"""Tests on Lab 17's scripts 04 and 05, from their recorded run (October 2026): 24 made-up incident reports with hand-coded facts, each rated three times by two methods on two models, and three made-up vendors' questionnaire answers. Run: pytest test_incident_practice.py""" from datetime import datetime from incident_practice import add_business_days, changed_across_tries, clocks, decide, score, severity # The 24 reports' hand-coded facts, in the lab's order. #0 is the CEO's salary, #1 the knee surgery, # #17 the card numbers in logs. FACTS = [{'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': False, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': True, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 37, 'money_usd': 6400, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 412, 'money_usd': 31000, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': True, 'public': True, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 2300, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': False, 'people_affected': 80, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 3, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': False, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': True, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 1, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': True, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 900, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': False, 'people_affected': 40, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': True, 'people_affected': 8000, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 120, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 15000, 'money_usd': 45000, 'wrong_commitment': True, 'public': False, 'reached_users': True}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 1800, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': False, 'special_or_financial_data': False, 'people_affected': 0, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}, {'physical_harm': False, 'discrimination': False, 'personal_data_exposed': True, 'special_or_financial_data': False, 'people_affected': 2000, 'money_usd': 0, 'wrong_commitment': False, 'public': False, 'reached_users': False}] # Each report's level under the policy (incidents.severity of the facts above): 5 P1, 8 P2, 5 P3, 6 P4. GOLD = ['P2', 'P1', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2'] # The recorded ratings: three tries each. "name": the model picks the level. "facts": the model answers # the fact questions and the policy's table decides. RUNS = { 'name_nano': [['P2', 'P2', 'P2', 'P2', 'P2', 'P3', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P2', 'P2', 'P4', 'P2', 'P4', 'P4', 'P2'], ['P2', 'P2', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P2', 'P2', 'P2', 'P2', 'P4', 'P2', 'P3', 'P4', 'P2'], ['P2', 'P2', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P3', 'P2', 'P2', 'P3', 'P4', 'P2', 'P4', 'P4', 'P2']], 'name_mini': [['P1', 'P2', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P2', 'P3', 'P4', 'P2'], ['P1', 'P2', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P2', 'P3', 'P4', 'P2'], ['P2', 'P2', 'P3', 'P2', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P2', 'P3', 'P4', 'P2']], 'facts_nano': [['P1', 'P2', 'P3', 'P2', 'P1', 'P1', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2'], ['P2', 'P2', 'P3', 'P2', 'P1', 'P1', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2'], ['P2', 'P2', 'P3', 'P2', 'P1', 'P1', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P4', 'P4', 'P1', 'P1', 'P2', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2']], 'facts_mini': [['P1', 'P2', 'P2', 'P1', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P3', 'P4', 'P1', 'P1', 'P1', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2'], ['P1', 'P2', 'P2', 'P1', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P3', 'P4', 'P1', 'P1', 'P1', 'P1', 'P3', 'P4', 'P1', 'P3', 'P4', 'P2'], ['P1', 'P2', 'P2', 'P1', 'P2', 'P2', 'P4', 'P4', 'P3', 'P2', 'P3', 'P2', 'P3', 'P4', 'P1', 'P1', 'P1', 'P1', 'P2', 'P4', 'P1', 'P3', 'P4', 'P2']], } # The