Agent Conversation: openai/gpt-5.4

Model: openai/gpt-5.4

Total steps: 7

Messages: 16

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  "content": "class SelectivePolicy:\n    \"\"\"Fairness-regularized learned deferral with subgroup calibration offsets.\n\n    The policy learns a compact correctness model from confidence features, applies\n    empirical-Bayes subgroup score corrections, and then searches for subgroup\n    thresholds that balance selective risk, worst-group risk, coverage error, and\n    deferral-rate parity on the calibration set.\n    \"\"\"\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.group_bias_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.strategy_name = \"fair_meta_threshold_search\"\n\n    def _raw_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        base = np.max(probs, axis=1)\n        if self.meta_model_ is None:\n            scores = base\n        else:\n            feats = _confidence_features(probs, groups, X)\n            meta = self.meta_model_.predict_proba(feats)[:, 1]\n            scores = 0.8 * meta + 0.2 * base\n        if self.group_bias_:\n            bias = np.asarray([self.group_bias_.get(int(group), 0.0) for group in groups], dtype=float)\n            scores = scores + bias\n        return np.clip(scores, 1e-6, 1.0 - 1e-6)\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        correct = (np.argmax(probs, axis=1) == y_true).astype(int)\n        feats = _confidence_features(probs, groups, X)\n        self.group_thresholds_ = {}\n        self.group_bias_ = {}\n        if len(np.unique(correct)) >= 2:\n            self.meta_model_ = Pipeline(\n                steps=[\n                    (\"scale\", StandardScaler()),\n                    (\n                        \"clf\",\n                        LogisticRegression(\n                            max_iter=1000,\n                            solver=\"lbfgs\",\n                            class_weight=\"balanced\",\n                            random_state=self.random_state,\n                        ),\n                    ),\n                ]\n            )\n            self.meta_model_.fit(feats, correct)\n        else:\n            self.meta_model_ = None\n\n        scores = self._raw_score(probs, groups, X)\n        for group_id in np.unique(groups):\n            mask = groups == group_id\n            n_group = int(mask.sum())\n            if n_group == 0:\n                continue\n            shrink = n_group / (n_group + 25.0)\n            bias = shrink * (float(correct[mask].mean()) - float(scores[mask].mean()))\n            self.group_bias_[int(group_id)] = float(np.clip(bias, -0.15, 0.15))\n        scores = self._raw_score(probs, groups, X)\n\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        group_ids = [int(group_id) for group_id in np.unique(groups)]\n        self.group_thresholds_ = {\n            group_id: float(np.quantile(scores[groups == group_id], quantile)) for group_id in group_ids\n        }\n\n        if len(group_ids) == 2:\n            counts = {group_id: int(np.sum(groups == group_id)) for group_id in group_ids}\n            lo = max(0.05, self.target_coverage - 0.20)\n            hi = min(0.98, self.target_coverage + 0.20)\n            grid = np.linspace(lo, hi, 17)\n            best_obj = float(\"inf\")\n            best_thresholds = dict(self.group_thresholds_)\n            for cov0 in grid:\n                for cov1 in grid:\n                    desired = (counts[group_ids[0]] * cov0 + counts[group_ids[1]] * cov1) / max(len(groups), 1)\n                    if abs(desired - self.target_coverage) > 0.03:\n                        continue\n                    candidate_thresholds = {}\n                    for group_id, cov in zip(group_ids, (cov0, cov1)):\n                        group_scores = scores[groups == group_id]\n                        candidate_thresholds[group_id] = float(np.quantile(group_scores, 1.0 - cov))\n                    thresholds = np.asarray([candidate_thresholds[int(group)] for group in groups], dtype=float)\n                    accept = scores >= thresholds\n                    coverage = float(accept.mean())\n                    selective_risk = 1.0 - float(correct[accept].mean()) if accept.any() else 1.0\n                    group_risks = []\n                    deferrals = []\n                    for group_id in group_ids:\n                        mask = groups == group_id\n                        group_accept = accept[mask]\n                        n_accept = int(group_accept.sum())\n                        if n_accept > 0:\n                            errors = float(np.sum(1 - correct[mask][group_accept]))\n                            risk = (errors + 0.5) / (n_accept + 1.0)\n                        else:\n                            risk = 1.0\n                        group_risks.append(risk)\n                        deferrals.append(float(1.0 - group_accept.mean()))\n                    obj = (\n                        selective_risk\n                        + 0.75 * max(group_risks)\n                        + 0.20 * (max(deferrals) - min(deferrals))\n                        + 3.0 * abs(coverage - self.target_coverage)\n                    )\n                    if obj < best_obj:\n                        best_obj = obj\n                        best_thresholds = candidate_thresholds\n            self.group_thresholds_ = best_thresholds\n            self.threshold_ = float(np.mean([best_thresholds[group_id] for group_id in group_ids]))\n        return self\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        return self._raw_score(probs, groups, X)\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        if self.group_thresholds_:\n            thresholds = np.asarray([self.group_thresholds_.get(int(group), self.threshold_) for group in groups], dtype=float)\n            return scores >= thresholds\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_)}\n        for group_id, threshold in self.group_thresholds_.items():\n            summary[f\"threshold_group_{group_id}\"] = float(threshold)\n        for group_id, bias in self.group_bias_.items():\n            summary[f\"bias_group_{group_id}\"] = float(bias)\n        return summary",
5  "after_line": 0,
6  "start_line": 161,
7  "end_line": 195
8}

