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tests/test_clm.py — unit tests for the Cognitive Load Manager environment.
Run with: pytest tests/test_clm.py -v
"""
import sys, os, pytest
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models import (
Action, Task, EnvState, CLMEnvironment,
generate_tasks, deterministic_grader, grader,
PRIORITY_WEIGHT,
)
from grader.clm_graders import (
EasyGrader, MediumGrader, HardGrader, ExpertGrader, _from_trajectory,
)
# ─────────────────────────────────────────────────────────────────────────────
# FIX 2 — Procedural generation
# ─────────────────────────────────────────────────────────────────────────────
class TestProceduralGeneration:
def test_seed_produces_same_tasks(self):
a = generate_tasks("medium", seed=7)
b = generate_tasks("medium", seed=7)
assert [t.model_dump() for t in a] == [t.model_dump() for t in b]
def test_different_seeds_differ(self):
results = set()
for s in range(20):
tasks = generate_tasks("medium", seed=s)
results.add(tuple(t.deadline for t in tasks))
assert len(results) > 1, "All seeds produced identical deadlines"
def test_task_counts(self):
assert len(generate_tasks("easy")) == 2
assert len(generate_tasks("medium")) == 5
assert len(generate_tasks("hard")) == 8
assert len(generate_tasks("expert")) == 10
def test_deadlines_positive_and_bounded(self):
"""Jitter can reorder adjacent deadlines, but all must be positive and sane."""
base_deadlines = {"medium": [14, 20, 28, 35, 45], "hard": [12, 16, 20, 24, 28, 32, 38, 46]}
for level, bases in base_deadlines.items():
for seed in range(20):
tasks = generate_tasks(level, seed=seed)
for t in tasks:
if t.deadline is not None:
assert t.deadline >= 1, f"Deadline must be >= 1, got {t.deadline}"
# Should be within ±5 of the nearest base (generous bound)
nearest = min(bases, key=lambda b: abs(b - t.deadline))
assert abs(t.deadline - nearest) <= 5, \
f"Deadline {t.deadline} too far from base {nearest}"
# ─────────────────────────────────────────────────────────────────────────────
# FIX 1 — Grader trajectory bug
# ─────────────────────────────────────────────────────────────────────────────
class TestGraderTrajectoryBug:
def test_empty_trajectory_returns_min(self):
assert grader({}) == 0.01
def test_missing_tasks_returns_min(self):
assert grader({"time_step": 50, "energy": 0.8}) == 0.01
def test_empty_tasks_list_returns_min(self):
assert grader({"tasks": [], "time_step": 50, "energy": 0.8}) == 0.01
def test_grader_class_empty_trajectory(self):
for cls in [EasyGrader, MediumGrader, HardGrader, ExpertGrader]:
score = cls()(trajectory={})
assert score == 0.01, f"{cls.__name__} returned {score} for empty trajectory"
def test_from_trajectory_empty(self):
score, success, msg = _from_trajectory({}, "easy")
assert score == 0.01
assert success is False
assert "empty trajectory" in msg
def test_real_trajectory_scores_above_min(self):
"""A trajectory with completed tasks should score > 0.01."""
tasks = generate_tasks("easy", seed=1)
for t in tasks:
t.progress = 1.0
traj = {"tasks": [t.model_dump() for t in tasks], "time_step": 20, "energy": 0.7}
assert grader(traj) > 0.01
# ─────────────────────────────────────────────────────────────────────────────
# Environment basics
# ─────────────────────────────────────────────────────────────────────────────
class TestReset:
def test_reset_produces_clean_state(self):
env = CLMEnvironment(tasks=generate_tasks("easy", seed=0), max_steps=50)
obs = env.reset()
assert env.state.energy == 1.0
assert env.state.stress == 0.0
assert env.state.time_step == 0
assert all(t.progress == 0.0 for t in env.state.tasks)
def test_reset_after_episode_clears_state(self):
env = CLMEnvironment(tasks=generate_tasks("easy", seed=0), max_steps=50)
env.reset()
for _ in range(10):
env.step(Action(type="work", task_id="e1"))
env.reset()
assert env.state.time_step == 0
assert env.state.energy == 1.0
# ─────────────────────────────────────────────────────────────────────────────
# Blocked-task penalty (Fix 3 indirectly — env mechanics)
# ─────────────────────────────────────────────────────────────────────────────
class TestBlockedTaskPenalty:
def test_working_on_blocked_task_gives_penalty(self):
tasks = generate_tasks("hard", seed=0)
env = CLMEnvironment(tasks=tasks, max_steps=50)
env.reset()
# h3 depends on h1 — h1 not done yet, so h3 is blocked
blocked = env._blocked_ids()
assert "h3" in blocked, "h3 should be blocked at episode start"
_, reward, _, _ = env.step(Action(type="work", task_id="h3"))
assert reward <= -0.15, f"Expected penalty for blocked task, got {reward}"
# ─────────────────────────────────────────────────────────────────────────────
# FIX 3 — Stochastic interruptions
# ─────────────────────────────────────────────────────────────────────────────
class TestStochasticInterruptions:
def test_hard_eventually_interrupts(self):
"""Over many seeds, at least one hard episode should fire an interruption."""
