Create scorer.py

scorer.py

@@ -0,0 +1,61 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+VALID_AXES = {"thermal","cooling","mount","metrology","contamination","none"}
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def parse(prediction: str):
+    # expected: drift_score,drift_flag,dominant_axis
+    try:
+        parts = [p.strip() for p in prediction.split(",")]
+        drift_score = float(parts[0])
+        drift_flag = int(parts[1])
+        axis = parts[2].lower()
+        return drift_score, drift_flag, axis
+    except Exception:
+        return None, None, None
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    ds, df, ax = parse(prediction or "")
+    if ds is None:
+        return ScoreResult(0.0, {"error":"parse failure"})
+
+    true_ds = sample.get("overlay_drift_score","")
+    true_df = sample.get("drift_flag","")
+    true_ax = str(sample.get("dominant_cause_axis","")).lower()
+
+    try:
+        true_ds = float(true_ds)
+        true_df = int(true_df)
+    except:
+        true_ds = None
+
+    if true_ds is None:
+        valid = (0 <= ds <= 1) and (df in (0,1)) and (ax in VALID_AXES)
+        return ScoreResult(1.0 if valid else 0.0, {"mode":"format_only"})
+
+    err = abs(true_ds - ds)
+    s = max(0.0, 1.0 - err)
+    if df == true_df:
+        s += 0.20
+    if ax == true_ax:
+        s += 0.20
+
+    return ScoreResult(min(1.0, s), {
+        "id": sample.get("id"),
+        "pred_drift_score": ds,
+        "true_drift_score": true_ds,
+        "pred_flag": df,
+        "true_flag": true_df,
+        "pred_axis": ax,
+        "true_axis": true_ax
+    })
+
+def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
+    if not results:
+        return {"mean":0.0,"n":0}
+    return {"mean": sum(r.score for r in results)/len(results), "n": len(results)}