Agent Conversation: deepseek-reasoner

Model: deepseek-reasoner
Total steps: 12
Messages: 26
UserStep 0

Task: ml-selective-deferral

Selective Deferral Under Subgroup Shift

Research Question

Design a practical selective prediction and deferral policy for high-stakes tabular decisions.

The task isolates one modular question: given a fixed base classifier, what acceptance / deferral rule best trades off selective risk, subgroup fairness, and overall discrimination?

Background

Selective prediction systems should be able to say "I do not know" when the classifier is uncertain. In high-stakes settings, that deferral can be handed to a human reviewer or a slower backup process. The benchmark studies whether a policy can:

  • keep selective risk low at a fixed target coverage,
  • avoid concentrating deferrals on one subgroup,
  • preserve AUROC as a confidence ranking signal, and
  • remain simple enough to run offline on modest compute.

Task

Modify the SelectivePolicy class in custom_selective.py. The rest of the pipeline is fixed: dataset loading, train / calibration / test splitting, base model training, and metric computation.

The policy receives calibration-time base-model probabilities and subgroup labels, then decides whether each test example should be accepted or deferred. You may implement a single global threshold, a learned deferral score, subgroup-specific thresholds, or any other compact policy that fits the interface.

Evaluation

The benchmark runs on four offline tabular proxies from scikit-learn:

  • breast_cancer
  • diabetes (binarized around the training-set median)
  • california (binarized around the training-set median)
  • madelon

Each dataset is split into train / calibration / test partitions. Subgroups are formed from a stable feature threshold so that worst-group behavior can be measured.

Metrics:

  • selective_risk_at80: classification error on accepted examples at 80% target coverage
  • worst_group_selective_risk: worst subgroup error on accepted examples
  • deferral_rate_gap: max subgroup deferral rate minus min subgroup deferral rate
  • auroc: AUROC of the acceptance score for predicting correctness

Baselines

  • confidence_thresholding: tune one confidence threshold to hit the target coverage
  • conformal_abstention: split-conformal abstention with a coverage target
  • learned_deferral: train a compact meta-model that predicts whether the base model will be correct
  • groupwise_thresholding: subgroup-specific thresholds as a stronger reference baseline

Practical Notes

This task intentionally uses datasets that are already available offline in the repository's scikit-learn package setup, so no shared package edit is required.

If you want the exact adult / ACSIncome / COMPAS / Law School datasets from the prompt, that would require a shared package-data follow-up, which I have not implemented here.

scikit-learn/custom_selective.py [EDITABLE — lines 161–195 only]

     1: """Selective prediction / deferral benchmark.
     2: 
     3: Fixed:
     4: - offline dataset loa
AssistantStep 1edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 161,
5  "end_line": 195,
6  "content": "class SelectivePolicy:\n    \"\"\"Group-specific isotonic calibration to estimate error probability.\"\"\"\n\n    def __init__(self, target_coverage: float = TARGET_COVERAGE_DEFAULT, random_state: int = 0):\n        self.target_coverage = float(target_coverage)\n        self.random_state = int(random_state)\n        self.threshold_: float = 0.5\n        self.group_thresholds_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.strategy_name = \"group_calibrated_error\"\n        self.group_calibrators_: dict[int, object] = {}\n        self.global_calibrator_ = None\n        self.min_samples_per_group = 20\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        from sklearn.isotonic import IsotonicRegression\n        \n        confidence = np.max(probs, axis=1)\n        correct = (np.argmax(probs, axis=1) == y_true).astype(float)\n        \n        # Global calibrator\n        self.global_calibrator_ = IsotonicRegression(out_of_bounds='clip', increasing=True)\n        self.global_calibrator_.fit(confidence, correct)\n        \n        # Group-specific calibrators\n        unique_groups = np.unique(groups)\n        for group_id in unique_groups:\n            mask = groups == group_id\n            if np.sum(mask) >= self.min_samples_per_group:\n                calibrator = IsotonicRegression(out_of_bounds='clip', increasing=True)\n                calibrator.fit(confidence[mask], correct[mask])\n                self.group_calibrators_[int(group_id)] = calibrator\n            else:\n                # Use global calibrator for small groups\n                pass\n        \n        # Compute expected error scores\n        scores = self._expected_error(confidence, groups)\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        self.group_thresholds_ = {}\n        self.meta_model_ = None\n        return self\n    \n    def _expected_error(self, confidence: np.ndarray, groups: np.ndarray) -> np.ndarray:\n        \"\"\"Compute expected error = 1 - expected accuracy.\"\"\"\n        expected_acc = np.zeros_like(confidence, dtype=float)\n        for i, (conf, group) in enumerate(zip(confidence, groups)):\n            calibrator = self.group_calibrators_.get(int(group), self.global_calibrator_)\n            if calibrator is None:\n                # fallback: error = 1 - confidence (i.e., perfect calibration)\n                expected_acc[i] = conf\n            else:\n                expected_acc[i] = calibrator.predict([conf])[0]\n        # expected error = 1 - expected accuracy\n        expected_err = 1.0 - expected_acc\n        return expected_err\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        confidence = np.max(probs, axis=1)\n        return self._expected_error(confidence, groups)\n\n    def predict_accept(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        scores = self.acceptance_score(probs, groups, X)\n        return scores <= self.threshold_  # lower error is better\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_)}\n        for group_id, calibrator in self.group_calibrators_.items():\n            summary[f\"calibrator_group_{group_id}_min\"] = float(np.min(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0\n            summary[f\"calibrator_group_{group_id}_max\"] = float(np.max(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0\n        return summary"
7}
Tool ResultStep 1
OK: Replaced lines 161..195 with 72 line(s) in scikit-learn/custom_selective.py. Editable range: 161–232.

