full upload so not to lose anything important

tools/evaluate_prc_singlerun.py

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+import pickle
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from sklearn.metrics import auc
+
+results_path = Path(
+    "/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/log/DeepSAD/subter_selective"
+)
+
+# Paths to your result files
+deepsad_results_file = results_path / "results.pkl"
+isoforest_results_file = results_path / "results_isoforest.pkl"
+
+# Load DeepSAD precision-recall data
+with deepsad_results_file.open("rb") as f:
+    data = pickle.load(f)
+    deep_precision, deep_recall, _ = data["test_prc"]
+
+# Compute AP for DeepSAD
+deep_ap = auc(deep_recall, deep_precision)
+
+# Load IsoForest precision-recall data
+with isoforest_results_file.open("rb") as f:
+    data = pickle.load(f)
+    iso_precision, iso_recall, _ = data["test_prc"]
+
+# Compute AP for IsoForest
+iso_ap = auc(iso_recall, iso_precision)
+
+# Create plot
+plt.figure(figsize=(8, 6))
+
+# Plot DeepSAD PR curve
+plt.plot(deep_recall, deep_precision, color="b", label=f"DeepSAD (AP = {deep_ap:.2f})")
+
+# Plot IsoForest PR curve
+plt.plot(iso_recall, iso_precision, color="r", label=f"IsoForest (AP = {iso_ap:.2f})")
+
+# Labels
+plt.xlabel("Recall")
+plt.ylabel("Precision")
+plt.title("Precision-Recall Curve (Single Run)")
+
+# Add legend
+plt.legend(loc="upper right")
+
+# Save and/or show plot
+plt.savefig("pr_curve_single_run.png")
+plt.show()