Update task_template.py

task_template.py

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+"""
+Task 25 — Attribute Inference: Sample Submission
+=================================================
+
+Goal
+----
+Infer the `marital-status` of 100 records per model (10 models = 1 000 rows
+total) using only the 5 observable features in target_features.csv.
+
+You can query each model's oracle endpoint to get income predictions as an
+additional signal.  This sample shows:
+  1. How to query the oracle endpoint (POST /25-attribute-inference/predict).
+  2. How to build a valid submission CSV.
+  3. How to submit it to the evaluation endpoint.
+
+Submission CSV format
+---------------------
+Columns (exact order):
+    model_id, datapoint_id,
+    Divorced_prob, Married-civ-spouse_prob, Married-spouse-absent_prob,
+    Never-married_prob, Separated_prob, Widowed_prob
+
+Constraints:
+  - One row per (model_id, datapoint_id) pair  → 1 000 rows total.
+  - model_id  ∈ {0, …, 9},  datapoint_id ∈ {0, …, 99}.
+  - All six probability columns must be floats in [0, 1].
+  - Each row's six probabilities must sum to 1.
+
+Rate limits on the oracle endpoint
+-----------------------------------
+  - 900 s cooldown per model after a successful query.
+  - 120 s cooldown per model after a failed query.
+  - Each model has its own independent cooldown.
+  → You can query all 10 models without waiting, but only once per 15 min per model.
+"""
+
+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
+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 = "Datatset/target_features.csv"
+
+QUERY_ORACLE = True   # set True to query the oracle endpoint
+SUBMIT       = False  # set True to submit OUTPUT_PATH to the evaluator
+
+LABELS = [
+    "Divorced",
+    "Married-civ-spouse",
+    "Married-spouse-absent",
+    "Never-married",
+    "Separated",
+    "Widowed",
+]
+PROB_COLUMNS = [f"{l}_prob" for l in LABELS]
+FEATURE_COLUMNS = ["education", "educational-num", "race", "gender", "native-country"]
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def die(msg: str) -> None:
+    print(msg, file=sys.stderr)
+    sys.exit(1)
+
+
+def query_oracle(model_id: int, feature_dicts: list[dict]) -> list[list[float]]:
+    """
+    Query the oracle endpoint for one model.
+
+    Returns a list of [p_<=50K, p_>50K] per record.
+    Raises on HTTP errors.
+    """
+    resp = requests.post(
+        f"{BASE_URL}/25-attribute-inference/predict",
+        json={"model_id": 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}")
+    resp.raise_for_status()
+    data = resp.json()
+    return data["probabilities"]   # list of [p_<=50K, p_>50K]
+
+
+# ---------------------------------------------------------------------------
+# Step 1 — Load target features
+# ---------------------------------------------------------------------------
+
+target_df = pd.read_csv(TARGET_FEATURES_PATH)
+print(f"Loaded {len(target_df)} target records across "
+      f"{target_df['model_id'].nunique()} models.")
+
+# ---------------------------------------------------------------------------
+# Step 2 — Optionally query the oracle for income probabilities
+# ---------------------------------------------------------------------------
+
+# oracle_probs[model_id] = list of [p_<=50K, p_>50K] for that model's records
+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")
+
+        print(f"Querying oracle for model {model_id} ({len(feature_dicts)} records)...")
+        probs = query_oracle(model_id, feature_dicts)
+        oracle_probs[model_id] = probs
+        print(f"  → received income probabilities, e.g. first record: {probs[0]}")
+
+# ---------------------------------------------------------------------------
+# Step 3 — Build marital-status predictions
+#
+# Replace this section with your actual inference logic.
+# This baseline simply assigns uniform probabilities regardless of oracle output.
+# A better approach would use oracle income predictions + features as signals.
+# ---------------------------------------------------------------------------
+
+rows = []
+for model_id in sorted(target_df["model_id"].unique()):
+    subset = target_df[target_df["model_id"] == model_id].sort_values("datapoint_id")
+
+    for i, (_, record) in enumerate(subset.iterrows()):
+        # --- YOUR INFERENCE LOGIC HERE ---
+        # You have access to:
+        #   record[FEATURE_COLUMNS]  — the 5 observable features
+        #   oracle_probs[model_id][i]  — income probs [p_<=50K, p_>50K] (if queried)
+        #
+        # Baseline: uniform distribution over all 6 marital-status labels.
+        marital_probs = np.ones(len(LABELS)) / len(LABELS)
+        # ---------------------------------
+
+        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), \
+    "Probabilities do not sum to 1 in all rows."
+
+submission.to_csv(OUTPUT_PATH, index=False)
+print(f"\nSubmission saved to '{OUTPUT_PATH}' ({len(submission)} rows).")
+print(submission.head(3).to_string(index=False))
+
+# ---------------------------------------------------------------------------
+# Step 4 — Submit to the evaluation endpoint
+# ---------------------------------------------------------------------------
+
+if SUBMIT:
+    if not os.path.isfile(OUTPUT_PATH):
+        die(f"Submission file not found: {OUTPUT_PATH}")
+
+    print(f"\nSubmitting '{OUTPUT_PATH}' to /{TASK_ID}...")
+    try:
+        with open(OUTPUT_PATH, "rb") as f:
+            resp = requests.post(
+                f"{BASE_URL}/submit/{TASK_ID}",
+                headers={"X-API-Key": API_KEY},
+                files={"file": (os.path.basename(OUTPUT_PATH), f, "text/csv")},
+                timeout=(10, 120),
+            )
+        try:
+            body = resp.json()
+        except Exception:
+            body = {"raw_text": resp.text}
+
+        if resp.status_code == 413:
+            die("Upload rejected: file too large (HTTP 413).")
+
+        resp.raise_for_status()
+
+        print("Successfully submitted.")
+        print("Server response:", body)
+        if body.get("submission_id"):
+            print(f"Submission ID: {body['submission_id']}")
+
+    except requests.exceptions.RequestException as e:
+        detail = getattr(e, "response", None)
+        print(f"Submission error: {e}")
+        if detail is not None:
+            try:
+                print("Server response:", detail.json())
+            except Exception:
+                print("Server response (text):", detail.text)
+        sys.exit(1)