fired = False
for seed in range(50):
tasks = generate_tasks("hard", seed=seed)
env = CLMEnvironment(tasks=tasks, max_steps=50, seed=seed)
env.reset()
done = False
while not done:
_, _, done, _ = env.step(Action(type="work", task_id=tasks[0].id))
if env.state.interruption_count > 0:
fired = True
break
assert fired, "Expected at least one interruption across 50 hard seeds"
def test_interruptions_respect_budget(self):
"""Hard episodes should never exceed budget=2 interruptions."""
for seed in range(30):
tasks = generate_tasks("hard", seed=seed)
env = CLMEnvironment(tasks=tasks, max_steps=50, seed=seed)
env.reset()
done = False
while not done:
_, _, done, _ = env.step(Action(type="work", task_id=tasks[0].id))
assert env.state.interruption_count <= 2, \
f"Seed {seed}: got {env.state.interruption_count} interruptions, max is 2"
def test_no_interruptions_on_easy(self):
for seed in range(10):
tasks = generate_tasks("easy", seed=seed)
env = CLMEnvironment(tasks=tasks, max_steps=50, seed=seed)
env.reset()
done = False
while not done:
_, _, done, _ = env.step(Action(type="break"))
assert env.state.interruption_count == 0
# ─────────────────────────────────────────────────────────────────────────────
# Burnout terminates episode
# ─────────────────────────────────────────────────────────────────────────────
class TestBurnout:
def test_burnout_terminates_episode(self):
tasks = generate_tasks("easy", seed=0)
env = CLMEnvironment(tasks=tasks, max_steps=200)
env.reset()
env.state.energy = 0.08 # just above burnout threshold
done = False
for _ in range(5):
_, _, done, info = env.step(Action(type="work", task_id="e1"))
if done:
break
assert done, "Episode should terminate on burnout"
def test_burnout_applies_penalty(self):
tasks = generate_tasks("easy", seed=0)
env = CLMEnvironment(tasks=tasks, max_steps=200)
env.reset()
env.state.energy = 0.08
rewards = []
done = False
for _ in range(5):
_, r, done, _ = env.step(Action(type="work", task_id="e1"))
rewards.append(r)
if done:
break
assert any(r <= -0.5 for r in rewards), "Burnout should produce a large negative reward"
# ─────────────────────────────────────────────────────────────────────────────
# Grader score bounds
# ─────────────────────────────────────────────────────────────────────────────
class TestGraderBounds:
def test_grader_always_in_bounds(self):
for level in ["easy", "medium", "hard", "expert"]:
for seed in range(10):
tasks = generate_tasks(level, seed=seed)
for frac in [0.0, 0.3, 0.7, 1.0]:
for t in tasks:
t.progress = frac
score = deterministic_grader(tasks, time_step=30, final_energy=0.5)
assert 0.01 <= score <= 0.99, \
f"Score {score} out of bounds for {level} seed={seed} progress={frac}"
def test_grader_higher_completion_scores_higher(self):
tasks_low = generate_tasks("medium", seed=1)
tasks_high = generate_tasks("medium", seed=1)
for t in tasks_low: t.progress = 0.0
for t in tasks_high: t.progress = 1.0
assert deterministic_grader(tasks_high, 30, 0.7) > \
deterministic_grader(tasks_low, 30, 0.7)
# ─────────────────────────────────────────────────────────────────────────────
# FIX 6 — Partial observability
# ─────────────────────────────────────────────────────────────────────────────
class TestPartialObservability:
def test_observation_has_no_raw_floats(self):
env = CLMEnvironment(tasks=generate_tasks("easy", seed=0))
obs = env.reset()
vs = obs.visible_state
# energy_level and stress float must NOT appear in visible state
assert not hasattr(vs, "energy_level"), "energy_level float should not be in observation"
assert isinstance(vs.fatigue_level, str)
assert isinstance(vs.stress_level, str)
def test_fatigue_levels_are_valid(self):
env = CLMEnvironment(tasks=generate_tasks("easy", seed=0))
env.reset()
env.state.energy = 0.1 # should be "high" fatigue
obs = env._get_observation()
assert obs.visible_state.fatigue_level == "high"
env.state.energy = 0.5 # "medium"
assert env._get_observation().visible_state.fatigue_level == "medium"
env.state.energy = 0.9 # "low"
assert env._get_observation().visible_state.fatigue_level == "low"
def test_stress_levels_are_valid(self):
env = CLMEnvironment(tasks=generate_tasks("easy", seed=0))
env.reset()
env.state.stress = 0.8
assert env._get_observation().visible_state.stress_level == "critical"
env.state.stress = 0.5
assert env._get_observation().visible_state.stress_level == "elevated"
env.state.stress = 0.1
assert env._get_observation().visible_state.stress_level == "calm"
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