Update sample_submission.py

sample_submission.py

@@ -20,14 +20,21 @@ Where score is a float in [0, 1]:
     - Higher score = more likely World A (70% male training data)
     - Lower score  = more likely World B (50% male training data)
     - 0.5 = uncertain (this is the default for missing model_ids)
-    - Do not submit scores of exactly 0 or 1
 
 All 200 model_ids should be included. Missing ones default to 0.5.
 
+API response format:
+    {
+        "model_id": 0,
+        "batch_size": 100,
+        "labels": [[4, 7], [2, 9], ...],          # top-2 predicted classes per record
+        "probs":  [[0.52, 0.21], [0.44, 0.18], ...]  # top-2 softmax probabilities per record
+    }
+
 API rate limits:
-    - Per model: 15 minute cooldown after a successful query
+    - Per model: 2 minute cooldown after a successful query
     - Failed requests: 2 minute cooldown
-    - Max batch size: 100 records per request
+    - Max batch size: 500 records per request
 """
 
 import csv
@@ -53,8 +60,14 @@ OUTPUT_CSV     = "submission.csv"
 HEADERS = {"X-API-Key": API_KEY, "Content-Type": "application/json"}
 
 # ── Query API ──────────────────────────────────────────────────────────────────
-def query_model(model_id: int, features: list) -> list:
-    """Query the predict API and return predicted labels."""
+def query_model(model_id: int, features: list) -> dict:
+    """
+    Query the predict API for one model.
+
+    Returns a dict with:
+        labels: list[list[int]]   — top-2 predicted classes per record
+        probs:  list[list[float]] — top-2 softmax probabilities per record
+    """
     resp = requests.post(
         f"{BASE_URL}/23-property-inference/predict",
         headers=HEADERS,
@@ -62,16 +75,20 @@ def query_model(model_id: int, features: list) -> list:
         timeout=30,
     )
     resp.raise_for_status()
-    return resp.json()["labels"]
+    data = resp.json()
+    return {"labels": data["labels"], "probs": data["probs"]}
 
 
 # ── Score computation (replace with your own method) ───────────────────────────
-def compute_score(labels_a: list, labels_b: list = None) -> float:
+def compute_score(labels: list, probs: list) -> float:
     """
     Compute a confidence score in [0, 1] that a model belongs to World A.
 
-    This baseline compares label distributions across two queries — you should
-    replace this with your own property inference method.
+    You have access to:
+        labels: list[list[int]]   — top-2 predicted class indices per record
+        probs:  list[list[float]] — top-2 softmax probabilities per record
+
+    This baseline returns a random score — replace with your actual method.
 
     Returns:
         float in [0, 1] — higher means more likely World A
@@ -85,14 +102,14 @@ def compute_score(labels_a: list, labels_b: list = None) -> float:
 def main():
     predictions = {}
 
-    # Use first 100 probe records (max batch size)
+    # Use first 100 probe records (max batch size is 500)
     probe_batch = PROBE_FEATURES[:100]
 
     for model_id in MODEL_IDS:
         print(f"Querying model {model_id}...")
         try:
-            labels = query_model(model_id, probe_batch)
-            score = compute_score(labels)
+            result = query_model(model_id, probe_batch)
+            score  = compute_score(result["labels"], result["probs"])
         except requests.exceptions.HTTPError as e:
             if e.response.status_code == 429:
                 print(f"  Rate limited on model {model_id} — skipping (will default to 0.5)")
@@ -100,6 +117,7 @@ def main():
             else:
                 raise
         predictions[model_id] = score
+        print(f"  score={score:.4f}")
 
     # Write CSV
     with open(OUTPUT_CSV, "w", newline="") as f:
@@ -125,4 +143,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()