Added some helper scripts

Author: liamdugan

detectors/detector.py

@@ -51,7 +51,7 @@ def get_detector(detector_name: str) -> Detector:
         return OriginalityAI()
     elif detector_name == "binoculars":
         return Binoculars()
-    if detector_name == "conda":
+    elif detector_name == "conda":
         return Conda()
     else:
         raise ValueError("Invalid detector name")

run_pred_eval.py

@@ -36,13 +36,13 @@
     print(f"Running detection...")
     results = run_detection(detector.inference, df)
 
-    print(f"Done! Writing predictions to output path: {args.output_path}")
+    print(f"Done! Writing predictions to output path: {args.output_preds_path}")
     with open(args.output_preds_path, "w") as f:
         json.dump(results, f)
 
-    if "label" not in df.columns:
-        print("No labels found in dataframe; evaluation will not be run.")
-        exit(0)
+    # if "label" not in df.columns:
+    #     print("No labels found in dataframe; evaluation will not be run.")
+    #     exit(0)
 
     print("Running evaluation on the predictions...")
     evaluation_result = run_evaluation(results, df)

visualize_results_file.py

@@ -23,6 +23,13 @@
         print(f"{d} -- {accuracy.item()}")
     except Exception:
         pass
+
+# for m in df.model.unique():
+#     accuracy = df[(df['model'] == m) & (df['domain'] == 'all') & (df['attack'] == 'none') & (df['decoding'] == 'all') & (df['repetition_penalty'] == 'all')]['auroc']
+#     try:
+#         print(f"{m} -- {accuracy.item()}")
+#     except Exception:
+#         pass
 #print(df)
 
 # # Conditions