[Current file: scikit-learn/custom_selective.py | editable: 161–232 | total: 376 lines]
   161  class SelectivePolicy:
   162      """Group-specific isotonic calibration to estimate error probability."""
   163  
       ... (66 more lines) ...
   230              summary[f"calibrator_group_{group_id}_min"] = float(np.min(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   231              summary[f"calibrator_group_{group_id}_max"] = float(np.max(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   232          return summary
AssistantStep 2edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 177,
5  "end_line": 180,
6  "content": "        # Compute expected error scores (lower is better)\n        scores = self._expected_error(confidence, groups)\n        quantile = float(np.clip(self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))"
7}
Tool ResultStep 2
OK: Replaced lines 177..180 with 4 line(s) in scikit-learn/custom_selective.py. Editable range: 161–232.

[Current file: scikit-learn/custom_selective.py | editable: 161–232 | total: 376 lines]
   161  class SelectivePolicy:
   162      """Group-specific isotonic calibration to estimate error probability."""
   163  
       ... (66 more lines) ...
   230              summary[f"calibrator_group_{group_id}_min"] = float(np.min(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   231              summary[f"calibrator_group_{group_id}_max"] = float(np.max(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   232          return summary
AssistantStep 3edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 161,
5  "end_line": 232,
6  "content": "class SelectivePolicy:\n    \"\"\"Group-specific isotonic calibration to estimate accuracy probability.\"\"\"\n\n    def __init__(self, target_coverage: float = TARGET_COVERAGE_DEFAULT, random_state: int = 0):\n        self.target_coverage = float(target_coverage)\n        self.random_state = int(random_state)\n        self.threshold_: float = 0.5\n        self.group_thresholds_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.strategy_name = \"group_calibrated_accuracy\"\n        self.group_calibrators_: dict[int, object] = {}\n        self.global_calibrator_ = None\n        self.min_samples_per_group = 20\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        from sklearn.isotonic import IsotonicRegression\n        \n        confidence = np.max(probs, axis=1)\n        correct = (np.argmax(probs, axis=1) == y_true).astype(float)\n        \n        # Global calibrator\n        self.global_calibrator_ = IsotonicRegression(out_of_bounds='clip', increasing=True)\n        self.global_calibrator_.fit(confidence, correct)\n        \n        # Group-specific calibrators\n        unique_groups = np.unique(groups)\n        for group_id in unique_groups:\n            mask = groups == group_id\n            if np.sum(mask) >= self.min_samples_per_group:\n                calibrator = IsotonicRegression(out_of_bounds='clip', increasing=True)\n                calibrator.fit(confidence[mask], correct[mask])\n                self.group_calibrators_[int(group_id)] = calibrator\n            else:\n                # Use global calibrator for small groups\n                pass\n        \n        # Compute expected accuracy scores\n        scores = self._expected_accuracy(confidence, groups)\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        self.group_thresholds_ = {}\n        self.meta_model_ = None\n        return self\n    \n    def _expected_accuracy(self, confidence: np.ndarray, groups: np.ndarray) -> np.ndarray:\n        \"\"\"Compute expected accuracy using group-specific calibrators.\"\"\"\n        expected_acc = np.zeros_like(confidence, dtype=float)\n        for i, (conf, group) in enumerate(zip(confidence, groups)):\n            calibrator = self.group_calibrators_.get(int(group), self.global_calibrator_)\n            if calibrator is None:\n                # fallback: accuracy = confidence (i.e., perfect calibration)\n                expected_acc[i] = conf\n            else:\n                expected_acc[i] = calibrator.predict([conf])[0]\n        return expected_acc\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        confidence = np.max(probs, axis=1)\n        return self._expected_accuracy(confidence, groups)\n\n    def predict_accept(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        scores = self.acceptance_score(probs, groups, X)\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_)}\n        for group_id, calibrator in self.group_calibrators_.items():\n            summary[f\"calibrator_group_{group_id}_min\"] = float(np.min(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0\n            summary[f\"calibrator_group_{group_id}_max\"] = float(np.max(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0\n        return summary"
7}
Tool ResultStep 3
OK: Replaced lines 161..232 with 70 line(s) in scikit-learn/custom_selective.py. Editable range: 161–230.

