File size: 2,617 Bytes
1ed4922 | 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 | from __future__ import annotations
import json
from pathlib import Path
import gradio as gr
import numpy as np
import plotly.graph_objects as go
import torch
from model import MeshGraphGAT
from safetensors.torch import load_file
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "meshgraph-gat"
GRAPH = np.load(PROJECT_DIR / "data" / "meshgraph.npz")
PREPROCESSING = np.load(ARTIFACT_DIR / "preprocessing.npz")
FEATURES = torch.from_numpy(
(
GRAPH["features"].astype(np.float32) - PREPROCESSING["mean"]
)
/ PREPROCESSING["scale"]
)
ADJACENCY = torch.from_numpy(GRAPH["adjacency"].astype(np.float32))
MODEL = MeshGraphGAT(FEATURES.shape[1])
MODEL.load_state_dict(load_file(ARTIFACT_DIR / "model.safetensors"))
MODEL.eval()
REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8"))
@torch.inference_mode()
def inspect_node(node: int) -> tuple[go.Figure, dict]:
node = int(node)
logits, attention = MODEL(FEATURES, ADJACENCY, return_attention=True)
risk = torch.softmax(logits, dim=1)[node, 1]
neighbors = torch.nonzero(ADJACENCY[node] > 0).flatten()
weights = attention[:, node, neighbors].mean(0)
order = torch.argsort(weights, descending=True)
neighbor_ids = neighbors[order].tolist()
sorted_weights = weights[order].tolist()
figure = go.Figure(go.Bar(x=[str(value) for value in neighbor_ids], y=sorted_weights))
figure.update_layout(
template="plotly_dark",
title=f"Mean four-head attention from node {node}",
xaxis_title="Neighbor node",
yaxis_title="Attention",
)
metrics = {
"node": node,
"compromise_probability": float(risk),
"predicted_compromised": bool(
float(risk) >= float(PREPROCESSING["threshold"])
),
"true_label": int(GRAPH["labels"][node]),
"degree": len(neighbor_ids),
"verified_test_roc_auc": REPORT["gat_test"]["roc_auc"],
}
return figure, metrics
with gr.Blocks(title="MeshGraph GAT") as demo:
gr.Markdown(
"# MeshGraph GAT\n"
"Inspect which enterprise communication edges a four-head graph-attention "
"network uses when estimating compromise risk."
)
node = gr.Slider(0, len(FEATURES) - 1, value=25, step=1, label="Node")
initial = inspect_node(25)
chart = gr.Plot(value=initial[0])
metrics = gr.JSON(value=initial[1])
button = gr.Button("Inspect attention", variant="primary")
button.click(inspect_node, inputs=node, outputs=[chart, metrics])
if __name__ == "__main__":
demo.launch()
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