Tool ResultStep 1

OK: Replaced lines 161..195 with 136 line(s) in scikit-learn/custom_selective.py. Editable range: 161–296.

[Current file: scikit-learn/custom_selective.py | editable: 161–296 | total: 440 lines]
   161  class SelectivePolicy:
   162      """Fairness-regularized learned deferral with subgroup calibration offsets.
   163  
       ... (130 more lines) ...
   294          for group_id, bias in self.group_bias_.items():
   295              summary[f"bias_group_{group_id}"] = float(bias)
   296          return summary

AssistantStep 2test

Tool ResultStep 2

[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=0.842105 policy_threshold=0.997264
Final metrics (breast_cancer): selective_risk_at80_breast_cancer=0.000000, coverage_at80_breast_cancer=0.754386, worst_group_selective_risk_breast_cancer=0.000000, deferral_rate_gap_breast_cancer=0.054155, auroc_breast_cancer=0.959091, target_coverage_breast_cancer=0.800000, actual_coverage_breast_cancer=0.754386, group_threshold_breast_cancer=13.270000

### diabetes (scripts/diabetes.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.750000 cal_accuracy=0.752809 cal_coverage=0.797753 policy_threshold=0.447216
Final metrics (diabetes): selective_risk_at80_diabetes=0.194805, coverage_at80_diabetes=0.865169, worst_group_selective_risk_diabetes=0.250000, deferral_rate_gap_diabetes=0.204776, auroc_diabetes=0.803922, targe

