From 4da624bfcf3b68635a9563d6582415a211ec59bb Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 19 Jun 2026 14:19:00 +0200 Subject: [PATCH] Fix route optimizer and group assignment; add test suite Route building / optimization (tatami_masterplan.py): - fast_total_time was permutation-invariant: it applied get_courses to group *values* instead of slots and ignored the permutation, so every ordering scored identically and the annealing optimized nothing. It now maps each rotation slot to its assigned group via the permutation. - Replaced the broken next_permutation/simulated_annealing (enumerated n! orderings per iteration, fed unnormalized Boltzmann weights to np.random.choice -> ValueError, and returned the last random sample) with a standard neighbor-swap annealer that tracks and returns the best solution and handles <2 slots. - Convert the reduced Timedelta matrix to float seconds before annealing (np.exp can't operate on Timedelta). Group building (tatami_masterplan.py): - assign_courses set each group's hosts (sorted by course) before all courses were assigned, so hosts whose course was still None got mis-ordered. Assign all courses first, then wire up hosts. - get_masterplan no longer mutates the caller's participant list. classes.py: - Narrow casts on distance-matrix lookups to satisfy the mypy gate (pre-existing failures). Tests: - Add pytest suite (74 tests) covering the rotation topology, route cost and optimization, the domain model, the masterplan pipeline, and the Routes API wrapper (HTTP mocked). The cost cross-check caught the assign_courses ordering bug above. --- pyproject.toml | 5 + src/tatami/classes.py | 17 +- src/tatami/tatami_masterplan.py | 123 +++++++++------ tests/conftest.py | 67 ++++++++ tests/test_classes.py | 141 +++++++++++++++++ tests/test_masterplan.py | 141 +++++++++++++++++ tests/test_routing.py | 153 ++++++++++++++++++ tests/test_traveltimes.py | 121 ++++++++++++++ uv.lock | 271 +++++++++++++++++++------------- 9 files changed, 880 insertions(+), 159 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/test_classes.py create mode 100644 tests/test_masterplan.py create mode 100644 tests/test_routing.py create mode 100644 tests/test_traveltimes.py diff --git a/pyproject.toml b/pyproject.toml index 8a1c371..6a1d8ff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,9 +17,14 @@ dependencies = [ [dependency-groups] dev = [ "mypy>=1.15.0", + "pytest>=9.1.1", "ruff>=0.11.5", ] [build-system] requires = ["hatchling"] build-backend = "hatchling.build" + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-q" diff --git a/src/tatami/classes.py b/src/tatami/classes.py index 4efd33d..f4dd26a 100644 --- a/src/tatami/classes.py +++ b/src/tatami/classes.py @@ -1,5 +1,6 @@ import datetime as dt import pandas as pd +from typing import cast from uuid import uuid4 @@ -23,7 +24,10 @@ class Participant: return ( self.get_penalty() + pd.to_timedelta( - distance_matrix.loc[self.uuid, after_party_group.main_member.uuid] + cast( + pd.Timedelta, + distance_matrix.loc[self.uuid, after_party_group.main_member.uuid], + ) ).to_pytimedelta() ) @@ -49,7 +53,9 @@ class Group: self.uuid = "Group_" + str(uuid4()) self.members = members self.main_member = members[main_member] - self.course: str | None = None # For ordering the groups allowed values: "starter", "main", "dessert" + self.course: str | None = ( + None # For ordering the groups allowed values: "starter", "main", "dessert" + ) self.hosts: list[Group] | None = None def set_course(self, course: str): @@ -89,7 +95,12 @@ class Group: total_time = dt.timedelta() for group, next_group in zip(groups[:-1], groups[1:]): total_time += pd.to_timedelta( - distance_matrix.loc[group.main_member.uuid, next_group.main_member.uuid] + cast( + pd.Timedelta, + distance_matrix.loc[ + group.main_member.uuid, next_group.main_member.uuid + ], + ) ).to_pytimedelta() total_time += group.main_member.get_penalty() diff --git a/src/tatami/tatami_masterplan.py b/src/tatami/tatami_masterplan.py index c9254e3..34f303f 100644 --- a/src/tatami/tatami_masterplan.py +++ b/src/tatami/tatami_masterplan.py @@ -4,7 +4,6 @@ import pandas as pd import numpy as np import random from tqdm import tqdm -from itertools import permutations def get_after_party_group(address: str) -> Group: @@ -23,8 +22,9 @@ def get_masterplan( after_party_group: Group, ) -> tuple[list[dict], list[dict]]: groups_per_course = np.floor(len(participants) / 6).astype(int) - participants.sort( - key=lambda x: x.get_after_party_time(distance_matrix, after_party_group) + participants = sorted( + participants, + key=lambda x: x.get_after_party_time(distance_matrix, after_party_group), ) hosts = participants[: 3 * groups_per_course] semi_hosts = participants[3 * groups_per_course :] @@ -59,10 +59,17 @@ def get_masterplan( def assign_courses(groups: list[Group], courses: list[str]) -> None: """ Assign courses to groups. + + Courses are assigned to every group first, then hosts