File size: 6,714 Bytes
81ae663 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | """Command-line entry points for data, training, generation, and verification."""
import argparse, json
from pathlib import Path
import numpy as np
from .graph import Graph, toy_graph, grid_graph
from .exact import (
solve,
sample,
endpoint_distribution,
expected_cost,
uniform_policy,
tilted_reference,
)
def main():
p = argparse.ArgumentParser(prog="dooable")
sub = p.add_subparsers(dest="command", required=True)
b = sub.add_parser("build")
b.add_argument("--parents", required=True)
b.add_argument("--reagents", required=True)
b.add_argument("--reactions", required=True)
b.add_argument("--budget", type=int, default=2)
b.add_argument("--max-nodes", type=int, default=100000)
b.add_argument("--parent-limit", type=int)
b.add_argument("--output", required=True)
t = sub.add_parser("toy")
t.add_argument("--kind", choices=["multiplicity", "grid"], default="multiplicity")
t.add_argument("--output", required=True)
t.add_argument("--budget", type=int, default=6)
for name in ["exact", "train"]:
a = sub.add_parser(name)
a.add_argument("--graph", required=True)
a.add_argument("--rewards")
a.add_argument("--temperature", type=float, default=1.0)
a.add_argument("--output", required=True)
a.add_argument("--seed", type=int, default=0)
if name == "train":
a.add_argument("--steps", type=int, default=2000)
a.add_argument("--batch-size", type=int, default=64)
a.add_argument(
"--backward",
choices=["learned", "uniform", "exact", "unnormalized"],
default="learned",
)
a.add_argument("--resume")
a.add_argument("--initialize")
a = sub.add_parser("sample")
a.add_argument("--model", required=True)
a.add_argument("--n", type=int, default=1000)
a.add_argument("--seed", type=int, default=0)
a.add_argument("--output", required=True)
a = sub.add_parser("fit-properties")
a.add_argument("--data", default="data/downloads")
a.add_argument("--output", required=True)
a.add_argument("--seed", type=int, default=0)
a = sub.add_parser("score")
a.add_argument("--graph", required=True)
a.add_argument("--models")
a.add_argument("--output", required=True)
a = sub.add_parser("replay")
a.add_argument("--samples", required=True)
a.add_argument("--budget", type=int, required=True)
args = p.parse_args()
if args.command == "build":
from .chemistry import read_catalog, reactions, build_graph
g = build_graph(
read_catalog(args.parents, args.parent_limit),
read_catalog(args.reagents),
reactions(args.reactions),
args.budget,
args.max_nodes,
)
g.save(args.output)
print(
json.dumps(
{
"nodes": len(g.nodes),
"edges": len(g.edges),
"outcomes": len(g.terminals),
}
)
)
return
if args.command == "toy":
g = toy_graph() if args.kind == "multiplicity" else grid_graph(6, args.budget)
g.save(args.output)
return
if args.command in ["exact", "train", "score"]:
g = Graph.load(args.graph)
if args.command == "score":
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
if args.models:
from .properties import property_rewards
rewards, scores = property_rewards(g, args.models)
scores.to_csv(Path(args.output).with_suffix(".csv"), index=False)
else:
from .chemistry import descriptor_rewards
rewards = descriptor_rewards(g)
Path(args.output).write_text(json.dumps(rewards, indent=2))
return
rewards = (
json.loads(Path(args.rewards).read_text())
if args.rewards
else {y: 0.0 for y in g.terminals}
)
out = Path(args.output)
out.mkdir(parents=True, exist_ok=True)
if args.command == "exact":
sol = solve(g, rewards, args.temperature)
g.save(out / "graph.json")
np.save(out / "forward.npy", sol.forward)
(out / "solution.json").write_text(
json.dumps(
{
"temperature": args.temperature,
"target": sol.target,
"log_z": sol.log_z,
"expected_cost": expected_cost(g, sol.forward),
},
indent=2,
)
)
else:
from .learning import train
_, h = train(
g,
rewards,
args.temperature,
args.steps,
args.batch_size,
seed=args.seed,
output=out,
backward=args.backward,
resume=args.resume,
initialize=args.initialize,
)
print(json.dumps(h[-1]))
return
if args.command == "sample":
d = Path(args.model)
if (d / "forward.npy").exists():
g = Graph.load(d / "graph.json")
forward = np.load(d / "forward.npy")
else:
from .learning import load_model
model, _ = load_model(d)
g = model.graph
forward = model.probabilities()
rows = sample(g, forward, args.n, args.seed)
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
Path(args.output).write_text("".join(json.dumps(r) + "\n" for r in rows))
return
if args.command == "fit-properties":
from .properties import fit_property
print(
json.dumps(
[
fit_property(n, args.data, args.output, args.seed)
for n in ["caco2", "bace"]
],
indent=2,
)
)
return
if args.command == "replay":
from .chemistry import replay
rows = [json.loads(l) for l in Path(args.samples).read_text().splitlines() if l]
passed = sum(replay(r, args.budget) for r in rows)
print(
json.dumps(
{
"paths": len(rows),
"replayed": passed,
"fraction": passed / max(len(rows), 1),
}
)
)
if passed != len(rows):
raise SystemExit(1)
if __name__ == "__main__":
main()
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