File size: 6,392 Bytes
bb0552c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | from __future__ import annotations
import json
import random
from pathlib import Path
import numpy as np
import torch
import trackio
from model import MeshGraphGCN, normalize_adjacency, parameter_count
from safetensors.torch import save_file
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
accuracy_score,
average_precision_score,
confusion_matrix,
f1_score,
precision_score,
recall_score,
roc_auc_score,
)
from torch.nn import functional as F
PROJECT_DIR = Path(__file__).resolve().parent
DATA_DIR = PROJECT_DIR / "data"
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "meshgraph-gcn"
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def metrics(labels: np.ndarray, scores: np.ndarray, threshold: float = 0.5) -> dict:
predictions = scores >= threshold
return {
"accuracy": float(accuracy_score(labels, predictions)),
"roc_auc": float(roc_auc_score(labels, scores)),
"average_precision": float(average_precision_score(labels, scores)),
"precision": float(precision_score(labels, predictions, zero_division=0)),
"recall": float(recall_score(labels, predictions, zero_division=0)),
"f1": float(f1_score(labels, predictions, zero_division=0)),
"confusion_matrix": confusion_matrix(labels, predictions).tolist(),
}
def best_threshold(labels: np.ndarray, scores: np.ndarray) -> float:
candidates = np.quantile(scores, np.linspace(0.05, 0.95, 300))
return float(
max(
candidates,
key=lambda threshold: f1_score(
labels,
scores >= threshold,
zero_division=0,
),
)
)
def main() -> None:
seed_everything(2033)
graph = np.load(DATA_DIR / "meshgraph.npz")
features = graph["features"].astype(np.float32)
labels = graph["labels"].astype(np.int64)
train_indices = graph["train_indices"]
validation_indices = graph["validation_indices"]
test_indices = graph["test_indices"]
mean = features[train_indices].mean(axis=0)
scale = np.maximum(features[train_indices].std(axis=0), 1e-5)
features = (features - mean) / scale
feature_tensor = torch.from_numpy(features)
label_tensor = torch.from_numpy(labels)
adjacency = torch.from_numpy(graph["adjacency"].astype(np.float32))
normalized = normalize_adjacency(adjacency)
baseline = LogisticRegression(
class_weight="balanced",
max_iter=2000,
random_state=2033,
)
baseline.fit(features[train_indices], labels[train_indices])
baseline_validation = baseline.predict_proba(features[validation_indices])[:, 1]
baseline_threshold = best_threshold(
labels[validation_indices],
baseline_validation,
)
baseline_test = baseline.predict_proba(features[test_indices])[:, 1]
model = MeshGraphGCN(features=features.shape[1])
positive_weight = (labels[train_indices] == 0).sum() / max(
1, (labels[train_indices] == 1).sum()
)
class_weights = torch.tensor([1.0, positive_weight], dtype=torch.float32)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.01, weight_decay=0.002)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=350)
best_validation_auc = -1.0
best_epoch = 0
best_state = None
trackio.init(
project="meshgraph-gcn",
name="two-layer-gcn-v1",
config={
"parameters": parameter_count(model),
"nodes": len(labels),
"edges": int(adjacency.sum().item() // 2),
"train_labels": len(train_indices),
"transductive": True,
},
)
for epoch in range(1, 351):
model.train()
logits = model(feature_tensor, normalized)
loss = F.cross_entropy(
logits[train_indices],
label_tensor[train_indices],
weight=class_weights,
)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
scheduler.step()
model.eval()
with torch.no_grad():
probabilities = model(feature_tensor, normalized).softmax(dim=1)[:, 1]
validation_auc = roc_auc_score(
labels[validation_indices],
probabilities[validation_indices].numpy(),
)
if validation_auc > best_validation_auc:
best_validation_auc = validation_auc
best_epoch = epoch
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
if epoch == 1 or epoch % 10 == 0:
trackio.log(
{
"epoch": epoch,
"train_loss": float(loss.detach()),
"validation_roc_auc": validation_auc,
"learning_rate": scheduler.get_last_lr()[0],
}
)
trackio.finish()
assert best_state is not None
model.load_state_dict(best_state)
model.eval()
with torch.no_grad():
gcn_scores = model(feature_tensor, normalized).softmax(dim=1)[:, 1].numpy()
gcn_threshold = best_threshold(
labels[validation_indices],
gcn_scores[validation_indices],
)
results = {
"model": "MeshGraph GCN",
"parameters": parameter_count(model),
"nodes": len(labels),
"edges": int(adjacency.sum().item() // 2),
"best_epoch": best_epoch,
"best_validation_roc_auc": best_validation_auc,
"gcn_threshold": gcn_threshold,
"gcn_test": metrics(
labels[test_indices],
gcn_scores[test_indices],
gcn_threshold,
),
"feature_only_logistic_test": metrics(
labels[test_indices],
baseline_test,
baseline_threshold,
),
}
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
save_file(model.state_dict(), ARTIFACT_DIR / "model.safetensors")
np.savez(
ARTIFACT_DIR / "preprocessing.npz",
mean=mean,
scale=scale,
)
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(results, indent=2),
encoding="utf-8",
)
print(json.dumps(results, indent=2))
if __name__ == "__main__":
main()
|