Update task_template.py

task_template.py

@@ -1,5 +1,5 @@
 """
-Task 2 — Attribute Inference: Sample Submission
+Task 2 — Attribute Inference: Querying and Sample Submission
 =================================================
 
 Goal
@@ -36,35 +36,21 @@ Rate limits on the oracle endpoint
 
 import os
 import sys
-import time
 
 import numpy as np
 import pandas as pd
 import requests
 
+
 # ---------------------------------------------------------------------------
 # Configuration — fill these in before running
 # ---------------------------------------------------------------------------
 
 BASE_URL = "http://35.192.205.84:80"
-API_KEY  = "your-api-key-here"             # replace with your team's API key
+API_KEY  = "<YOUR_API_KEY>"
 TASK_ID  = "25-attribute-inference"
-ZIP_FILE = "Dataset.zip"          # Path to dataset zip file
-DATASET_DIR = Path("Dataset")     # Folder after extraction
 OUTPUT_PATH = "submission.csv"
-
-# ----------------------------
-# UNZIP DATASET
-# ----------------------------
-if not DATASET_DIR.exists():
-    print("Unzipping dataset...")
-    with zipfile.ZipFile(ZIP_FILE, "r") as zip_ref:
-        zip_ref.extractall(DATASET_DIR)
-else:
-    print("Dataset already extracted.")
-
-
-TARGET_FEATURES_PATH = "Dataset/target_features.csv"
+TARGET_FEATURES_PATH = "target_features.csv"
 
 QUERY_ORACLE = True   # set True to query the oracle endpoint
 SUBMIT       = False  # set True to submit OUTPUT_PATH to the evaluator
@@ -79,6 +65,8 @@ LABELS = [
 ]
 PROB_COLUMNS = [f"{l}_prob" for l in LABELS]
 FEATURE_COLUMNS = ["education", "educational-num", "race", "gender", "native-country"]
+NULL_KEYS = ["age", "capital-gain", "capital-loss", "hours-per-week",
+             "marital-status", "occupation", "workclass"]
 
 # ---------------------------------------------------------------------------
 # Helpers
@@ -98,16 +86,14 @@ def query_oracle(model_id: int, feature_dicts: list[dict]) -> list[list[float]]:
     """
     resp = requests.post(
         f"{BASE_URL}/25-attribute-inference/predict",
-        json={"model_id": model_id, "features": feature_dicts},
+        json={"model_id": int(model_id), "features": feature_dicts},
         headers={"X-API-Key": API_KEY},
         timeout=(10, 60),
     )
     if resp.status_code == 429:
-        detail = resp.json().get("detail", "")
-        die(f"Rate limited on model {model_id}: {detail}")
+        die(f"Rate limited on model {model_id}: {resp.json().get('detail', '')}")
     resp.raise_for_status()
-    data = resp.json()
-    return data["probabilities"]   # list of [p_<=50K, p_>50K]
+    return resp.json()["probabilities"]   # list of [p_<=50K, p_>50K]
 
 
 # ---------------------------------------------------------------------------
@@ -128,7 +114,14 @@ oracle_probs: dict[int, list[list[float]]] = {}
 if QUERY_ORACLE:
     for model_id in sorted(target_df["model_id"].unique()):
         subset = target_df[target_df["model_id"] == model_id].sort_values("datapoint_id")
-        feature_dicts = subset[FEATURE_COLUMNS].to_dict(orient="records")
+        feature_dicts = (
+            subset[FEATURE_COLUMNS]
+            .where(pd.notna(subset[FEATURE_COLUMNS]), None)
+            .to_dict(orient="records")
+        )
+        for d in feature_dicts:
+            for k in NULL_KEYS:
+                d[k] = None
 
         print(f"Querying oracle for model {model_id} ({len(feature_dicts)} records)...")
         probs = query_oracle(model_id, feature_dicts)
@@ -157,19 +150,15 @@ for model_id in sorted(target_df["model_id"].unique()):
         marital_probs = np.ones(len(LABELS)) / len(LABELS)
         # ---------------------------------
 
-        row = {
-            "model_id":    model_id,
-            "datapoint_id": int(record["datapoint_id"]),
-        }
+        row = {"model_id": model_id, "datapoint_id": int(record["datapoint_id"])}
         for label, prob in zip(LABELS, marital_probs):
             row[f"{label}_prob"] = float(prob)
         rows.append(row)
 
 submission = pd.DataFrame(rows, columns=["model_id", "datapoint_id"] + PROB_COLUMNS)
 
-# Sanity checks
 assert len(submission) == 1000, f"Expected 1000 rows, got {len(submission)}"
-assert np.allclose(submission[PROB_COLUMNS].sum(axis=1), 1.0), \
+assert np.allclose(submission[PROB_COLUMNS].to_numpy().sum(axis=1), 1.0), \
     "Probabilities do not sum to 1 in all rows."
 
 submission.to_csv(OUTPUT_PATH, index=False)