From 2b4900d835526ebfd1009f56c2725d24d3f712bb Mon Sep 17 00:00:00 2001 From: Marco Reverenna Date: Mon, 6 Apr 2026 13:54:56 +0200 Subject: [PATCH 1/3] fix(grid-search): correct Composite Score formula and repair evaluate_combination Replace AQS weights (0.35/0.25/0.25/0.15) with the Composite Score formula as defined in Reverenna et al., bioRxiv 2025: 0.5 * coverage + 0.3 * N50 + 0.1 * (1 - scaffolds_count) + 0.1 * max_length Additional fixes: - add max_length to evaluate_combination return dict - fix FileNotFoundError when output_folder='' via tempfile.TemporaryDirectory - fix numpy int64 JSON serialization with .item() - add _build_instanexus_command for ready-to-run CLI output --- scripts/optimization/grid_search.py | 119 +++++++++++++++++++--------- 1 file changed, 82 insertions(+), 37 deletions(-) diff --git a/scripts/optimization/grid_search.py b/scripts/optimization/grid_search.py index 5918766..e34baa0 100755 --- a/scripts/optimization/grid_search.py +++ b/scripts/optimization/grid_search.py @@ -38,6 +38,7 @@ import json import logging import sys +import tempfile import time from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path @@ -82,32 +83,32 @@ def load_grid_params(json_path: Path, mode: str) -> List[Dict[str, Any]]: def compute_final_ranking(df_results: pd.DataFrame) -> pd.DataFrame: - """ - Applies MinMax scaling to normalize metrics and computes the Composite Score. - - WEIGHTING STRATEGY: 'Aggressive Consolidation Split' - ---------------------------------------------------- - 1. Coverage (0.35): DOMINANT. - Rationale: The primary goal is to recover the protein sequence. High N50 - is useless if we only recover 10% of the target. - - 2. N50 (0.25) & Scaffold Count (0.25): STRUCTURAL (50% Combined). - Rationale: We strongly penalize fragmentation. We want the algorithm to - prioritize merging contigs into longer, fewer scaffolds over keeping - them separate to maximize local identity. - - 3. Mean Identity (0.15): QUALITY. - Rationale: Lower weight because input data is usually pre-filtered - (e.g., >80% identity during mapping). Differences between 95% and 99% - are less critical than differences in coverage or fragmentation. + """Applies MinMax scaling and computes the Composite Score for ranking. + + As defined in Reverenna et al., bioRxiv 2025. + + Formula: + composite_score = 0.5 * coverage_norm + + 0.3 * N50_norm + + 0.1 * (1 - scaffolds_count_norm) # inverted: fewer = better + + 0.1 * max_length_norm + + Note: mean_identity is collected for reporting but is NOT part of this + formula. Do not add it here. For benchmarking against other tools use the + AQS formula defined in Reverenna et al., MCP 2026. + + Args: + df_results: DataFrame with one row per parameter combination. + + Returns: + df_results with a composite_score column, sorted descending. """ if df_results.empty: return df_results - # Metrics to use for scoring - metrics = ["coverage", "N50", "mean_identity", "scaffolds_count"] + # Metrics used in the Composite Score (Reverenna et al., bioRxiv 2025) + metrics = ["coverage", "N50", "scaffolds_count", "max_length"] - # Check if metrics exist available_metrics = [m for m in metrics if m in df_results.columns] if len(available_metrics) != len(metrics): logger.warning("Some metrics missing from results. Skipping scoring.") @@ -115,26 +116,22 @@ def compute_final_ranking(df_results: pd.DataFrame) -> pd.DataFrame: df_scoring = df_results[metrics].copy().fillna(0) - # Normalize (0 to 1) + # Normalize all metrics to [0, 1] scaler = MinMaxScaler() scaled_values = scaler.fit_transform(df_scoring) df_scaled = pd.DataFrame(scaled_values, columns=metrics, index=df_results.index) - # Invert 