full upload so not to lose anything important

tools/evaluate_roc.py

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+import pickle
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import sem, t
+from sklearn.metrics import auc
+
+
+# Confidence interval function
+def confidence_interval(data, confidence=0.95):
+    n = len(data)
+    mean = np.mean(data)
+    std_err = sem(data)
+    h = std_err * t.ppf((1 + confidence) / 2.0, n - 1)
+    return mean, h
+
+
+# Load ROC and AUC values from pickle files
+roc_data = []
+auc_scores = []
+isoforest_roc_data = []
+isoforest_auc_scores = []
+
+results_path = Path(
+    "/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/log/DeepSAD/subter_kfold_0_0"
+)
+
+for i in range(5):
+    with (results_path / f"results_{i}.pkl").open("rb") as f:
+        data = pickle.load(f)
+        roc_data.append(data["test_roc"])
+        auc_scores.append(data["test_auc"])
+    with (results_path / f"results.isoforest_{i}.pkl").open("rb") as f:
+        data = pickle.load(f)
+        isoforest_roc_data.append(data["test_roc"])
+        isoforest_auc_scores.append(data["test_auc"])
+
+# Calculate mean and confidence interval for AUC scores
+mean_auc, auc_ci = confidence_interval(auc_scores)
+
+# Combine ROC curves
+mean_fpr = np.linspace(0, 1, 100)
+tprs = []
+
+for fpr, tpr, _ in roc_data:
+    interp_tpr = np.interp(mean_fpr, fpr, tpr)
+    interp_tpr[0] = 0.0
+    tprs.append(interp_tpr)
+
+mean_tpr = np.mean(tprs, axis=0)
+mean_tpr[-1] = 1.0
+std_tpr = np.std(tprs, axis=0)
+
+# Plot ROC curves with confidence margins
+plt.figure()
+plt.plot(
+    mean_fpr,
+    mean_tpr,
+    color="b",
+    label=f"Mean ROC (AUC = {mean_auc:.2f} ± {auc_ci:.2f})",
+)
+plt.fill_between(
+    mean_fpr,
+    mean_tpr - std_tpr,
+    mean_tpr + std_tpr,
+    color="b",
+    alpha=0.2,
+    label="± 1 std. dev.",
+)
+
+# Plot each fold's ROC curve (optional)
+ for i, (fpr, tpr, _) in enumerate(roc_data):
+     plt.plot(fpr, tpr, lw=1, alpha=0.3, label=f"Fold {i + 1} ROC")
+
+# Labels and legend
+plt.plot([0, 1], [0, 1], "k--", label="Chance")
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("ROC Curve with 5-Fold Cross-Validation")
+plt.legend(loc="lower right")
+plt.savefig("roc_curve_0_0.png")