[Current file: scikit-learn/custom_selective.py | editable: 161–230 | total: 374 lines]
   161  class SelectivePolicy:
   162      """Group-specific isotonic calibration to estimate accuracy probability."""
   163  
       ... (64 more lines) ...
   228              summary[f"calibrator_group_{group_id}_min"] = float(np.min(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   229              summary[f"calibrator_group_{group_id}_max"] = float(np.max(calibrator.X_thresholds_)) if hasattr(calibrator, 'X_thresholds_') else -1.0
   230          return summary
AssistantStep 4test
Tool ResultStep 4
[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### breast_cancer (scripts/breast_cancer.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.991202 cal_accuracy=0.956140 cal_coverage=0.929825 policy_threshold=1.000000
Final metrics (breast_cancer): selective_risk_at80_breast_cancer=0.009524, coverage_at80_breast_cancer=0.921053, worst_group_selective_risk_breast_cancer=0.018519, deferral_rate_gap_breast_cancer=0.142857, auroc_breast_cancer=0.852273, target_coverage_breast_cancer=0.800000, actual_coverage_breast_cancer=0.921053, group_threshold_breast_cancer=13.270000

### diabetes (scripts/diabetes.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.750000 cal_accuracy=0.752809 cal_coverage=0.876404 policy_threshold=0.500000
Final metrics (diabetes): selective_risk_at80_diabetes=0.209302, coverage_at80_diabetes=0.966292, worst_group_selective_risk_diabetes=0.210526, deferral_rate_gap_diabetes=0.073171, auroc_diabetes=0.702381, targe
AssistantStep 5edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 161,
5  "end_line": 230,
6  "content": "class SelectivePolicy:\n    \"\"\"Group-specific Platt scaling to estimate accuracy probability.\"\"\"\n\n    def __init__(self, target_coverage: float = TARGET_COVERAGE_DEFAULT, random_state: int = 0):\n        self.target_coverage = float(target_coverage)\n        self.random_state = int(random_state)\n        self.threshold_: float = 0.5\n        self.group_thresholds_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.strategy_name = \"group_platt_scaling\"\n        self.group_models_: dict[int, object] = {}\n        self.global_model_ = None\n        self.min_samples_per_group = 20\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        from sklearn.linear_model import LogisticRegression\n        \n        confidence = np.max(probs, axis=1).reshape(-1, 1)\n        correct = (np.argmax(probs, axis=1) == y_true).astype(int)\n        \n        # Global Platt scaling\n        self.global_model_ = LogisticRegression(\n            penalty='l2',\n            C=1.0,\n            solver='lbfgs',\n            max_iter=1000,\n            class_weight='balanced',\n            random_state=self.random_state,\n        )\n        self.global_model_.fit(confidence, correct)\n        \n        # Group-specific models\n        unique_groups = np.unique(groups)\n        for group_id in unique_groups:\n            mask = groups == group_id\n            if np.sum(mask) >= self.min_samples_per_group:\n                model = LogisticRegression(\n                    penalty='l2',\n                    C=1.0,\n                    solver='lbfgs',\n                    max_iter=1000,\n                    class_weight='balanced',\n                    random_state=self.random_state,\n                )\n                model.fit(confidence[mask], correct[mask])\n                # Ensure monotonicity: coefficient for confidence should be non-negative\n                if model.coef_[0, 0] < 0:\n                    # If negative, fit intercept-only model (constant probability)\n                    model.coef_[0, 0] = 0.0\n                    model.intercept_[0] = np.log(np.mean(correct[mask]) / (1.0 - np.mean(correct[mask]))) if 0 < np.mean(correct[mask]) < 1 else 0.0\n                self.group_models_[int(group_id)] = model\n            else:\n                # Use global model for small groups\n                pass\n        \n        # Compute expected accuracy scores\n        scores = self._expected_accuracy(confidence.flatten(), groups)\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        self.group_thresholds_ = {}\n        self.meta_model_ = None\n        return self\n    \n    def _expected_accuracy(self, confidence: np.ndarray, groups: np.ndarray) -> np.ndarray:\n        \"\"\"Compute expected accuracy using group-specific Platt scaling.\"\"\"\n        expected_acc = np.zeros_like(confidence, dtype=float)\n        for i, (conf, group) in enumerate(zip(confidence, groups)):\n            model = self.group_models_.get(int(group), self.global_model_)\n            if model is None:\n                expected_acc[i] = conf  # fallback\n            else:\n                prob = model.predict_proba([[conf]])[0, 1]\n                expected_acc[i] = prob\n        return expected_acc\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        confidence = np.max(probs, axis=1)\n        return self._expected_accuracy(confidence, groups)\n\n    def predict_accept(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        scores = self.acceptance_score(probs, groups, X)\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_)}\n        for group_id, model in self.group_models_.items():\n            summary[f\"model_group_{group_id}_coef\"] = float(model.coef_[0, 0]) if hasattr(model, 'coef_') else 0.0\n            summary[f\"model_group_{group_id}_intercept\"] = float(model.intercept_[0]) if hasattr(model, 'intercept_') else 0.0\n        return summary"
7}
Tool ResultStep 5
OK: Replaced lines 161..230 with 89 line(s) in scikit-learn/custom_selective.py. Editable range: 161–249.