'scaffolds_count' because fewer is better. - # Formula: 1 - normalized_val (where 1 becomes best/fewest, 0 becomes worst/most) + # Invert scaffolds_count: fewer assembled sequences = better df_scaled["scaffolds_count"] = 1 - df_scaled["scaffolds_count"] - # Define Weights - weights = {"coverage": 0.35, "N50": 0.25, "scaffolds_count": 0.25, "mean_identity": 0.15} + # Composite Score weights (Reverenna et al., bioRxiv 2025) + weights = {"coverage": 0.5, "N50": 0.3, "scaffolds_count": 0.1, "max_length": 0.1} - # Calculate Weighted Sum composite_scores = df_scaled[list(weights.keys())].dot(pd.Series(weights)) - # Merge back df_final = df_results.copy() df_final["composite_score"] = composite_scores - # Sort by score descending return df_final.sort_values(by="composite_score", ascending=False) @@ -193,18 +190,20 @@ def evaluate_combination( df_mapped = visualization.create_dataframe_from_mapped_sequences(mapped_scaffolds) - stats = helpers.compute_assembly_statistics( - df=df_mapped, - sequence_type="scaffolds", - output_folder="", # We don't save individual JSONs to save IO/Time - reference=protein_norm, - ) + with tempfile.TemporaryDirectory() as _tmpdir: + stats = helpers.compute_assembly_statistics( + df=df_mapped, + sequence_type="scaffolds", + output_folder=_tmpdir, + reference=protein_norm, + ) return { **params, "scaffolds_count": len(scaffolds), "coverage": stats.get("coverage", 0), "N50": stats.get("N50", 0), + "max_length": stats.get("max_length", 0), "mean_identity": stats.get("mean_identity", 0), "total_mismatches": stats.get("total_mismatches", 0), "duration_sec": round(duration, 2), @@ -216,6 +215,49 @@ def evaluate_combination( return {**params, "error": str(e)} +def _build_instanexus_command( + input_csv: str, + mode: str, + best_params: Dict[str, Any], + best_score: float, +) -> str: + """Builds a ready-to-run instanexus CLI command from the best grid parameters. + + Args: + input_csv: Path to the input CSV used during the search. + mode: Assembly mode (e.g. 'dbg_weighted'). + best_params: Dict of parameter name → best value from the grid. + best_score: Best composite score achieved. + + Returns: + A formatted string with the suggested instanexus command. + """ + # Map grid-search parameter names to instanexus CLI flags + cli_map: Dict[str, str] = { + "fdr": "--fdr", + "kmer_size": "--kmer-size", + "min_overlap": "--min-overlap", + "size_threshold": "--size-threshold", + } + int_params = {"kmer_size", "min_overlap", "size_threshold"} + + parts = [ + "instanexus", + f"--input-csv {input_csv}", + f"--assembly-mode {mode}", + ] + + for key, flag in cli_map.items(): + if key in best_params: + val = int(best_params[key]) if key in int_params else best_params[key] + parts.append(f"{flag} {val}") + + if int(best_params.get("refine_rounds", 0)) > 0: + parts.append("--refine") + + return f"Based on optimization (composite_score={best_score:.3f}), run:\n {' '.join(parts)}" + + def main(): parser = argparse.ArgumentParser(description="Hyperparameter Grid Search for InstaNexus.") parser.add_argument("--input-csv", required=True, help="Path to input cleaned CSV.") @@ -276,7 +318,7 @@ def main(): df_valid = df_results[df_results["error"].isnull()].copy() if not df_valid.empty: - logger.info("Computing final ranking (Aggressive Consolidation Split)...") + logger.info("Computing final ranking (Composite Score, Reverenna et al., bioRxiv 2025)...") df_ranked = compute_final_ranking(df_valid) df_errors = df_results[df_results["error"].notnull()] @@ -296,8 +338,11 @@ def main(): f" Cov: {best['coverage'] * 100:.1f}% | N50: {best['N50']} | Scaffolds: {best['scaffolds_count']}" ) - best_params = {k: best[k] for k in combinations[0].keys() if