_features: {fp_array.shape[1]}")
        
        reducer = umap.UMAP(
            n_neighbors=15,
            min_dist=0.1,
            n_components=2,
            metric='jaccard',
            random_state=42,
            verbose=True
        )
        
        embedding = reducer.fit_transform(fp_array)
        print(f"UMAP embedding complete")
        
        # create umap visualization
        print("\nGenerating UMAP visualization...")
        fig, ax = plt.subplots(figsize=(14, 10))
        
        if 'fold' in df_valid.columns:
            colors = sns.color_palette("husl", df_valid['fold'].nunique())
            
            for fold_id in sorted(df_valid['fold'].unique()):
                mask = df_valid['fold'] == fold_id
                fold_data = embedding[mask]
                n_compounds = mask.sum()
                
                ax.scatter(fold_data[:, 0], fold_data[:, 1],
                          label=f'Fold {int(fold_id)} (n={n_compounds:,})',
                          alpha=0.6, s=20, c=[colors[int(fold_id)-1]])
        else:
            ax.scatter(embedding[:, 0], embedding[:, 1], 
                      alpha=0.6, s=20, color='blue')
        
        ax.set_title('UMAP Projection of Compound Space (Colored by Scaffold Split)', 
                    fontsize=14, fontweight='bold')
        ax.legend(loc='best', frameon=True, fancybox=True, shadow=True, fontsize=10)
        ax.grid(alpha=0.3)
        
        plt.tight_layout()
        
        # saving results to figures subdirectory
        figures_dir = self.output_dir / 'figures'
        figures_dir.mkdir(exist_ok=True)
        
        output_file = figures_dir / 'scaffold_umap.png'
        plt.savefig(output_file, dpi=300, bbox_inches='tight')
        print(f"Saved UMAP visualization to figures/scaffold_umap.png")
        plt.close()

        return embedding, df_valid


# ===== Main ===== #


def main():
    """
    Main execution function.
    """
    print("\n" + "=" * 80)
    print(" " * 20 + "BEMIS-MURCKO SCAFFOLD SPLIT")
    print(" " * 25 + "Retina Dataset Analysis")
    print("=" * 80)
    
    script_dir = Path(__file__).parent
    data_path = script_dir / INPUT_CSV
    output_dir = script_dir / OUTPUT_DIR
    
    print(f"\nConfiguration:")
    print(f"Script location: {script_dir}")
    print(f"Data path: {data_path}")
    print(f"Output directory: {output_dir}")
    print(f"Number of folds: {N_FOLDS}")
    print(f"Random seed: {RANDOM_SEED}")
    
    if not data_path.exists():
        raise FileNotFoundError(f"Input file not found: {data_path}")
    
    analyzer = ScaffoldAnalyzer(data_path, output_dir)
    
    print("\nLoading dataset...")
    analyzer.load_data(sample_size=None)
    
    analyze_dataset(analyzer.df)
    
    scaffold_sizes = analyzer.extract_scaffolds()
    
    fold_assignments, scaffold_to_fold, fold_dataframes = analyzer.create_scaffold_splits(
        scaffold_sizes, n_splits=N_FOLDS
    )

    analyzer.visualize_rt_distributions(fold_dataframes)
    
    analyzer.generate_umap(n_samples=None)  # Use all datapoints
    
    print("\n" + "=" * 80)
    print(" " * 25 + "5-FOLD SCAFFOLD SPLIT COMPLETE!")
    print("=" * 80)


if __name__ == "__main__":
    main()