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"""Tiny synthetic round-trip test for CompactGraphDataset.
The test creates only a few dozen tensor values in a temporary directory. It
does not read any production dataset.
"""
from __future__ import annotations
import json
import pickle
import subprocess
import sys
import tempfile
import unittest
from pathlib import Path
from typing import Dict, List, Sequence, Tuple
import torch
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
from compact_graph_dataset import CompactGraphDataset
CUTOFF = 2.5
def _upper_edges(pos: torch.Tensor, n_protein: int) -> Tuple[torch.Tensor, torch.Tensor]:
pairs: List[Tuple[int, int]] = []
nonpp: List[Tuple[int, int]] = []
for src in range(pos.shape[0]):
for dst in range(src + 1, pos.shape[0]):
distance = torch.sqrt(
torch.sum(
(pos[src].to(torch.float64) - pos[dst].to(torch.float64)) ** 2
)
)
if float(distance) <= CUTOFF:
if dst < n_protein:
pairs.append((src, dst))
else:
nonpp.append((src, dst))
pp_tensor = (
torch.tensor(pairs, dtype=torch.int32).t().contiguous()
if pairs
else torch.empty((2, 0), dtype=torch.int32)
)
nonpp_tensor = (
torch.tensor(nonpp, dtype=torch.int32).t().contiguous()
if nonpp
else torch.empty((2, 0), dtype=torch.int32)
)
return pp_tensor, nonpp_tensor
def _legacy_graph(
static: torch.Tensor,
dynamic: torch.Tensor,
protein: torch.Tensor,
ligand: torch.Tensor,
native: torch.Tensor,
pp_upper: torch.Tensor,
nonpp_upper: torch.Tensor,
) -> Data:
n_protein = protein.shape[0]
n = static.shape[0]
x = torch.empty((n, 82), dtype=torch.float32)
x[:, :34] = static[:, :34]
x[:, 34:61] = dynamic[:, :27]
x[:, 61:71] = static[:, 34:44]
x[:, 71:82] = dynamic[:, 27:38]
pos = torch.cat((protein, ligand), dim=0)
y_grt = torch.cat((protein, native), dim=0)
is_protein = torch.zeros((n, 1), dtype=torch.float32)
is_protein[:n_protein] = 1
y_true = torch.zeros((n, 1), dtype=torch.float32)
y_true[n_protein:, 0] = torch.sqrt(
torch.sum((ligand - native) ** 2, dim=1)
)
upper = torch.cat((pp_upper.to(torch.int64), nonpp_upper.to(torch.int64)), dim=1)
src = torch.cat((upper[0], upper[1]))
dst = torch.cat((upper[1], upper[0]))
distance = torch.sqrt(
torch.sum(
(
pos[upper[0]].to(torch.float64)
- pos[upper[1]].to(torch.float64)
)
** 2,
dim=1,
)
)
attr0 = torch.cat(((distance / CUTOFF).float(), (distance / CUTOFF).float()))
attr1 = torch.cat((torch.exp(-distance / 3).float(), torch.exp(-distance / 3).float()))
order = torch.argsort(src * n + dst)
src, dst = src[order], dst[order]
edge_index = torch.stack((src, dst))
edge_attr = torch.stack(
(
attr0[order],
attr1[order],
(src < n_protein).float(),
(dst < n_protein).float(),
),
dim=1,
)
return Data(
x=x,
edge_index=edge_index,
edge_attr=edge_attr,
pos=pos,
is_protein=is_protein,
y_true=y_true,
y_pred=pos,
y_grt=y_grt,
num_nodes=n,
)
def _make_dataset(root: Path) -> Sequence[Data]:
generator = torch.Generator().manual_seed(17)
protein_a = torch.tensor(
[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
dtype=torch.float32,
)
native_a = torch.tensor([[1.4, 1.1, 0.0], [2.0, 1.0, 0.0]], dtype=torch.float32)
ligands_a = [
native_a + torch.tensor([[0.1, 0.0, 0.0], [0.0, -0.2, 0.1]]),
native_a + torch.tensor([[-0.2, 0.1, 0.0], [0.2, 0.0, -0.1]]),
]
protein_b = torch.tensor([[10.0, 0.0, 0.0], [11.0, 0.0, 0.0]], dtype=torch.float32)
native_b = torch.tensor([[10.5, 1.0, 0.0]], dtype=torch.float32)
ligands_b = [native_b + torch.tensor([[0.0, 0.2, -0.1]])]
systems = [
(protein_a, native_a, ligands_a),
(protein_b, native_b, ligands_b),
]
static_parts = [
torch.randn((protein.shape[0] + native.shape[0], 44), generator=generator)
for protein, native, _ in systems
]
pp_parts: List[torch.Tensor] = []
for protein, native, _ in systems:
pp, _ = _upper_edges(torch.cat((protein, native), dim=0), protein.shape[0])
pp_parts.append(pp)