k in best} + best_params = { + k: best[k].item() if hasattr(best[k], "item") else best[k] for k in combinations[0].keys() if k in best + } logger.info(f" Params: {json.dumps(best_params, indent=2)}") + logger.info(_build_instanexus_command(args.input_csv, args.mode, best_params, best["composite_score"])) else: logger.warning("All runs failed or produced no valid assemblies.") csv_out = output_dir / f"grid_{args.mode}_{run_name}_FAILED.csv" From 5c7b8b963b5e702d094d9671294bdd0319f0ee84 Mon Sep 17 00:00:00 2001 From: Marco Reverenna Date: Mon, 6 Apr 2026 13:55:12 +0200 Subject: [PATCH 2/3] feat(optimize): add instanexus-optimize CLI entry point Add optimize.py as a thin entry point delegating to grid_search.main(). Register instanexus-optimize in [project.scripts] and add scikit-learn>=1.3 as a core dependency (required by MinMaxScaler in the grid search). --- pyproject.toml | 2 + src/instanexus/optimize.py | 21 +++ uv.lock | 270 +++++++++++++++++++++++++++++++++++++ 3 files changed, 293 insertions(+) create mode 100644 src/instanexus/optimize.py diff --git a/pyproject.toml b/pyproject.toml index 7f608cb..eb8d7eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ dependencies = [ "plotly>=6.2.0", "logomaker>=0.8", "networkx>=3.3", + "scikit-learn>=1.3", "upsetplot" ] @@ -58,6 +59,7 @@ Issues = "https://github.com/Multiomics-Analytics-Group/InstaNexus/issues" [project.scripts] instanexus = "instanexus.main:cli" +instanexus-optimize = "instanexus.optimize:cli" # --- TOOL CONFIGURATIONS --- diff --git a/src/instanexus/optimize.py b/src/instanexus/optimize.py new file mode 100644 index 0000000..f652aba --- /dev/null +++ b/src/instanexus/optimize.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +"""CLI entry point for the instanexus-optimize command.""" + +import sys +from pathlib import Path + + +def cli() -> None: + """Launch the hyperparameter grid search optimizer. + + Delegates to scripts/optimization/grid_search.py, which contains + the full implementation. The package entry point exists here so that + `instanexus-optimize` is available after `uv sync`. + """ + _scripts_opt = Path(__file__).resolve().parents[2] / "scripts" / "optimization" + if str(_scripts_opt) not in sys.path: + sys.path.insert(0, str(_scripts_opt)) + + import grid_search # type: ignore[import-not-found] + + grid_search.main() diff --git a/uv.lock b/uv.lock index 9365849..859e4a2 100644 --- a/uv.lock +++ b/uv.lock @@ -832,6 +832,8 @@ dependencies = [ { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "pandas", version = "3.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "plotly" }, + { name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker 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functions from utils.py into analyze_optimization.py: - combine_json_to_csv: walks output dirs, merges JSON stats into a single CSV - plot_coverages_from_runs: barplots of contig/scaffold coverage across runs --- scripts/optimization/analyze_optimization.py | 119 ++++++++++++++++++- 1 file changed, 118 insertions(+), 1 deletion(-) diff --git a/scripts/optimization/analyze_optimization.py b/scripts/optimization/analyze_optimization.py index 8fd1af2..04de107 100644 --- a/scripts/optimization/analyze_optimization.py +++ b/scripts/optimization/analyze_optimization.py @@ -7,11 +7,13 @@ """ import glob +import json import os from pathlib import Path + +import matplotlib.pyplot as plt import pandas as pd import seaborn as sns -import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap # --- CONFIGURATION --- @@ -203,6 +205,121 @@ def save_detailed_rankings(df_best, output_dir, mode_name): final_df.to_csv(out_path, index=False) +def combine_json_to_csv( + run: str, + type_method: str, + type_sequence: str, + base_path: Path = Path("outputs"), +) -> None: + """Walks output directories, reads JSON stats files, and saves a combined CSV. + + Args: + run: Run identifier (e.g. 