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555972b 744f7db | 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 | import logging
import torch
import numpy as np
from sklearn.metrics import roc_curve, auc, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import torchvision.transforms.functional as TF
from config import Config
from file_io_manager import FileIOManager
logger = logging.getLogger(__name__)
class Evaluator:
def __init__(self, model, criterion, io: FileIOManager | None = None):
self.model = model
self.criterion = criterion
self._device = Config.get_training_config()['device']
self._io = io
def evaluate(self, val_loader, use_tta=False):
self.model.eval()
total_loss = 0.0
all_preds: list[int] = []
all_labels: list[int] = []
all_probs: list[float] = []
with torch.no_grad():
for images_batch_tensor, metadata_batch, labels_batch in tqdm(val_loader, desc="Evaluating"):
metadata_batch = metadata_batch.to(self._device)
all_labels.extend(labels_batch.cpu().numpy().flatten())
for i in range(images_batch_tensor.size(0)):
img_tensor = images_batch_tensor[i]
meta_single = metadata_batch[i:i+1]
label_single = labels_batch[i:i+1].unsqueeze(1).float().to(self._device)
outputs = self.model(img_tensor.unsqueeze(0).to(self._device), meta_single)
loss = self.criterion(outputs, label_single)
total_loss += loss.item()
if use_tta:
probs_tta = [torch.sigmoid(outputs).item()]
hflip_out = self.model(TF.hflip(img_tensor).unsqueeze(0).to(self._device), meta_single)
probs_tta.append(torch.sigmoid(hflip_out).item())
final_prob = float(np.mean(probs_tta))
else:
final_prob = torch.sigmoid(outputs).item()
all_probs.append(final_prob)
all_preds.append(1 if final_prob > 0.5 else 0)
avg_loss = total_loss / len(all_labels) if all_labels else 0.0
return avg_loss, all_preds, all_labels, all_probs
def plot_roc_curve(self, labels, probs):
fpr, tpr, _ = roc_curve(labels, probs)
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc="lower right")
path = self._io.roc_curve_path() if self._io else FileIOManager.for_run("default").roc_curve_path()
plt.savefig(path)
plt.close()
logger.info("ROC curve saved to %s", path)
def plot_confusion_matrix(self, labels, preds):
cm = confusion_matrix(labels, preds)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=['Benign', 'Malignant'],
yticklabels=['Benign', 'Malignant'])
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
path = self._io.confusion_matrix_path() if self._io else FileIOManager.for_run("default").confusion_matrix_path()
plt.savefig(path)
plt.close()
logger.info("Confusion matrix saved to %s", path)
def plot_shap(self, val_loader, feature_names: list[str]) -> None:
"""Generate SHAP feature importance plot for the metadata branch.
Uses KernelExplainer (model-agnostic) to attribute the model's output
to each of the 14 metadata features while holding image features fixed
at their validation-set mean.
"""
import shap
self.model.eval()
# Collect metadata + compute mean image features across validation set
all_metadata: list = []
all_img_feats: list = []
with torch.no_grad():
for img_batch, metadata_batch, _ in val_loader:
all_metadata.append(metadata_batch)
img_feats = self.model.cnn_dropout(
self.model.image_backbone(img_batch.to(self._device))
)
all_img_feats.append(img_feats)
all_metadata_np = torch.cat(all_metadata, dim=0).cpu().numpy()
mean_img_feats = torch.cat(all_img_feats, dim=0).mean(dim=0).unsqueeze(0)
background = shap.kmeans(all_metadata_np, 25)
test_sample = all_metadata_np[:200]
explainer = shap.KernelExplainer(
lambda m: self.model.predict_metadata_proba(m, mean_img_feats),
background,
)
shap_values = explainer.shap_values(test_sample, silent=True)
plt.figure()
shap.summary_plot(
shap_values,
features=test_sample,
feature_names=feature_names,
show=False,
)
io = self._io or FileIOManager.for_run("default")
path = io.shap_plot_path()
plt.savefig(path, bbox_inches='tight')
plt.close()
logger.info("SHAP feature importance plot saved to %s", path)
def compute_ood_stats(self, val_loader) -> None:
"""Compute mean + inverse covariance of backbone features for Mahalanobis OOD detection.
Based on: Lee et al., "A Simple Unified Framework for Detecting
Out-of-Distribution Samples and Adversarial Attacks", NeurIPS 2018.
https://arxiv.org/abs/1807.03888
"""
self.model.eval()
features_list: list[torch.Tensor] = []
with torch.no_grad():
for images, _, _ in tqdm(val_loader, desc="Computing OOD stats"):
images = images.to(self._device)
feats = self.model.cnn_dropout(self.model.image_backbone(images))
features_list.append(feats.cpu())
features = torch.cat(features_list, dim=0).float()
mean = features.mean(dim=0)
centered = features - mean
cov = (centered.T @ centered) / (centered.shape[0] - 1)
cov += 1e-5 * torch.eye(cov.shape[0]) # regularise for invertibility
cov_inv = torch.linalg.inv(cov)
# Compute empirical threshold from validation distances
dists = (centered @ cov_inv * centered).sum(dim=1)
threshold = float(dists.mean() + 3 * dists.std())
logger.info("OOD distances — mean: %.1f, std: %.1f, threshold (μ+3σ): %.1f",
dists.mean(), dists.std(), threshold)
io = self._io or FileIOManager.for_run("default")
io.save_ood_stats(mean, cov_inv, threshold)
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