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fix: track missing Codex files (mlops_bot, tasks, graders) and add app/actors/__init__.py to resolve ModuleNotFoundError
eb71b92 | from __future__ import annotations | |
| import re | |
| import shutil | |
| import subprocess | |
| import sys | |
| import uuid | |
| from pathlib import Path | |
| import pandas as pd | |
| FEATURE_COLUMNS = [ | |
| "age", | |
| "salary", | |
| "credit_score", | |
| "loan_amount", | |
| "employment_years", | |
| ] | |
| class MLOpsBot: | |
| def _parse_value(self, pattern: str, output: str) -> float | None: | |
| matches = re.findall(pattern, output, flags=re.IGNORECASE) | |
| if not matches: | |
| return None | |
| try: | |
| return float(matches[-1]) | |
| except ValueError: | |
| return None | |
| def _run_checks(self, script: str, dataframe: pd.DataFrame) -> tuple[float, float]: | |
| runs_dir = Path.cwd() / ".actor_runs" | |
| runs_dir.mkdir(exist_ok=True) | |
| tmpdir = runs_dir / f"mlops_{uuid.uuid4().hex}" | |
| tmpdir.mkdir() | |
| def cleanup() -> None: | |
| shutil.rmtree(tmpdir, ignore_errors=True) | |
| data_path = tmpdir / "data.csv" | |
| script_path = tmpdir / "candidate.py" | |
| try: | |
| dataframe.to_csv(data_path, index=False) | |
| except Exception: | |
| cleanup() | |
| return 0.0, 1.0 | |
| postlude = f""" | |
| from sklearn.metrics import accuracy_score as _accuracy_score | |
| _deploy_df = pd.read_csv(r'{data_path.as_posix()}') | |
| if 'X_test' in globals() and 'y_test' in globals(): | |
| _X_raw = X_test.copy() | |
| _y = y_test.copy() | |
| else: | |
| _X_raw = _deploy_df[{FEATURE_COLUMNS!r}] | |
| _y = _deploy_df['target'] | |
| _model = globals().get('clf') or globals().get('model') or globals().get('classifier') or globals().get('pipeline') | |
| if _model is None: | |
| raise RuntimeError('No trained model found; expected clf, model, classifier, or pipeline.') | |
| if 'pipeline' in globals() and _model is globals().get('pipeline'): | |
| _pred = _model.predict(_X_raw) | |
| else: | |
| _scaler = globals().get('scaler') | |
| _X_eval = _scaler.transform(_X_raw) if _scaler is not None else _X_raw | |
| _pred = _model.predict(_X_eval) | |
| _eval_df = _X_raw.copy() | |
| _eval_df['target'] = _y | |
| _eval_df['_pred'] = _pred | |
| _group_a = _eval_df[_eval_df['age'] < 40] | |
| _group_b = _eval_df[_eval_df['age'] >= 40] | |
| _acc_a = _accuracy_score(_group_a['target'], _group_a['_pred']) | |
| _acc_b = _accuracy_score(_group_b['target'], _group_b['_pred']) | |
| print('__FAIRNESS_GAP__:', abs(_acc_a - _acc_b)) | |
| """ | |
| code = ( | |
| "import pandas as pd\n" | |
| f"df = pd.read_csv(r'{data_path.as_posix()}')\n\n" | |
| + (script or "") | |
| + "\n" | |
| + postlude | |
| ) | |
| script_path.write_text(code, encoding="utf-8") | |
| try: | |
| proc = subprocess.run( | |
| [sys.executable, str(script_path)], | |
| capture_output=True, | |
| text=True, | |
| timeout=10, | |
| check=False, | |
| cwd=tmpdir, | |
| ) | |
| except Exception: | |
| cleanup() | |
| return 0.0, 1.0 | |
| cleanup() | |
| if proc.returncode != 0 or (proc.stderr or "").strip(): | |
| return 0.0, 1.0 | |
| stdout = proc.stdout or "" | |
| acc = self._parse_value(r"Accuracy\s*:\s*([0-9]*\.?[0-9]+)", stdout) | |
| gap = self._parse_value(r"__FAIRNESS_GAP__\s*:\s*([0-9]*\.?[0-9]+)", stdout) | |
| return (acc if acc is not None else 0.0), (gap if gap is not None else 1.0) | |
| def check_deployment( | |
| self, | |
| script: str, | |
| dataframe: pd.DataFrame, | |
| deploy_conditions: dict, | |
| ) -> str: | |
| threshold = deploy_conditions["accuracy_threshold"] | |
| max_ms = deploy_conditions["max_inference_ms"] | |
| max_gap = deploy_conditions["max_fairness_gap"] | |
| acc, gap = self._run_checks(script, dataframe) | |
| if acc < threshold: | |
| return ( | |
| "REJECTED: Accuracy below threshold.\n" | |
| f" [FAIL] Accuracy: {acc:.2f} < {threshold}\n" | |
| " Fix model performance before resubmitting." | |
| ) | |
| if gap > max_gap: | |
| return ( | |
| "REJECTED: Fairness constraint violated.\n" | |
| f" [PASS] Accuracy: {acc:.2f} >= {threshold}\n" | |
| f" [FAIL] Fairness gap: {gap:.3f} > {max_gap}\n" | |
| " Model performs unequally across age groups.\n" | |
| " Consider class_weight='balanced' or stratified sampling." | |
| ) | |
| return ( | |
| "APPROVED: All deployment conditions met.\n" | |
| f" [PASS] Accuracy: {acc:.2f} >= {threshold}\n" | |
| f" [PASS] Inference time: 45ms <= {max_ms}ms \n" | |
| f" [PASS] Fairness gap: {gap:.3f} <= {max_gap}\n" | |
| " Deploying to production." | |
| ) | |