[Current file: scikit-learn/custom_selective.py | editable: 161–249 | total: 393 lines]
   161  class SelectivePolicy:
   162      """Group-specific Platt scaling to estimate accuracy probability."""
   163  
       ... (83 more lines) ...
   247              summary[f"model_group_{group_id}_coef"] = float(model.coef_[0, 0]) if hasattr(model, 'coef_') else 0.0
   248              summary[f"model_group_{group_id}_intercept"] = float(model.intercept_[0]) if hasattr(model, 'intercept_') else 0.0
   249          return summary
AssistantStep 6test
Tool ResultStep 6
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### breast_cancer (scripts/breast_cancer.sh)
[COMMAND FAILED exit=1]
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 393, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 389, in main
    run_benchmark(args.dataset, args.seed, args.target_coverage, args.output_dir)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 345, in run_benchmark
    policy.fit(cal_probs, y[cal_idx], groups[cal_idx], X=X[cal_idx])
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selecti
AssistantStep 7edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 161,
5  "end_line": 249,
6  "content": "class SelectivePolicy:\n    \"\"\"Group-specific Platt scaling with fallback to global model.\"\"\"\n\n    def __init__(self, target_coverage: float = TARGET_COVERAGE_DEFAULT, random_state: int = 0):\n        self.target_coverage = float(target_coverage)\n        self.random_state = int(random_state)\n        self.threshold_: float = 0.5\n        self.group_thresholds_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.strategy_name = \"group_platt_scaling_v2\"\n        self.group_models_: dict[int, object] = {}\n        self.global_model_ = None\n        self.min_samples_per_group = 10\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        from sklearn.linear_model import LogisticRegression\n        \n        confidence = np.max(probs, axis=1).reshape(-1, 1)\n        correct = (np.argmax(probs, axis=1) == y_true).astype(int)\n        \n        # Global Platt scaling\n        self.global_model_ = LogisticRegression(\n            penalty='l2',\n            C=1.0,\n            solver='lbfgs',\n            max_iter=1000,\n            class_weight='balanced',\n            random_state=self.random_state,\n        )\n        # Check if global data has both classes\n        if len(np.unique(correct)) == 2:\n            self.global_model_.fit(confidence, correct)\n        else:\n            # Single class: constant predictor\n            const = np.mean(correct)\n            self.global_model_ = _ConstantPredictor(const)\n        \n        # Group-specific models\n        unique_groups = np.unique(groups)\n        for group_id in unique_groups:\n            mask = groups == group_id\n            if np.sum(mask) >= self.min_samples_per_group:\n                group_correct = correct[mask]\n                if len(np.unique(group_correct)) == 2:\n                    model = LogisticRegression(\n                        penalty='l2',\n                        C=1.0,\n                        solver='lbfgs',\n                        max_iter=1000,\n                        class_weight='balanced',\n                        random_state=self.random_state,\n                    )\n                    model.fit(confidence[mask], group_correct)\n                    # Ensure monotonicity: coefficient for confidence should be non-negative\n                    if model.coef_[0, 0] < 0:\n                        # If negative, fit intercept-only model (constant probability)\n                        model.coef_[0, 0] = 0.0\n                        p = np.mean(group_correct)\n                        if p == 0.0 or p == 1.0:\n                            model.intercept_[0] = 0.0  # will be handled by predict_proba\n                        else:\n                            model.intercept_[0] = np.log(p / (1.0 - p))\n                else:\n                    # Single class group\n                    p = np.mean(group_correct)\n                    model = _ConstantPredictor(p)\n                self.group_models_[int(group_id)] = model\n            else:\n                # Too few samples: use global model\n                pass\n        \n        # Compute expected accuracy scores\n        scores = self._expected_accuracy(confidence.flatten(), groups)\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        self.group_thresholds_ = {}\n        self.meta_model_ = None\n        return self\n    \n    def _expected_accuracy(self, confidence: np.ndarray, groups: np.ndarray) -> np.ndarray:\n        \"\"\"Compute expected accuracy using group-specific models.\"\"\"\n        expected_acc = np.zeros_like(confidence, dtype=float)\n        for i, (conf, group) in enumerate(zip(confidence, groups)):\n            model = self.group_models_.get(int(group), self.global_model_)\n            if isinstance(model, _ConstantPredictor):\n                expected_acc[i] = model.predict_proba([[conf]])[0, 1]\n            else:\n                prob = model.predict_proba([[conf]])[0, 1]\n                expected_acc[i] = prob\n        return expected_acc\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        confidence = np.max(probs, axis=1)\n        return self._expected_accuracy(confidence, groups)\n\n    def predict_accept(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        scores = self.acceptance_score(probs, groups, X)\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_)}\n        for group_id, model in self.group_models_.items():\n            if isinstance(model, _ConstantPredictor):\n                summary[f\"model_group_{group_id}_const\"] = float(model.const)\n            else:\n                summary[f\"model_group_{group_id}_coef\"] = float(model.coef_[0, 0]) if hasattr(model, 'coef_') else 0.0\n                summary[f\"model_group_{group_id}_intercept\"] = float(model.intercept_[0]) if hasattr(model, 'intercept_') else 0.0\n        return summary\n\n\nclass _ConstantPredictor:\n    \"\"\"Simple predictor returning constant probability of class 1.\"\"\"\n    def __init__(self, const: float):\n        self.const = float(const)\n    \n    def predict_proba(self, X: np.ndarray) -> np.ndarray:\n        n = X.shape[0]\n        return np.column_stack([1.0 - self.const, self.const])"
7}
Tool ResultStep 7
OK: Replaced lines 161..249 with 118 line(s) in scikit-learn/custom_selective.py. Editable range: 161–278.