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "content": "class SelectivePolicy:\n    \"\"\"Adaptive correctness scoring with fairness-constrained subgroup thresholds.\n\n    The policy learns a compact correctness model from confidence statistics,\n    adaptively blends it with raw classifier confidence, applies a small\n    subgroup calibration offset when it does not hurt ranking quality, and then\n    tunes subgroup thresholds under explicit coverage/fairness constraints.\n    \"\"\"\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.group_bias_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.meta_weight_: float = 0.0\n        self.strategy_name = \"adaptive_fair_deferral\"\n\n    def _score_without_bias(self, probs: np.ndarray) -> np.ndarray:\n        base = np.max(probs, axis=1)\n        if self.meta_model_ is None or self.meta_weight_ <= 1e-8:\n            return base\n        feats = _confidence_features(probs, None, None)\n        meta = self.meta_model_.predict_proba(feats)[:, 1]\n        scores = self.meta_weight_ * meta + (1.0 - self.meta_weight_) * base\n        return np.clip(scores, 1e-6, 1.0 - 1e-6)\n\n    def _apply_group_bias(self, scores: np.ndarray, groups: np.ndarray) -> np.ndarray:\n        if not self.group_bias_:\n            return np.clip(scores, 1e-6, 1.0 - 1e-6)\n        bias = np.asarray([self.group_bias_.get(int(group), 0.0) for group in groups], dtype=float)\n        return np.clip(scores + bias, 1e-6, 1.0 - 1e-6)\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        del X\n        correct = (np.argmax(probs, axis=1) == y_true).astype(int)\n        base_scores = np.max(probs, axis=1)\n        self.group_thresholds_ = {}\n        self.group_bias_ = {}\n        self.meta_weight_ = 0.0\n\n        feats = _confidence_features(probs, None, None)\n        if len(np.unique(correct)) >= 2:\n            self.meta_model_ = Pipeline(\n                steps=[\n                    (\"scale\", StandardScaler()),\n                    (\n                        \"clf\",\n                        LogisticRegression(\n                            max_iter=1000,\n                            solver=\"lbfgs\",\n                            class_weight=\"balanced\",\n                            random_state=self.random_state,\n                        ),\n                    ),\n                ]\n            )\n            self.meta_model_.fit(feats, correct)\n            meta_scores = self.meta_model_.predict_proba(feats)[:, 1]\n            best_value = -float(\"inf\")\n            for weight in (0.0, 0.25, 0.5, 0.75, 1.0):\n                blended = np.clip(weight * meta_scores + (1.0 - weight) * base_scores, 1e-6, 1.0 - 1e-6)\n                value = _safe_roc_auc(correct, blended) - 0.05 * float(np.mean((blended - correct) ** 2))\n                if value > best_value:\n                    best_value = value\n                    self.meta_weight_ = float(weight)\n        else:\n            self.meta_model_ = None\n\n        raw_scores = self._score_without_bias(probs)\n        raw_auc = _safe_roc_auc(correct, raw_scores)\n        candidate_biases: dict[int, float] = {}\n        for group_id in np.unique(groups):\n            mask = groups == group_id\n            n_group = int(mask.sum())\n            if n_group == 0:\n                continue\n            shrink = n_group / (n_group + 40.0)\n            bias = shrink * (float(correct[mask].mean()) - float(raw_scores[mask].mean()))\n            candidate_biases[int(group_id)] = float(np.clip(bias, -0.10, 0.10))\n        self.group_bias_ = candidate_biases\n        biased_scores = self._apply_group_bias(raw_scores, groups)\n        biased_auc = _safe_roc_auc(correct, biased_scores)\n        if biased_auc + 1e-4 < raw_auc:\n            self.group_bias_ = {}\n            scores = raw_scores\n        else:\n            scores = biased_scores\n\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n        group_ids = [int(group_id) for group_id in np.unique(groups)]\n        self.group_thresholds_ = {\n            group_id: float(np.quantile(scores[groups == group_id], quantile)) for group_id in group_ids\n        }\n\n        if len(group_ids) == 2:\n            counts = {group_id: int(np.sum(groups == group_id)) for group_id in group_ids}\n            lo = max(0.60, self.target_coverage - 0.12)\n            hi = min(0.92, self.target_coverage + 0.12)\n            grid = np.linspace(lo, hi, 13)\n            best_obj = float(\"inf\")\n            best_thresholds = dict(self.group_thresholds_)\n            for cov0 in grid:\n                for cov1 in grid:\n                    if abs(cov0 - cov1) > 0.10:\n                        continue\n                    desired = (counts[group_ids[0]] * cov0 + counts[group_ids[1]] * cov1) / max(len(groups), 1)\n                    if abs(desired - self.target_coverage) > 0.015:\n                        continue\n                    candidate_thresholds = {}\n                    for group_id, cov in zip(group_ids, (cov0, cov1)):\n                        group_scores = scores[groups == group_id]\n                        candidate_thresholds[group_id] = float(np.quantile(group_scores, 1.0 - cov))\n                    thresholds = np.asarray([candidate_thresholds[int(group)] for group in groups], dtype=float)\n                    accept = scores >= thresholds\n                    coverage = float(accept.mean())\n                    selective_risk = 1.0 - float(correct[accept].mean()) if accept.any() else 1.0\n                    group_risks = []\n                    deferrals = []\n                    for group_id in group_ids:\n                        mask = groups == group_id\n                        group_accept = accept[mask]\n                        n_accept = int(group_accept.sum())\n                        if n_accept > 0:\n                            errors = float(np.sum(1 - correct[mask][group_accept]))\n                            risk = (errors + 0.5) / (n_accept + 1.0)\n                        else:\n                            risk = 1.0\n                        group_risks.append(risk)\n                        deferrals.append(float(1.0 - group_accept.mean()))\n                    obj = (\n                        selective_risk\n                        + 0.80 * max(group_risks)\n                        + 0.45 * (max(deferrals) - min(deferrals))\n                        + 4.0 * abs(coverage - self.target_coverage)\n                    )\n                    if obj < best_obj:\n                        best_obj = obj\n                        best_thresholds = candidate_thresholds\n            self.group_thresholds_ = best_thresholds\n            self.threshold_ = float(np.mean([best_thresholds[group_id] for group_id in group_ids]))\n        return self\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        del X\n        return self._apply_group_bias(self._score_without_bias(probs), 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        if self.group_thresholds_:\n            thresholds = np.asarray([self.group_thresholds_.get(int(group), self.threshold_) for group in groups], dtype=float)\n            return scores >= thresholds\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\"threshold\": float(self.threshold_), \"meta_weight\": float(self.meta_weight_)}\n        for group_id, threshold in self.group_thresholds_.items():\n            summary[f\"threshold_group_{group_id}\"] = float(threshold)\n        for group_id, bias in self.group_bias_.items():\n            summary[f\"bias_group_{group_id}\"] = float(bias)\n        return summary",
5  "after_line": 0,
6  "start_line": 161,
7  "end_line": 296
8}

