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Unit Tests for Task Graders
Tests grading logic for easy, medium, and hard tasks.
"""
import pytest
from adaptive_alert_triage.models import Action, Alert, Reward
from tasks.easy import EasyTaskGrader
from tasks.medium import MediumTaskGrader
from tasks.hard import HardTaskGrader
class TestEasyTaskGrader:
"""Test easy task grading logic."""
def test_critical_alert_correct(self):
"""Test correct handling of critical alert."""
grader = EasyTaskGrader()
alert = Alert(
id="alert_001",
visible_severity=0.85,
confidence=0.9,
alert_type="CPU",
age=1,
true_severity=0.90, # Critical
)
action = Action(alert_id="alert_001", action_type="INVESTIGATE")
reward = Reward(value=10.0)
score = grader.grade_action(action, alert, reward)
assert score == 1.0, "Should get full score for correct action"
assert grader.correct_actions == 1
assert grader.total_actions == 1
def test_critical_alert_incorrect(self):
"""Test incorrect handling of critical alert (ignored)."""
grader = EasyTaskGrader()
alert = Alert(
id="alert_002",
visible_severity=0.7,
confidence=0.8,
alert_type="SECURITY",
age=2,
true_severity=0.95, # Critical
)
action = Action(alert_id="alert_002", action_type="IGNORE")
reward = Reward(value=-8.0)
score = grader.grade_action(action, alert, reward)
assert score == 0.0, "Should get zero score for missed critical"
assert grader.correct_actions == 0
assert grader.total_actions == 1
def test_false_positive_correct(self):
"""Test correct handling of false positive (ignored)."""
grader = EasyTaskGrader()
alert = Alert(
id="alert_003",
visible_severity=0.3,
confidence=0.4,
alert_type="DISK",
age=0,
true_severity=0.15, # False positive
)
action = Action(alert_id="alert_003", action_type="IGNORE")
reward = Reward(value=3.0)
score = grader.grade_action(action, alert, reward)
assert score == 1.0, "Should get full score for ignoring FP"
assert grader.correct_actions == 1
def test_episode_score_calculation(self):
"""Test episode score aggregation."""
grader = EasyTaskGrader()
# 3 actions: 2 correct, 1 incorrect
alerts_actions = [
(Alert(id="a1", visible_severity=0.9, confidence=0.9, alert_type="CPU",
age=1, true_severity=0.9), "INVESTIGATE", True),
(Alert(id="a2", visible_severity=0.3, confidence=0.4, alert_type="DISK",
age=0, true_severity=0.2), "IGNORE", True),
(Alert(id="a3", visible_severity=0.8, confidence=0.8, alert_type="SECURITY",
age=1, true_severity=0.95), "IGNORE", False),
]
for alert, action_type, _ in alerts_actions:
action = Action(alert_id=alert.id, action_type=action_type)
reward = Reward(value=0.0)
grader.grade_action(action, alert, reward)
score = grader.get_episode_score()
assert abs(score - 2/3) < 0.01, f"Expected 0.667, got {score}"
def test_metrics_breakdown(self):
"""Test detailed metrics generation."""
grader = EasyTaskGrader()
alert = Alert(
id="a1", visible_severity=0.9, confidence=0.9, alert_type="CPU",
age=1, true_severity=0.9
)
action = Action(alert_id="a1", action_type="INVESTIGATE")
reward = Reward(value=10.0)
grader.grade_action(action, alert, reward)
metrics = grader.get_metrics()
assert "overall_score" in metrics
assert "correct_actions" in metrics
assert "critical_accuracy" in metrics
assert "action_breakdown" in metrics
class TestMediumTaskGrader:
"""Test medium task grading logic with resource constraints."""
def test_productive_investigation(self):
"""Test high-value investigation scores well."""
grader = MediumTaskGrader(max_investigations_per_step=3)
alert = Alert(
id="alert_001",
visible_severity=0.85,
confidence=0.9,
alert_type="CPU",
age=1,
true_severity=0.90,
)
action = Action(alert_id="alert_001", action_type="INVESTIGATE")
reward = Reward(value=10.0)
contribution = grader.grade_action(action, alert, reward)
assert contribution > 0.0, "High-value investigation should contribute positively"
assert grader._total_investigations == 1
def test_wasteful_investigation(self):
"""Test investigation on false positive is penalized."""
grader = MediumTaskGrader(max_investigations_per_step=3)
alert = Alert(
id="alert_002",
visible_severity=0.3,
confidence=0.4,
alert_type="DISK",
age=0,
true_severity=0.15, # False positive
)
action = Action(alert_id="alert_002", action_type="INVESTIGATE")
reward = Reward(value=-2.0)
contribution = grader.grade_action(action, alert, reward)
assert contribution == 0.0, "Wasteful investigation should give zero contribution"
assert grader._unnecessary_invest == 1
def test_resource_efficiency_calculation(self):
"""Test resource efficiency metric."""
grader = MediumTaskGrader(max_investigations_per_step=3)
# 2 productive investigations, 1 wasteful
alerts_actions = [
(0.9, "INVESTIGATE", True), # Productive
(0.8, "INVESTIGATE", True), # Productive
(0.15, "INVESTIGATE", False), # Wasteful
]
for true_sev, action_type, _ in alerts_actions:
alert = Alert(
id=f"a_{true_sev}", visible_severity=true_sev, confidence=0.8,
alert_type="CPU", age=1, true_severity=true_sev
)
action = Action(alert_id=alert.id, action_type=action_type)
reward = Reward(value=0.0)
grader.grade_action(action, alert, reward)
efficiency = grader.calculate_resource_efficiency()
assert abs(efficiency - 2/3) < 0.01, f"Expected 0.667, got {efficiency}"
def test_episode_score_with_efficiency(self):
"""Test that episode score considers efficiency factor."""
