| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| from sklearn.model_selection import train_test_split |
|
|
| PROJECT_DIR = Path(__file__).resolve().parent |
| DATA_DIR = PROJECT_DIR / "data" |
|
|
|
|
| def build_graph(nodes: int = 600, seed: int = 2033) -> dict[str, np.ndarray]: |
| rng = np.random.default_rng(seed) |
| subnets = np.repeat(np.arange(6), nodes // 6) |
| adjacency = np.zeros((nodes, nodes), dtype=np.float32) |
| for left in range(nodes): |
| same_subnet = subnets == subnets[left] |
| probabilities = np.where(same_subnet, 0.045, 0.0025) |
| links = rng.random(nodes) < probabilities |
| links[: left + 1] = False |
| adjacency[left, links] = 1 |
| adjacency = np.maximum(adjacency, adjacency.T) |
|
|
| compromised = np.zeros(nodes, dtype=bool) |
| seeds = rng.choice(nodes, size=14, replace=False) |
| compromised[seeds] = True |
| for _ in range(4): |
| exposure = adjacency @ compromised.astype(np.float32) |
| infection_probability = 1 - np.exp(-0.22 * exposure) |
| new_infections = (rng.random(nodes) < infection_probability) & ~compromised |
| compromised |= new_infections |
|
|
| base = rng.normal(0, 1, (nodes, 8)).astype(np.float32) |
| labels = compromised.astype(np.int64) |
| signal = labels[:, None].astype(np.float32) |
| features = base.copy() |
| features[:, 0:1] += signal * rng.normal(1.0, 0.5, (nodes, 1)) |
| features[:, 1:2] += signal * rng.normal(0.8, 0.6, (nodes, 1)) |
| features[:, 2:3] += signal * rng.normal(0.7, 0.6, (nodes, 1)) |
| features[:, 3:4] += signal * rng.normal(0.5, 0.7, (nodes, 1)) |
| features[:, 4] += subnets * 0.12 |
|
|
| indices = np.arange(nodes) |
| train, remainder = train_test_split( |
| indices, |
| test_size=0.40, |
| stratify=labels, |
| random_state=seed, |
| ) |
| validation, test = train_test_split( |
| remainder, |
| test_size=0.50, |
| stratify=labels[remainder], |
| random_state=seed, |
| ) |
| return { |
| "features": features, |
| "adjacency": adjacency, |
| "labels": labels, |
| "subnets": subnets.astype(np.int64), |
| "train_indices": train, |
| "validation_indices": validation, |
| "test_indices": test, |
| "seed_nodes": seeds.astype(np.int64), |
| } |
|
|
|
|
| def main() -> None: |
| DATA_DIR.mkdir(parents=True, exist_ok=True) |
| graph = build_graph() |
| np.savez_compressed(DATA_DIR / "meshgraph.npz", **graph) |
| manifest = { |
| "nodes": len(graph["labels"]), |
| "edges": int(graph["adjacency"].sum() // 2), |
| "features": graph["features"].shape[1], |
| "subnets": len(np.unique(graph["subnets"])), |
| "compromised_rate": float(graph["labels"].mean()), |
| "train_nodes": len(graph["train_indices"]), |
| "validation_nodes": len(graph["validation_indices"]), |
| "test_nodes": len(graph["test_indices"]), |
| "path": "meshgraph.npz", |
| } |
| (DATA_DIR / "manifest.json").write_text( |
| json.dumps(manifest, indent=2), |
| encoding="utf-8", |
| ) |
| print(json.dumps(manifest, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