Tool ResultStep 3

OK: Replaced lines 161..296 with 163 line(s) in scikit-learn/custom_selective.py. Editable range: 161–323.

[Current file: scikit-learn/custom_selective.py | editable: 161–323 | total: 467 lines]
   161  class SelectivePolicy:
   162      """Adaptive correctness scoring with fairness-constrained subgroup thresholds.
   163  
       ... (157 more lines) ...
   321          for group_id, bias in self.group_bias_.items():
   322              summary[f"bias_group_{group_id}"] = float(bias)
   323          return summary

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test 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=0.798246 policy_threshold=0.813874
Final metrics (breast_cancer): selective_risk_at80_breast_cancer=0.000000, coverage_at80_breast_cancer=0.710526, worst_group_selective_risk_breast_cancer=0.000000, deferral_rate_gap_breast_cancer=0.256769, auroc_breast_cancer=0.954545, target_coverage_breast_cancer=0.800000, actual_coverage_breast_cancer=0.710526, group_threshold_breast_cancer=13.270000

### diabetes (scripts/diabetes.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.750000 cal_accuracy=0.752809 cal_coverage=0.797753 policy_threshold=0.497682
Final metrics (diabetes): selective_risk_at80_diabetes=0.202532, coverage_at80_diabetes=0.887640, worst_group_selective_risk_diabetes=0.230769, deferral_rate_gap_diabetes=0.117886, auroc_diabetes=0.761204, target

