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- Upload 4 files (925a10bf4b342895ffd5e48401b205344f27451c)


Co-authored-by: Aditya Kumar <adyk07@users.noreply.huggingface.co>

auxiliary.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0326887f8592d02b364f1609ee11e48fc344c830f3181844e275f37477aea27
+size 480514

model_ids.json

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+[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]

preprocessing.json

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+{
+  "num_features": 10,
+  "num_classes": 100,
+  "col_sex_code": 0,
+  "col_total_charges": 8,
+  "sex_code_encoding": {
+    "0.0": "Male",
+    "1.0": "Female"
+  },
+  "target": "PRINC_SURG_PROC_CODE",
+  "note": "10 features (THCIC_ID excluded). Normalized by column max. Labels are procedure codes 0-99.",
+  "columns": [
+    "SEX_CODE",
+    "TYPE_OF_ADMISSION",
+    "SOURCE_OF_ADMISSION",
+    "LENGTH_OF_STAY",
+    "PAT_AGE",
+    "PAT_STATUS",
+    "RACE",
+    "ETHNICITY",
+    "TOTAL_CHARGES",
+    "ADMITTING_DIAGNOSIS"
+  ]
+}

sample_submission.py

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+"""
+Sample submission script for Task 23: Property Inference.
+
+Auxiliary dataset: https://huggingface.co/datasets/SprintML/Property_Inference
+Download auxiliary.npz from there and place it in the same directory as this script.
+
+Steps:
+  1. Query the predict API for each model using auxiliary probe data
+  2. Compute a confidence score per model (higher = more likely World A)
+  3. Save predictions to submission.csv
+  4. Submit submission.csv to the hackathon platform
+
+Submission format (submission.csv):
+    model_id,score
+    0,0.73
+    1,0.41
+    ...
+
+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 rate limits:
+    - Per model: 15 minute cooldown after a successful query
+    - Failed requests: 2 minute cooldown
+    - Max batch size: 100 records per request
+"""
+
+import csv
+import json
+import os
+
+import numpy as np
+import requests
+
+# ── Configuration ──────────────────────────────────────────────────────────────
+BASE_URL = "http://35.192.205.84:80"
+API_KEY  = "YOUR_API_KEY_HERE"
+TASK_ID  = "23-property-inference"
+
+# Paths (relative to this script)
+HERE          = os.path.dirname(os.path.abspath(__file__))
+MODEL_IDS     = json.load(open(os.path.join(HERE, "model_ids.json")))
+AUXILIARY_NPZ = np.load(os.path.join(HERE, "auxiliary.npz"))
+
+PROBE_FEATURES = AUXILIARY_NPZ["features"].tolist()  # shape (10000, 10), already normalized
+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."""
+    resp = requests.post(
+        f"{BASE_URL}/23-property-inference/predict",
+        headers=HEADERS,
+        json={"model_id": model_id, "features": features},
+        timeout=30,
+    )
+    resp.raise_for_status()
+    return resp.json()["labels"]
+
+
+# ── Score computation (replace with your own method) ───────────────────────────
+def compute_score(labels_a: list, labels_b: list = None) -> 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.
+
+    Returns:
+        float in [0, 1] — higher means more likely World A
+    """
+    # Placeholder: random score. Replace with your actual method.
+    score = float(np.random.uniform(0, 1))
+    return float(np.clip(score, 0.001, 0.999))
+
+
+# ── Main ───────────────────────────────────────────────────────────────────────
+def main():
+    predictions = {}
+
+    # Use first 100 probe records (max batch size)
+    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)
+        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)")
+                continue
+            else:
+                raise
+        predictions[model_id] = score
+
+    # Write CSV
+    with open(OUTPUT_CSV, "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(["model_id", "score"])
+        for model_id in MODEL_IDS:
+            score = predictions.get(model_id, 0.5)
+            writer.writerow([model_id, round(score, 6)])
+
+    print(f"\nSaved {len(predictions)} predictions to {OUTPUT_CSV}")
+    print(f"Missing models (defaulted to 0.5): {len(MODEL_IDS) - len(predictions)}")
+
+    # Submit
+    print("\nSubmitting...")
+    with open(OUTPUT_CSV, "rb") as f:
+        resp = requests.post(
+            f"{BASE_URL}/submit/{TASK_ID}",
+            headers={"X-API-Key": API_KEY},
+            files={"file": (OUTPUT_CSV, f, "text/csv")},
+            timeout=120,
+        )
+    print("Response:", resp.json())
+
+
+if __name__ == "__main__":
+    main()