# Local pose order: A0, A1, B0. Original/source order: B0, A0, A1.
pose_system = torch.tensor([0, 0, 1], dtype=torch.int32)
source_graph_index = torch.tensor([1, 2, 0], dtype=torch.int64)
dynamic_parts: List[torch.Tensor] = []
ligand_parts: List[torch.Tensor] = []
nonpp_parts: List[torch.Tensor] = []
local_graphs: List[Data] = []
for system_index, (_, _, ligands) in enumerate(systems):
protein, native, _ = systems[system_index]
for ligand in ligands:
n = protein.shape[0] + ligand.shape[0]
dynamic = torch.randn((n, 38), generator=generator)
_, nonpp = _upper_edges(torch.cat((protein, ligand), dim=0), protein.shape[0])
dynamic_parts.append(dynamic)
ligand_parts.append(ligand)
nonpp_parts.append(nonpp)
local_graphs.append(
_legacy_graph(
static_parts[system_index],
dynamic,
protein,
ligand,
native,
pp_parts[system_index],
nonpp,
)
)
def pointer(lengths: Sequence[int]) -> torch.Tensor:
result = [0]
for length in lengths:
result.append(result[-1] + int(length))
return torch.tensor(result, dtype=torch.int64)
shard: Dict[str, torch.Tensor] = {
"schema_version": torch.tensor([1], dtype=torch.int32),
"system_graph_ptr": torch.tensor([0, 2, 3], dtype=torch.int64),
"pose_system": pose_system,
"source_graph_index": source_graph_index,
"system_node_ptr": pointer([part.shape[0] for part in static_parts]),
"n_protein": torch.tensor(
[protein.shape[0] for protein, _, _ in systems], dtype=torch.int32
),
"x_static": torch.cat(static_parts, dim=0),
"protein_ptr": pointer([protein.shape[0] for protein, _, _ in systems]),
"protein_pos": torch.cat([protein for protein, _, _ in systems], dim=0),
"native_ligand_ptr": pointer([native.shape[0] for _, native, _ in systems]),
"native_ligand_pos": torch.cat([native for _, native, _ in systems], dim=0),
"pose_node_ptr": pointer([part.shape[0] for part in dynamic_parts]),
"x_dynamic": torch.cat(dynamic_parts, dim=0),
"pose_ligand_ptr": pointer([part.shape[0] for part in ligand_parts]),
"ligand_pos": torch.cat(ligand_parts, dim=0),
"pp_edge_ptr": pointer([part.shape[1] for part in pp_parts]),
"pp_edge_upper": torch.cat(pp_parts, dim=1),
"nonpp_edge_ptr": pointer([part.shape[1] for part in nonpp_parts]),
"nonpp_edge_upper": torch.cat(nonpp_parts, dim=1),
}
(root / "shards").mkdir()
torch.save(shard, root / "shards" / "shard_00000.pt")
manifest = {
"format": "gnncp_compact_v1",
"schema_version": 1,
"cutoff": CUTOFF,
"num_graphs": 3,
"static_columns": [[0, 34], [61, 71]],
"dynamic_columns": [[34, 61], [71, 82]],
"shards": [
{
"path": "shards/shard_00000.pt",
"num_graphs": 3,
"system_ids": ["system_a", "system_b"],
}
],
"graph_map": [[0, 2], [0, 0], [0, 1]],
}
(root / "manifest.json").write_text(json.dumps(manifest), encoding="utf-8")
return [local_graphs[2], local_graphs[0], local_graphs[1]]
class CompactGraphDatasetTest(unittest.TestCase):
def test_round_trip_and_batch(self) -> None:
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
references = _make_dataset(root)
dataset = CompactGraphDataset(root)
self.assertEqual(len(dataset), 3)
for index, reference in enumerate(references):
actual = dataset[index]
for field in (
"x",
"edge_index",
"edge_attr",
"pos",
"is_protein",
"y_true",
"y_pred",
"y_grt",
):
self.assertTrue(
torch.equal(getattr(actual, field), getattr(reference, field)),
msg=f"mismatch at graph={index}, field={field}",
)
self.assertEqual(dataset.metadata(index)["source_graph_index"], index)
batch = next(iter(DataLoader(dataset, batch_size=2, shuffle=False)))
self.assertEqual(batch.x.shape[1], 82)
self.assertEqual(batch.edge_attr.shape[1], 4)
self.assertEqual(batch.num_graphs, 2)
# DataLoader spawn/fork must not serialise mmap shard objects.
restored = pickle.loads(pickle.dumps(dataset))
self.assertEqual(len(restored._cache), 0)
self.assertTrue(torch.equal(restored[-1].x, references[-1].x))
def test_converter_cli_round_trip(self) -> None:
with tempfile.TemporaryDirectory() as temporary:
root = Path(temporary)
seed_root = root / "seed"
seed_root.mkdir()
references = _make_dataset(seed_root)
legacy = root / "legacy.pt"
system_index = root / "system_index.json"
output = root / "converted"
torch.save(list(references), legacy)
system_index.write_text(
json.dumps(
{"graph_to_system": ["system_b", "system_a", "system_a"]}
),
encoding="utf-8",
)
script = Path(__file__).with_name("convert_to_compact_v1.py")
subprocess.run(
[
sys.executable,
str(script),
"--input",
str(legacy),
"--output-dir",
str(output),
"--method",
"synthetic",
"--system-index",
str(system_index),
"--target-shard-mib",
"1",
"--cutoff",
str(CUTOFF),
],
check=True,
cwd=script.parent,
capture_output=True,
text=True,
)
dataset = CompactGraphDataset(output)
self.assertEqual(len(dataset), len(references))
for index, reference in enumerate(references):
actual = dataset[index]
for field in (
"x",
"edge_index",
"edge_attr",
"pos",
"is_protein",
"y_true",
"y_pred",
"y_grt",
):
self.assertTrue(
torch.equal(getattr(actual, field), getattr(reference, field)),
msg=f"writer round-trip mismatch graph={index}, field={field}",
)
self.assertEqual(dataset.metadata(index)["source_graph_index"], index)
if __name__ == "__main__":
unittest.main()
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