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_selective.py",
4  "content": "class SelectivePolicy:\n    \"\"\"Adaptive score blending with fairness-interpolated subgroup thresholds.\n\n    The policy first learns a compact correctness score from probability-derived\n    features only, then adaptively blends it with raw classifier confidence to\n    avoid overfitting. Acceptance uses subgroup thresholds interpolated between a\n    global threshold and fully groupwise thresholds, with the interpolation weight\n    selected on calibration data to balance selective risk, worst-group risk,\n    coverage, and deferral parity.\n    \"\"\"\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.group_bias_: dict[int, float] = {}\n        self.meta_model_ = None\n        self.meta_weight_: float = 0.0\n        self.fairness_mix_: float = 0.0\n        self.strategy_name = \"adaptive_interpolated_group_thresholds\"\n\n    def _score_without_group_adjustment(self, probs: np.ndarray) -> np.ndarray:\n        base = np.max(probs, axis=1)\n        if self.meta_model_ is None or self.meta_weight_ <= 1e-8:\n            return np.clip(base, 1e-6, 1.0 - 1e-6)\n        feats = _confidence_features(probs, None, None)\n        meta = self.meta_model_.predict_proba(feats)[:, 1]\n        scores = self.meta_weight_ * meta + (1.0 - self.meta_weight_) * base\n        return np.clip(scores, 1e-6, 1.0 - 1e-6)\n\n    def fit(self, probs: np.ndarray, y_true: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> \"SelectivePolicy\":\n        del X\n        correct = (np.argmax(probs, axis=1) == y_true).astype(int)\n        base_scores = np.max(probs, axis=1)\n        self.group_thresholds_ = {}\n        self.group_bias_ = {}\n        self.meta_weight_ = 0.0\n        self.fairness_mix_ = 0.0\n\n        feats = _confidence_features(probs, None, None)\n        if len(np.unique(correct)) >= 2:\n            self.meta_model_ = Pipeline(\n                steps=[\n                    (\"scale\", StandardScaler()),\n                    (\n                        \"clf\",\n                        LogisticRegression(\n                            max_iter=1000,\n                            solver=\"lbfgs\",\n                            class_weight=\"balanced\",\n                            random_state=self.random_state,\n                        ),\n                    ),\n                ]\n            )\n            self.meta_model_.fit(feats, correct)\n            meta_scores = self.meta_model_.predict_proba(feats)[:, 1]\n            best_value = -float(\"inf\")\n            for weight in (0.0, 0.2, 0.4, 0.6, 0.8, 1.0):\n                blended = np.clip(weight * meta_scores + (1.0 - weight) * base_scores, 1e-6, 1.0 - 1e-6)\n                value = _safe_roc_auc(correct, blended) - 0.03 * float(np.mean((blended - correct) ** 2))\n                if value > best_value:\n                    best_value = value\n                    self.meta_weight_ = float(weight)\n        else:\n            self.meta_model_ = None\n\n        scores = self._score_without_group_adjustment(probs)\n        quantile = float(np.clip(1.0 - self.target_coverage, 0.0, 1.0))\n        self.threshold_ = float(np.quantile(scores, quantile))\n\n        group_ids = [int(group_id) for group_id in np.unique(groups)]\n        global_threshold = float(self.threshold_)\n        pure_group_thresholds = {\n            group_id: float(np.quantile(scores[groups == group_id], quantile)) for group_id in group_ids\n        }\n\n        best_obj = float(\"inf\")\n        best_thresholds = {group_id: global_threshold for group_id in group_ids}\n        best_mix = 0.0\n        for mix in (0.0, 0.25, 0.5, 0.75, 1.0):\n            candidate_thresholds = {\n                group_id: float((1.0 - mix) * global_threshold + mix * pure_group_thresholds[group_id])\n                for group_id in group_ids\n            }\n            thresholds = np.asarray([candidate_thresholds[int(group)] for group in groups], dtype=float)\n            accept = scores >= thresholds\n            coverage = float(accept.mean())\n            selective_risk = 1.0 - float(correct[accept].mean()) if accept.any() else 1.0\n            group_risks = []\n            deferrals = []\n            for group_id in group_ids:\n                mask = groups == group_id\n                group_accept = accept[mask]\n                n_accept = int(group_accept.sum())\n                if n_accept > 0:\n                    errors = float(np.sum(1 - correct[mask][group_accept]))\n                    risk = (errors + 0.5) / (n_accept + 1.0)\n                else:\n                    risk = 1.0\n                group_risks.append(risk)\n                deferrals.append(float(1.0 - group_accept.mean()))\n            obj = (\n                selective_risk\n                + 0.70 * max(group_risks)\n                + 0.35 * (max(deferrals) - min(deferrals) if deferrals else 0.0)\n                + 3.0 * abs(coverage - self.target_coverage)\n            )\n            if obj < best_obj:\n                best_obj = obj\n                best_thresholds = candidate_thresholds\n                best_mix = float(mix)\n\n        self.group_thresholds_ = best_thresholds\n        self.fairness_mix_ = best_mix\n        self.threshold_ = global_threshold\n        return self\n\n    def acceptance_score(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        del groups, X\n        return self._score_without_group_adjustment(probs)\n\n    def predict_accept(self, probs: np.ndarray, groups: np.ndarray, X: np.ndarray | None = None) -> np.ndarray:\n        del X\n        scores = self.acceptance_score(probs, groups, None)\n        if self.group_thresholds_:\n            thresholds = np.asarray([self.group_thresholds_.get(int(group), self.threshold_) for group in groups], dtype=float)\n            return scores >= thresholds\n        return scores >= self.threshold_\n\n    def calibration_summary(self) -> dict[str, float]:\n        summary = {\n            \"threshold\": float(self.threshold_),\n            \"meta_weight\": float(self.meta_weight_),\n            \"fairness_mix\": float(self.fairness_mix_),\n        }\n        for group_id, threshold in self.group_thresholds_.items():\n            summary[f\"threshold_group_{group_id}\"] = float(threshold)\n        return summary",
5  "after_line": 0,
6  "start_line": 161,
7  "end_line": 323
8}