'bsa', 'ma1'). + type_method: Assembly method prefix used in the JSON filename (e.g. 'scaffolds'). + type_sequence: Sequence type suffix used in the JSON filename (e.g. 'contigs'). + base_path: Root outputs folder. + """ + run_path = Path(base_path) / run + dataframes = [] + files_added = 0 + + for root, dirs, _ in os.walk(run_path): + for dir_name in dirs: + json_path = Path(root) / dir_name / "statistics" / f"{type_method}_{type_sequence}_stats.json" + if json_path.exists(): + try: + with open(json_path) as f: + data = json.load(f) + df = pd.json_normalize(data) + df["source"] = dir_name + dataframes.append(df) + files_added += 1 + except Exception as e: + print(f"Error loading {json_path}: {e}") + + if not dataframes: + print(f"No stats files found under {run_path}.") + return + + combined_df = pd.concat(dataframes, ignore_index=True) + + if "ass_method" in combined_df.columns: + combined_df["ass_method"] = combined_df["ass_method"].fillna("greedy") + + combined_df["sequence_type"] = type_sequence + combined_df["method_type"] = type_method + combined_df["run"] = run + + output_file = run_path / f"{type_sequence}_combined_stats.csv" + combined_df.to_csv(output_file, index=False) + print(f"Combined stats saved to: {output_file} ({files_added} files merged)") + + +def plot_coverages_from_runs( + runs: list, + base_path: Path = Path("outputs"), + combination_folder: str = "", + contigs_json: str = "contigs_stats.json", + scaffolds_json: str = "scaffolds_stats.json", + save: bool = False, + output_dir: Path = Path("."), +) -> None: + """Plots coverage barplots for contigs and scaffolds across multiple runs. + + Args: + runs: List of run identifiers to include (e.g. ['bsa', 'nb1']). + base_path: Root outputs folder. + combination_folder: Sub-folder name for the specific parameter combination. + contigs_json: Filename of the contigs stats JSON (default: contigs_stats.json). + scaffolds_json: Filename of the scaffolds stats JSON (default: scaffolds_stats.json). + save: If True, saves plots as PNG files. + output_dir: Directory where PNG files are saved when save=True. + """ + base_path = Path(base_path) + contig_coverages: list = [] + scaffold_coverages: list = [] + labels: list = [] + + for run in runs: + stats_path = base_path / run / combination_folder / "statistics" + if not stats_path.exists(): + print(f"[{run}] Missing statistics folder: {stats_path}") + continue + + for coverage_list, fname in [(contig_coverages, contigs_json), (scaffold_coverages, scaffolds_json)]: + json_path = stats_path / fname + if json_path.exists(): + try: + with open(json_path) as f: + coverage_list.append(json.load(f).get("coverage", 0)) + except Exception as e: + print(f"[{run}] Error reading {fname}: {e}") + coverage_list.append(0) + else: + print(f"[{run}] {fname} not found.") + coverage_list.append(0) + + labels.append(run) + + for coverages, color, title, suffix in [ + (contig_coverages, "mediumslateblue", "Contigs Coverage per Run", "contigs"), + (scaffold_coverages, "seagreen", "Scaffolds Coverage per Run", "scaffolds"), + ]: + plt.figure(figsize=(10, 4)) + plt.bar(labels, coverages, color=color) + plt.ylabel("Coverage") + plt.title(title) + plt.xticks(rotation=45, ha="right") + plt.tight_layout() + + if save: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + plt.savefig(output_dir / f"{suffix}_coverage.png", dpi=300) + + plt.show() + + def main(): print("--- Starting Optimization Analysis (Seaborn Edition) ---")