Delete sample_submission.py

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 texas_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 140 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, "texas_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()