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task1_easy.py
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Task 1 (Easy) — Cognitive Stage Classification
The agent sees a SINGLE snapshot of behavioral metrics and must output
the correct Alzheimer's stage (0–4).
Grading:
Exact match → 1.00
Off by 1 stage → 0.50
Off by 2 stages → 0.20
Off by 3+ stages → 0.00
"""
from __future__ import annotations
from typing import List, Dict, Any
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from env.cognitive_env import CogTraceEnv
from env.patient_simulator import PatientConfig, make_easy_patient
TASK_ID = "task1_easy"
TASK_NAME = "Cognitive Stage Classification"
DIFFICULTY = "easy"
DESCRIPTION = (
"Given a single snapshot of a patient's behavioral metrics, "
"classify their Alzheimer's stage (0=healthy, 1=very mild, "
"2=mild, 3=moderate, 4=severe)."
)
def _stage_score(predicted: int, true: int) -> float:
diff = abs(int(predicted) - int(true))
if diff == 0: return 1.00
if diff == 1: return 0.50
if diff == 2: return 0.20
return 0.00
def run_episode(env: CogTraceEnv, agent_fn) -> Dict[str, Any]:
"""
Run a single-step episode and collect trajectory.
agent_fn : callable(observation_dict) -> int
The agent function. Receives the observation as a plain dict,
must return an integer 0–4 representing the predicted stage.
(This is NOT the action space — Task 1 overloads the action
to mean "stage prediction".)
"""
obs = env.reset()
predicted_stage = int(agent_fn(obs.model_dump()))
predicted_stage = max(0, min(4, predicted_stage))
true_stage = env._sim.true_stage(0)
score = _stage_score(predicted_stage, true_stage)
return {
"predicted_stage": predicted_stage,
"true_stage": true_stage,
"score": score,
"observation": obs.model_dump(),
}
def grade(trajectory: List[Dict[str, Any]]) -> float:
"""
Grade a list of trajectory records (one per patient seed).
Each record must contain:
- "predicted_stage" : int (0–4)
- "true_stage" : int (0–4)
Returns mean score across all records, in [0.0, 1.0].
"""
if not trajectory:
return 0.0
scores = [_stage_score(r["predicted_stage"], r["true_stage"]) for r in trajectory]
return round(sum(scores) / len(scores), 4)
def build_env(seed: int = 0) -> CogTraceEnv:
"""Build a Task-1 environment for a given seed."""
sim = make_easy_patient(seed)
cfg = sim.config
return CogTraceEnv(config=cfg)
if __name__ == "__main__":
# Quick smoke-test with a random-guess agent
results = []
for seed in range(10):
env = build_env(seed)
sim = make_easy_patient(seed)
true_s = sim.true_stage(0)
import random
result = run_episode(env, agent_fn=lambda obs: random.randint(0, 4))
results.append(result)
final = grade(results)
print(f"[Task 1] Random baseline score: {final:.4f}")
print("Expected ~0.25 (random on 5 classes with partial credit)")
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