[Current file: scikit-learn/custom_selective.py | editable: 161–278 | total: 422 lines]
   161  class SelectivePolicy:
   162      """Group-specific Platt scaling with fallback to global model."""
   163  
       ... (112 more lines) ...
   276      def predict_proba(self, X: np.ndarray) -> np.ndarray:
   277          n = X.shape[0]
   278          return np.column_stack([1.0 - self.const, self.const])
AssistantStep 8test
Tool ResultStep 8
[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)

[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.

### breast_cancer (scripts/breast_cancer.sh)
[COMMAND FAILED exit=1]
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 422, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 418, in main
    run_benchmark(args.dataset, args.seed, args.target_coverage, args.output_dir)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-selective-deferral/deepseek-reasoner_20260414_110948/scikit-learn/custom_selective.py", line 374, in run_benchmark
    policy.fit(cal_probs, y[cal_idx], groups[cal_idx], X=X[cal_idx])
  File "/srv/
AssistantStep 9edit
Tool Input
1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "start_line": 274,
5  "end_line": 278,
6  "content": "    def predict_proba(self, X: np.ndarray) -> np.ndarray:\n        X = np.asarray(X)\n        n = X.shape[0]\n        return np.column_stack([1.0 - self.const, self.const])"
7}
Tool ResultStep 9
OK: Replaced lines 274..278 with 4 line(s) in scikit-learn/custom_selective.py. Editable range: 161–277.

[Current file: scikit-learn/custom_selective.py | editable: 161–277 | total: 421 lines]
   161  class SelectivePolicy:
   162      """Group-specific Platt scaling with fallback to global model."""
   163  
       ... (111 more lines) ...
   275          X = np.asarray(X)
   276          n = X.shape[0]
   277          return np.column_stack([1.0 - self.const, self.const])