vendor questionnaire's rules: an answer listed here blocks the deal or needs a contract condition. RULES = {'location': {'us': 'condition', 'not_stated': 'condition'}, 'training': {'yes': 'blocker', 'not_stated': 'blocker', 'opt_out': 'condition'}, 'retention': {'more_than_30_days': 'condition', 'not_stated': 'condition'}, 'deletion': {'yes_unconfirmed': 'condition', 'not_stated': 'condition'}, 'soc2': {'type_1': 'condition', 'claimed_type_not_stated': 'condition', 'not_stated': 'blocker'}, 'breach_notice': {'no_fixed_time': 'condition', 'not_stated': 'blocker'}, 'subprocessors': {'published_no_notice': 'condition', 'not_stated': 'condition'}, 'dpa': {'not_stated': 'blocker'}, 'model_changes': {'no_notice': 'condition', 'not_stated': 'condition'}, 'encryption': {'vague': 'condition', 'not_stated': 'condition'}} # Kestrel's trust page, as a person reads it, and as gpt-5.4-mini read it (first try). KESTREL = {'location': 'us', 'training': 'opt_out', 'retention': 'more_than_30_days', 'deletion': 'yes_unconfirmed', 'soc2': 'type_1', 'breach_notice': 'no_fixed_time', 'subprocessors': 'published_no_notice', 'dpa': 'yes', 'model_changes': 'no_notice', 'encryption': 'both'} KESTREL_MINI = {'location': 'us', 'training': 'opt_out', 'retention': 'more_than_30_days', 'deletion': 'yes_confirmed', 'soc2': 'type_1', 'breach_notice': 'not_stated', 'subprocessors': 'published_no_notice', 'dpa': 'yes', 'model_changes': 'no_notice', 'encryption': 'both'} NORTHWIND = {'location': 'customer_choice', 'training': 'no', 'retention': '30_days_or_less', 'deletion': 'yes_confirmed', 'soc2': 'type_2', 'breach_notice': '72_hours_or_less', 'subprocessors': 'published_with_notice', 'dpa': 'yes', 'model_changes': 'advance_notice', 'encryption': 'both'} BRIGHTLINE = {'location': 'us', 'training': 'yes', 'retention': 'not_stated', 'deletion': 'not_stated', 'soc2': 'claimed_type_not_stated', 'breach_notice': 'not_stated', 'subprocessors': 'not_stated', 'dpa': 'not_stated', 'model_changes': 'no_notice', 'encryption': 'vague'} def test_the_policy_gives_every_reports_level(): assert [severity(f) for f in FACTS] == GOLD assert [GOLD.count(p) for p in ("P1", "P2", "P3", "P4")] == [5, 8, 5, 6] def test_one_fact_decides_the_knee_surgery(): knee = FACTS[1] assert severity(knee) == "P1" assert severity(dict(knee, special_or_financial_data=False)) == "P2" def test_naming_the_level_first_try(): assert score(RUNS["name_nano"][0], GOLD) == {"right": 17, "too_low": 5, "too_high": 2, "p1_found": 2} assert score(RUNS["name_mini"][0], GOLD) == {"right": 20, "too_low": 2, "too_high": 2, "p1_found": 3} def test_facts_first_rates_fewer_too_low_and_more_too_high(): assert score(RUNS["facts_nano"][0], GOLD) == {"right": 20, "too_low": 1, "too_high": 3, "p1_found": 4} assert score(RUNS["facts_mini"][0], GOLD) == {"right": 18, "too_low": 1, "too_high": 5, "p1_found": 4} def test_the_knee_surgery_was_p2_all_twelve_times(): assert [r[1] for k in RUNS for r in RUNS[k]] == ["P2"] * 12 def test_facts_first_changed_its_answer_less(): assert {k: changed_across_tries(v) for k, v in RUNS.items()} == { "name_nano": 6, "name_mini": 2, "facts_nano": 1, "facts_mini": 1} def test_business_days_skip_the_weekend(): friday = datetime(2026, 10, 23, 16, 0) assert add_business_days(friday, 1) == datetime(2026, 10, 26, 16, 0) assert add_business_days(datetime(2026, 10, 20, 10, 0), 4) == datetime(2026, 10, 26, 10, 0) def test_friday_afternoon_clocks(): c = clocks(datetime(2026, 10, 23, 16, 0)) assert c == {"gdpr": datetime(2026, 10, 26, 16, 0), "sec": datetime(2026, 10, 29, 16, 0)} def test_three_vendors_from_their_pages(): assert decide(NORTHWIND, RULES) == ("approve", [], []) assert decide(BRIGHTLINE, RULES)[:2] == ("reject", ["training", "breach_notice", "dpa"]) d, blockers, conditions = decide(KESTREL, RULES) assert (d, blockers, len(conditions)) == ("approve with conditions", [], 8) def test_one_misread_sentence_turns_approve_into_reject(): wrong = [q for q in KESTREL if KESTREL_MINI[q] != KESTREL[q]] assert wrong == ["deletion", "breach_notice"] assert decide(KESTREL_MINI, RULES)[:2] == ("reject", ["breach_notice"])