grader = MediumTaskGrader(max_investigations_per_step=3)
# Add some actions
alert = Alert(
id="a1", visible_severity=0.9, confidence=0.9, alert_type="CPU",
age=1, true_severity=0.9
)
action = Action(alert_id="a1", action_type="INVESTIGATE")
reward = Reward(value=10.0)
grader.grade_action(action, alert, reward)
score = grader.get_episode_score()
assert 0.0 <= score <= 1.0, "Score should be normalized"
def test_critical_missed_penalty(self):
"""Test missing critical alerts incurs penalty."""
grader = MediumTaskGrader(max_investigations_per_step=3)
alert = Alert(
id="a1", visible_severity=0.8, confidence=0.8, alert_type="SECURITY",
age=1, true_severity=0.95 # Critical
)
action = Action(alert_id="a1", action_type="IGNORE")
reward = Reward(value=-8.0)
grader.grade_action(action, alert, reward)
assert grader._critical_missed == 1
# Score should be penalized
score = grader.get_episode_score()
assert score < 0.5, "Missing critical should heavily impact score"
class TestHardTaskGrader:
"""Test hard task grading with correlation detection."""
def test_correlation_detection(self):
"""Test bonus for handling correlated alerts."""
correlation_chains = [["alert_001", "alert_002", "alert_003"]]
grader = HardTaskGrader()
grader.update_correlation_state(correlation_chains)
alert = Alert(
id="alert_001",
visible_severity=0.8,
confidence=0.85,
alert_type="CPU",
age=1,
true_severity=0.85,
is_correlated=True,
)
action = Action(alert_id="alert_001", action_type="INVESTIGATE")
reward = Reward(value=10.0)
contribution = grader.grade_action(action, alert, reward)
assert contribution >= alert.true_severity, "Should be rewarded proportionally for chain trigger"
def test_failure_prevention_bonus(self):
"""Test bonus for preventing cascading failures."""
correlation_chains = [["alert_001", "alert_002", "alert_003"]]
grader = HardTaskGrader()
grader.update_correlation_state(correlation_chains)
# Handle first alert in chain (early detection)
alert = Alert(
id="alert_001",
visible_severity=0.75,
confidence=0.85,
alert_type="CPU",
age=1,
true_severity=0.80,
is_correlated=True,
)
action = Action(alert_id="alert_001", action_type="INVESTIGATE")
reward = Reward(value=10.0)
grader.grade_action(action, alert, reward)
m = grader.get_metrics()
assert m["chains_stopped"] >= 1, "Should register failure prevention"
def test_system_failure_penalty(self):
"""Test heavy penalty for system failures."""
grader = HardTaskGrader()
# Record a failure
grader.record_failures(1)
assert grader._system_failures == 1
# Stability score should be reduced
stability = grader.calculate_stability_score()
assert stability < 1.0
def test_missed_correlated_alert_penalty(self):
"""Test extra penalty for missing correlated alerts."""
correlation_chains = [["alert_001", "alert_002"]]
grader = HardTaskGrader()
grader.update_correlation_state(correlation_chains)
alert = Alert(
id="alert_001",
visible_severity=0.7,
confidence=0.8,
alert_type="CPU",
age=1,
true_severity=0.85,
is_correlated=True,
)
action = Action(alert_id="alert_001", action_type="IGNORE")
reward = Reward(value=-8.0)
contribution = grader.grade_action(action, alert, reward)
# Should have negative contribution for missing correlated critical
assert contribution < -0.2, f"Should have extra penalty for correlated miss, got {contribution}"
def test_correlation_detection_rate(self):
"""Test calculation of correlation detection rate."""
correlation_chains = [
["alert_001", "alert_002"],
["alert_003", "alert_004"],
]
grader = HardTaskGrader()
grader.update_correlation_state(correlation_chains)
# Handle one chain
alert = Alert(id="alert_001", visible_severity=0.8, confidence=0.85, alert_type="CPU", age=1, true_severity=0.85, is_correlated=True)
grader.grade_action(Action(alert_id="alert_001", action_type="INVESTIGATE"), alert, Reward(value=0))
rate = grader.calculate_correlation_detection_rate()
assert abs(rate - 0.5) < 0.01, "Should detect 50% of chains"
def test_stability_score_perfect(self):
"""Test perfect stability (zero failures)."""
grader = HardTaskGrader()
stability = grader.calculate_stability_score()
assert stability == 1.0, "Zero failures should give perfect stability"
def test_stability_score_degraded(self):
"""Test degraded stability with failures."""
grader = HardTaskGrader()
# Multiple failures
for _ in range(3):
grader.record_failures(1)
stability = grader.calculate_stability_score()
assert stability < 1.0, "Failures should reduce stability"
def test_grader_reset():
"""Test that graders can be reset between episodes."""
grader = EasyTaskGrader()
# Do some actions
alert = Alert(
id="a1", visible_severity=0.9, confidence=0.9, alert_type="CPU",
age=1, true_severity=0.9
)
action = Action(alert_id="a1", action_type="INVESTIGATE")
reward = Reward(value=10.0)
grader.grade_action(action, alert, reward)
assert grader.total_actions == 1
# Reset
grader.reset()
assert grader.total_actions == 0
assert grader.correct_actions == 0
assert len(grader.action_history) == 0
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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