Spaces:
Sleeping
Sleeping
| import re | |
| class CodeReviewer: | |
| """ | |
| Simulated actor bot that reviews the agent's code edits | |
| for Stage 2 and Stage 3 to detect data leakage and missing scaler. | |
| """ | |
| def review(self, script: str, stage: int) -> str: | |
| script = script or "" | |
| has_scaler = "StandardScaler" in script | |
| # Check if X_scaled = fit_transform(X) happens before train_test_split | |
| # or if scaler is applied to the raw dataframe before splitting | |
| leakage_detected = False | |
| if has_scaler: | |
| if re.search(r"scaler\.fit_transform\(\s*X\s*\)", script): | |
| leakage_detected = True | |
| elif re.search(r"scaler\.fit\(\s*X\s*\)", script): | |
| leakage_detected = True | |
| if stage == 2: | |
| if not has_scaler: | |
| return ( | |
| "REJECT: Feature scaling is missing. MLPClassifier is " | |
| "highly sensitive to unscaled features. Add StandardScaler " | |
| "before fitting." | |
| ) | |
| if leakage_detected: | |
| return ( | |
| "REJECT: You're fitting the scaler on the full dataset. " | |
| "This causes data leakage. Fit only on X_train." | |
| ) | |
| return ( | |
| "LGTM: Scaler is correctly fitted on training data only. " | |
| "This looks good to merge." | |
| ) | |
| if stage == 3: | |
| if leakage_detected or not has_scaler: | |
| return ( | |
| "REJECT: Critical data leakage detected. Scaler must be " | |
| "fit on training data only. This invalidates your evaluation." | |
| ) | |
| return ( | |
| "LGTM: No data leakage detected. Evaluation pipeline " | |
| "looks correct." | |
| ) | |
| return "I have no feedback for this stage." | |