File size: 4,165 Bytes
916755e | 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 | """Run the documented new-stack workflow on a tiny temporary ROOT sample.
This is an integration smoke test, not a physics example. It requires the
validated ``root-gnn`` extra and uses only the public CLI for preparation,
training, evaluation, and prediction.
"""
from __future__ import annotations
import subprocess
import sys
import tempfile
from pathlib import Path
import awkward as ak
import numpy as np
import uproot
def _run(root: Path, *arguments: str) -> None:
command = [sys.executable, "-m", "gnn4colliders.cli", *arguments]
subprocess.run(command, cwd=root, check=True)
def _write_root(path: Path) -> None:
events = 5
with uproot.recreate(path) as output:
output["Events"] = {
"jet_pt": ak.Array([[40.0, 25.0]] * events),
"jet_eta": ak.Array([[-1.0, 1.0]] * events),
"jet_phi": ak.Array([[-2.5, 2.5]] * events),
"eventNumber": np.arange(events, dtype=np.int64),
"weight": np.ones(events, dtype=np.float32),
}
def _prepare(root_file: Path, cache: Path) -> None:
_run(
root_file.parent,
"prepare",
f"data.files=[{root_file}]",
"data.tree_name=Events",
f"data.cache.path={cache}",
'data.feature_branches=[["jet_pt"],["jet_eta"],["jet_phi"],CALC_E,[1.0],[0.0],NODE_TYPE]',
"data.object_types=[vector]",
"data.scales=[1,1,1,1,1,1,1]",
"data.fold_var=eventNumber",
"data.weight_var=weight",
)
def _train(root: Path, cache: Path, output: Path, *extra: str) -> Path:
_run(
root,
"train",
f"data.cache.path={cache}",
"trainer.max_epochs=1",
"trainer.device=cpu",
"data.batch_size=1",
"model.hid_size=8",
"model.n_layers=1",
"model.n_proc_steps=1",
f"environment.output_root={output}",
*extra,
)
return output / "checkpoints" / "epoch_0000.pt"
def main() -> None:
try:
import dgl # noqa: F401
except ImportError as error: # pragma: no cover - environment-dependent
raise SystemExit(
"install the root-gnn extra before running this smoke test"
) from error
with tempfile.TemporaryDirectory(prefix="gnn4colliders-smoke-") as directory:
root = Path(directory)
root_file = root / "events.root"
cache = root / "graphs.pt"
target_cache = root / "target.pt"
_write_root(root_file)
_prepare(root_file, cache)
pretrained = _train(root, cache, root / "pretrain")
_prepare(root_file, target_cache)
fine_tuned = _train(
root,
target_cache,
root / "finetune",
"model=root_gnn/fine_tuned_edge_network",
"task=binary_classification",
f"checkpoint.pretrained={pretrained}",
"model.freeze_backbone=true",
)
_run(
root,
"evaluate",
f"data.cache.path={target_cache}",
"inference.split=test",
f"inference.checkpoint={fine_tuned}",
"model=root_gnn/fine_tuned_edge_network",
"task=binary_classification",
f"checkpoint.pretrained={pretrained}",
"model.hid_size=8",
"model.n_layers=1",
"model.n_proc_steps=1",
"trainer.device=cpu",
)
prediction = root / "predictions.npz"
_run(
root,
"predict",
f"data.cache.path={target_cache}",
"inference.split=test",
f"inference.checkpoint={fine_tuned}",
"model=root_gnn/fine_tuned_edge_network",
"task=binary_classification",
f"checkpoint.pretrained={pretrained}",
"model.hid_size=8",
"model.n_layers=1",
"model.n_proc_steps=1",
f"inference.output={prediction}",
"trainer.device=cpu",
)
if not prediction.is_file():
raise RuntimeError("smoke workflow did not produce predictions.npz")
print(f"smoke workflow succeeded in {root}")
if __name__ == "__main__":
main()
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