| """Evaluate new public-property weights on one stored reaction graph.""" |
|
|
| import argparse, json |
| from pathlib import Path |
| import pandas as pd |
| from dooable.graph import Graph |
| from dooable.properties import property_rewards |
| from dooable.exact import solve, endpoint_distribution, expected_cost |
| from dooable.learning import train |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--graph", required=True) |
| parser.add_argument("--models", required=True) |
| parser.add_argument("--output", default="results/preferences") |
| parser.add_argument("--neural-steps", type=int, default=0) |
| args = parser.parse_args() |
| g = Graph.load(args.graph) |
| out = Path(args.output) |
| out.mkdir(parents=True, exist_ok=True) |
| rows = [] |
| previous = None |
| for beta in [1.0, 2.0, 5.0]: |
| for w in [0.0, 0.25, 0.5, 0.75, 1.0]: |
| rewards, scores = property_rewards( |
| g, args.models, weights=(w, 1 - w), concentration=beta |
| ) |
| directory = out / f"beta{beta:g}_weight{w:g}" |
| directory.mkdir(exist_ok=True) |
| (directory / "rewards.json").write_text(json.dumps(rewards, indent=2)) |
| policies = {"exact": solve(g, rewards, 0.7).forward} |
| if args.neural_steps: |
| model, _ = train( |
| g, |
| rewards, |
| 0.7, |
| steps=args.neural_steps, |
| output=directory, |
| initialize=previous, |
| ) |
| policies["dooable"] = model.probabilities() |
| previous = directory |
| for name, p in policies.items(): |
| masses = endpoint_distribution(g, p) |
| s = scores.set_index("smiles") |
| a = pd.Series(masses).reindex(s.index) |
| rows.append( |
| { |
| "method": name, |
| "bace_weight": w, |
| "concentration": beta, |
| "mean_bace_utility": float(a @ s.bace_utility), |
| "mean_caco2_utility": float(a @ s.caco2_utility), |
| "mean_cost": expected_cost(g, p), |
| } |
| ) |
| pd.DataFrame(rows).to_csv(out / "measurements.csv", index=False) |
| print(f"Saved {len(rows)} preference measurements to {out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
