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graders.py

@@ -6,20 +6,40 @@ from tasks import TaskConfig
 
 
 SCORE_EPS = 1e-3
+SCORE_MARGIN = 1e-6
 
 
 def _clamp(value: float, low: float = SCORE_EPS, high: float = 1.0 - SCORE_EPS) -> float:
-    return max(low, min(high, value))
+    try:
+        numeric_value = float(value)
+    except (TypeError, ValueError):
+        numeric_value = low
+    if numeric_value <= low:
+        return low + SCORE_MARGIN
+    if numeric_value >= high:
+        return high - SCORE_MARGIN
+    return numeric_value
 
 
 def _strict_score(value: float) -> float:
     return _clamp(value, SCORE_EPS, 1.0 - SCORE_EPS)
 
 
+def _normalize_weights(weights: dict[str, float]) -> dict[str, float]:
+    total = sum(float(weight) for weight in weights.values())
+    if total <= 0:
+        return weights
+    return {metric_name: float(weight) / total for metric_name, weight in weights.items()}
+
+
+def _format_metric(value: float) -> float:
+    return float(f"{_clamp(value):.6f}")
+
+
 def grade_episode(task: TaskConfig, metrics: dict[str, Any]) -> float:
-    weights = task.score_weights
-    score = sum(weights.get(metric_name, 0.0) * _clamp(metrics.get(metric_name, 0.0)) for metric_name in weights)
-    return round(_strict_score(score), 4)
+    weights = _normalize_weights(task.score_weights)
+    score = sum(weight * _clamp(metrics.get(metric_name, 0.0)) for metric_name, weight in weights.items())
+    return float(f"{_strict_score(score):.6f}")
 
 
 def summarize_episode(total_reward: float, state_history: list[dict[str, Any]], terminal_outcome: str) -> dict[str, Any]:
@@ -51,16 +71,16 @@ def summarize_episode(total_reward: float, state_history: list[dict[str, Any]],
     timeliness = _clamp(1.0 - (first_meaningful_step / step_count))
     stability = _clamp(sum(item.get("stability_score", 0.0) for item in state_history) / step_count)
     safety = _clamp(1.0 - (safety_violations / step_count))
-    outcome = 1.0 - SCORE_EPS if terminal_outcome == "survived" else SCORE_EPS
+    outcome = _format_metric(1.0 if terminal_outcome == "survived" else 0.0)
     return {
         "steps": step_count,
-        "avg_reward": _clamp(total_reward / step_count),
-        "detection": round(_clamp(detection), 4),
-        "lab_workup": round(_clamp(lab_workup), 4),
-        "treatment": round(_clamp(treatment), 4),
-        "timeliness": round(_clamp(timeliness), 4),
-        "stability": round(_clamp(stability), 4),
-        "safety": round(_clamp(safety), 4),
-        "safety_violation_rate": _clamp(safety_violations / step_count),
+        "avg_reward": _format_metric(total_reward / step_count),
+        "detection": _format_metric(detection),
+        "lab_workup": _format_metric(lab_workup),
+        "treatment": _format_metric(treatment),
+        "timeliness": _format_metric(timeliness),
+        "stability": _format_metric(stability),
+        "safety": _format_metric(safety),
+        "safety_violation_rate": _format_metric(safety_violations / step_count),
         "outcome": outcome,
     }