| """Build, score, train, and evaluate a configured local reaction inventory.""" |
|
|
| import argparse |
| import json |
| import subprocess |
| import sys |
| from pathlib import Path |
| import yaml |
| from dooable.chemistry import read_catalog, reactions, build_graph, descriptor_rewards |
| from dooable.properties import property_rewards |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--config", required=True) |
| args = parser.parse_args() |
| cfg = yaml.safe_load(Path(args.config).read_text()) |
| out = Path(cfg["output"]) |
| out.mkdir(parents=True, exist_ok=True) |
| parents = read_catalog(cfg["parents"], cfg.get("parent_limit")) |
| reagents = read_catalog(cfg["reagents"], cfg.get("reagent_limit")) |
| templates = reactions(cfg["reactions"]) |
| for budget in cfg.get("budgets", [0, 1, 2]): |
| directory = out / f"budget{budget}" |
| directory.mkdir(parents=True, exist_ok=True) |
| graph = build_graph( |
| parents, reagents, templates, budget, cfg.get("max_nodes", 100000) |
| ) |
| graph.save(directory / "graph.json") |
| if cfg.get("property_models"): |
| rewards, scores = property_rewards(graph, cfg["property_models"]) |
| scores.to_csv(directory / "scores.csv", index=False) |
| else: |
| rewards = descriptor_rewards(graph) |
| (directory / "rewards.json").write_text(json.dumps(rewards, indent=2)) |
| run = dict( |
| graph=str(directory / "graph.json"), |
| rewards=str(directory / "rewards.json"), |
| output=str(directory / "benchmark"), |
| methods=cfg.get("methods", ["dooable", "tb_uniform", "exact"]), |
| temperature=cfg.get("temperature", 0.7), |
| steps=cfg.get("steps", 2000), |
| samples=cfg.get("samples", 1000), |
| seeds=cfg.get("seeds", [0]), |
| batch_size=cfg.get("batch_size", 64), |
| ) |
| run_path = directory / "run.yaml" |
| run_path.write_text(yaml.safe_dump(run)) |
| subprocess.run( |
| [ |
| sys.executable, |
| str(Path(__file__).with_name("run_benchmarks.py")), |
| "--config", |
| str(run_path), |
| ], |
| check=True, |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
