| """Attach one-hot node features from nodes_ML to the saved PyG graph. |
| |
| Each feature group is one categorical column; a node has exactly one 1 per |
| group, so every row sums to the number of groups. |
| """ |
|
|
| import duckdb |
| import torch |
|
|
| from node2vec_model import load_graph |
|
|
| from paths import DB_PATH, GRAPH_PATH |
|
|
| DB = DB_PATH |
| GRAPH = GRAPH_PATH |
|
|
| |
| |
| GROUPS = { |
| "n.Study": "Study", |
| "n.Analytics": "Analytics", |
| "n.species": "species", |
| "n.factor1_org": "factor1_org", |
| "n.factor2_org": "factor2_org", |
| "n.color_group": "color_group", |
| "n.color_group_upper1": "color_group_upper1", |
| "split_part(trim(n.factor1, '()'), '|', 2)": "tissue1", |
| "split_part(trim(n.factor2, '()'), '|', 2)": "tissue2", |
| } |
|
|
| data = load_graph(GRAPH) |
| con = duckdb.connect(DB, read_only=True) |
|
|
| select = ", ".join(f'{e} AS "{name}"' for e, name in GROUPS.items()) |
| rows = con.execute(f""" |
| SELECT g.id, {select} |
| FROM graph_nodes g JOIN nodes_ML n ON n.id = g.id |
| ORDER BY g.idx |
| """).fetchall() |
|
|
| ids = [r[0] for r in rows] |
| assert ids == data.node_id, "node order does not match the saved graph" |
|
|
| blocks, names, bounds = [], [], {} |
| offset = 0 |
| for col, group in enumerate(GROUPS.values(), start=1): |
| values = sorted({r[col] for r in rows}) |
| index = {v: i for i, v in enumerate(values)} |
| block = torch.zeros(len(rows), len(values)) |
| block[torch.arange(len(rows)), torch.tensor([index[r[col]] for r in rows])] = 1.0 |
| blocks.append(block) |
| names += [f"{group}={v}" for v in values] |
| bounds[group] = (offset, offset + len(values)) |
| offset += len(values) |
| print(f" {group:<20} {len(values):>4} dims cols {bounds[group]}") |
|
|
| x = torch.cat(blocks, dim=1) |
| data.x = x |
| data.feature_names = names |
| data.feature_groups = bounds |
| torch.save(data, GRAPH) |
|
|
| print(f"\nx = {tuple(x.shape)} {x.dtype}") |
| print(f"row sums: min={x.sum(1).min():.0f} max={x.sum(1).max():.0f} " |
| f"(expected {len(GROUPS)} = one 1 per group)") |
| print(f"all-zero columns: {(x.sum(0) == 0).sum().item()}") |
| print(f"density: {x.mean():.4f}") |
| print(f"saved -> {GRAPH}") |
| print(data) |
|
|
