File size: 6,246 Bytes
16d6869 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | """
Evaluation entry point for extended metrics analysis.
Computes extended evaluation metrics, ROC curves, and statistical tests.
Usage:
python -m brain_gcn.eval_cli --checkpoint <path> --test_metrics
"""
from __future__ import annotations
import argparse
import json
import logging
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from sklearn.metrics import auc
from brain_gcn.main import build_datamodule
from brain_gcn.tasks import ClassificationTask
from brain_gcn.utils.evaluation import (
compute_metrics,
compute_roc_curve,
compute_pr_curve,
compute_confusion_matrix,
StatisticalTester,
)
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
def add_eval_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
"""Add evaluation-specific arguments."""
parser.add_argument(
"--eval_checkpoint",
type=str,
required=True,
help="Path to model checkpoint.",
)
parser.add_argument(
"--eval_output_dir",
type=str,
default="results/evaluation",
help="Output directory for evaluation results.",
)
parser.add_argument(
"--eval_plot_roc",
action="store_true",
help="Save ROC curve plot.",
)
parser.add_argument(
"--eval_plot_pr",
action="store_true",
help="Save Precision-Recall curve plot.",
)
parser.add_argument(
"--eval_bootstrap_ci",
action="store_true",
help="Compute bootstrap confidence intervals.",
)
parser.add_argument(
"--eval_ci_n_bootstrap",
type=int,
default=1000,
help="Number of bootstrap samples.",
)
return parser
def load_checkpoint(
ckpt_path: str | Path,
device: str = "cpu",
) -> ClassificationTask:
"""Load trained model from checkpoint."""
return ClassificationTask.load_from_checkpoint(ckpt_path, map_location=device)
def get_predictions(
model: ClassificationTask,
dm,
device: str = "cpu",
) -> tuple[np.ndarray, np.ndarray]:
"""Get predictions on test set."""
model.eval()
model.to(device)
all_probs = []
all_labels = []
with torch.no_grad():
for bold_windows, adj, labels in dm.test_dataloader():
logits = model(bold_windows.to(device), adj.to(device))
probs = torch.softmax(logits, dim=-1)[:, 1]
all_probs.append(probs.cpu().numpy())
all_labels.append(labels.numpy())
return np.concatenate(all_probs), np.concatenate(all_labels)
def plot_roc(
probs: np.ndarray,
labels: np.ndarray,
output_path: str | Path,
) -> None:
"""Plot and save ROC curve."""
roc_data = compute_roc_curve(probs, labels)
fpr = roc_data["fpr"]
tpr = roc_data["tpr"]
auc_score = roc_data["auc"]
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, label=f"ROC (AUC={auc_score:.4f})", linewidth=2)
plt.plot([0, 1], [0, 1], "k--", label="Random", linewidth=1)
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("ROC Curve")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig(output_path, dpi=150)
plt.close()
log.info(f"ROC curve saved to {output_path}")
def plot_pr(
probs: np.ndarray,
labels: np.ndarray,
output_path: str | Path,
) -> None:
"""Plot and save Precision-Recall curve."""
pr_data = compute_pr_curve(probs, labels)
precision = pr_data["precision"]
recall = pr_data["recall"]
ap = pr_data["ap"]
plt.figure(figsize=(8, 6))
plt.plot(recall, precision, label=f"PR (AP={ap:.4f})", linewidth=2)
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("Precision-Recall Curve")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig(output_path, dpi=150)
plt.close()
log.info(f"PR curve saved to {output_path}")
def main():
from brain_gcn.main import build_parser
parser = build_parser()
parser = add_eval_arguments(parser)
args = parser.parse_args()
output_dir = Path(args.eval_output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Load model and data
log.info(f"Loading checkpoint: {args.eval_checkpoint}")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = load_checkpoint(args.eval_checkpoint, device=device)
log.info("Building datamodule")
dm = build_datamodule(args)
dm.prepare_data()
dm.setup()
# Get predictions
log.info("Generating predictions on test set")
probs, labels = get_predictions(model, dm, device=device)
# Compute metrics
log.info("Computing metrics")
metrics = compute_metrics(probs, labels)
cm = compute_confusion_matrix(probs, labels)
# Print metrics
log.info("\n" + "=" * 70)
log.info("CLASSIFICATION METRICS")
log.info("=" * 70)
for key, value in metrics.to_dict().items():
log.info(f"{key:20s}: {value:.4f}")
log.info("\nConfusion Matrix:")
log.info(f" TP={cm.true_positives}, FP={cm.false_positives}")
log.info(f" FN={cm.false_negatives}, TN={cm.true_negatives}")
# Compute confidence intervals if requested
if args.eval_bootstrap_ci:
log.info(f"\nComputing {args.eval_ci_n_bootstrap} bootstrap samples")
ci_auc = StatisticalTester.bootstrap_ci(
lambda p, l: compute_metrics(p, l).auc,
probs,
labels,
n_bootstrap=args.eval_ci_n_bootstrap,
)
log.info(f"AUC 95% CI: [{ci_auc[0]:.4f}, {ci_auc[2]:.4f}]")
# Save results
results = {
"metrics": metrics.to_dict(),
"confusion_matrix": cm.to_dict(),
}
results_file = output_dir / "metrics.json"
with open(results_file, "w") as f:
json.dump(results, f, indent=2)
log.info(f"\nResults saved to {results_file}")
# Plot ROC and PR curves if requested
if args.eval_plot_roc:
roc_path = output_dir / "roc_curve.png"
plot_roc(probs, labels, roc_path)
if args.eval_plot_pr:
pr_path = output_dir / "pr_curve.png"
plot_pr(probs, labels, pr_path)
if __name__ == "__main__":
main()
|