Tool ResultStep 5

OK: Replaced lines 161..323 with 140 line(s) in scikit-learn/custom_selective.py. Editable range: 161–300.

[Current file: scikit-learn/custom_selective.py | editable: 161–300 | total: 444 lines]
   161  class SelectivePolicy:
   162      """Adaptive score blending with fairness-interpolated subgroup thresholds.
   163  
       ... (134 more lines) ...
   298          for group_id, threshold in self.group_thresholds_.items():
   299              summary[f"threshold_group_{group_id}"] = float(threshold)
   300          return summary

AssistantStep 6test

Tool ResultStep 6

[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)
Training progress:
TRAIN_METRICS: train_accuracy=0.991202 cal_accuracy=0.956140 cal_coverage=0.789474 policy_threshold=0.986454
Final metrics (breast_cancer): selective_risk_at80_breast_cancer=0.000000, coverage_at80_breast_cancer=0.710526, worst_group_selective_risk_breast_cancer=0.000000, deferral_rate_gap_breast_cancer=0.256769, auroc_breast_cancer=0.954545, target_coverage_breast_cancer=0.800000, actual_coverage_breast_cancer=0.710526, group_threshold_breast_cancer=13.270000

### diabetes (scripts/diabetes.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.750000 cal_accuracy=0.752809 cal_coverage=0.797753 policy_threshold=0.416491
Final metrics (diabetes): selective_risk_at80_diabetes=0.192308, coverage_at80_diabetes=

AssistantStep 7submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 7

[submit] Submitting result from test #1 as final.

### breast_cancer (scripts/breast_cancer.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.991202 cal_accuracy=0.956140 cal_coverage=0.842105 policy_threshold=0.997264
Final metrics (breast_cancer): selective_risk_at80_breast_cancer=0.000000, coverage_at80_breast_cancer=0.754386, worst_group_selective_risk_breast_cancer=0.000000, deferral_rate_gap_breast_cancer=0.054155, auroc_breast_cancer=0.959091, target_coverage_breast_cancer=0.800000, actual_coverage_breast_cancer=0.754386, group_threshold_breast_cancer=13.270000

### diabetes (scripts/diabetes.sh)
Training progress:
TRAIN_METRICS: train_accuracy=0.750000 cal_accuracy=0.752809 cal_coverage=0.797753 policy_threshold=0.447216
Final metrics (diabetes): selective_risk_at80_diabetes=0.194805, coverage_at80_diabetes=0.865169, worst_group_selective_risk_diabetes=0.250000, deferral_rate_gap_diabetes=0.204776, auroc_diabetes=0.803922, target_coverage_diabetes=0.800000, actual_coverage