are wired up: a group's + hosts are sorted by course (``sort_hosts``), so all course assignments must be + in place before any ``set_hosts`` call, otherwise hosts whose course is still + unset get mis-ordered. """ - for i, (group, course) in enumerate(zip(groups, courses)): + for group, course in zip(groups, courses): group.set_course(course) - hosts = [groups[i] for i in get_courses(i, len(groups))] + + for i, group in enumerate(groups): + hosts = [groups[j] for j in get_courses(i, len(groups))] group.set_hosts(hosts) @@ -77,11 +84,18 @@ def run_simulated_annealing( """ Run simulated annealing to find the optimal order of groups. """ - group_indices = list(range(len(groups))) - distance_matrix = reduced_distance_matrix.to_numpy() + group_indices = [int(i) for i in range(len(groups))] - # Convert group indices to a list of integers - group_indices = [int(i) for i in group_indices] + # The reduced matrix holds pd.Timedelta values; convert to a float matrix of + # seconds so it can be used numerically in the acceptance criterion + # (np.exp cannot operate on Timedelta objects). + distance_matrix = np.array( + [ + [pd.Timedelta(x).total_seconds() for x in row] + for row in reduced_distance_matrix.to_numpy() + ], + dtype=float, + ) if multiprocessing > 1: raise NotImplementedError("Multiprocessing is not implemented yet.") @@ -98,25 +112,6 @@ def run_simulated_annealing( return best_ordererd_groups -def next_permutation( - group_indices: list[int], - distance_matrix: np.ndarray, - T: float, -) -> list[int]: - """ - Generate the next permutation of group indices that minimizes the total travel time using boltzmann annealing. - """ - all_permutations = list(permutations(group_indices)) - times = { - i: fast_total_time(distance_matrix, perm) - for i, perm in enumerate(all_permutations) - } - min_time = min(times.values()) - probabilities = {i: np.exp(-(time - min_time) / T) for i, time in times.items()} - key = np.random.choice(list(probabilities.keys()), p=list(probabilities.values())) - return all_permutations[key] - - def simulated_annealing( distance_matrix: np.ndarray, group_indices: list[int], @@ -125,23 +120,48 @@ def simulated_annealing( max_iterations: int, ) -> list[int]: """ - Simulated annealing algorithm to find the optimal order of groups. + Simulated annealing over assignments of groups to rotation slots. + + ``solution[slot]`` is the group index placed in that slot. Each iteration + proposes a random two-slot swap and accepts it with the Boltzmann + probability; the best solution seen is tracked and returned. """ - solution = group_indices.copy() - np.random.shuffle(solution) + current = group_indices.copy() + np.random.shuffle(current) + current_cost = fast_total_time(distance_matrix, current) + + best = current.copy() + best_cost = current_cost + + # Nothing to optimize with fewer than two slots. + if len(current) < 2: + return best + current_temperature = initial_temperature for iteration in tqdm(range(max_iterations)): try: - solution = next_permutation(solution, distance_matrix, current_temperature) - time = fast_total_time(distance_matrix, solution) + candidate = current.copy() + i, j = np.random.choice(len(candidate), size=2, replace=False) + candidate[i], candidate[j] = candidate[j], candidate[i] + candidate_cost = fast_total_time(distance_matrix, candidate) + + delta = candidate_cost - current_cost + if delta <= 0 or np.random.random() < np.exp(-delta / current_temperature): + current, current_cost = candidate, candidate_cost + if current_cost < best_cost: + best, best_cost = current.copy(), current_cost + if iteration % 100 == 0: - print(f"Iteration {iteration}: Time = {time}") + print( + f"Iteration {iteration}: current = {current_cost:.0f}s, " + f"best = {best_cost:.0f}s" + ) current_temperature *= cooling_rate except KeyboardInterrupt: - print("Simulation interrupted. Returning current solution.") + print("Simulation interrupted. Returning best solution so far.") break - return solution + return best def get_courses( @@ -155,25 +175,32 @@ def get_courses( return order -def fast_total_time(distance_matrix: np.ndarray, group_indices: list[int]): - """Calculates the total travel time for all groups. +def fast_total_time(distance_matrix: np.ndarray, solution: list[int]) -> float: + """Calculates the total travel time for a given slot-to-group assignment. + + The rotation topology is defined over *slots* ``0..n-1`` via ``get_courses``; + ``solution[slot]`` gives the group placed in that slot. For each slot the + occupying group travels starter-host -> main-host -> dessert-host -> after + party, and the durations of those legs are summed across all slots. Args: - distance_matrix (np.ndarray): 2D array representing the distance matrix, where - distance_matrix[i][j] is the travel time from group i to group j in seconds. Should be of shape (n+1, n+1). The last column/row should be the after party group. - group_indices (list[int]): List of group indices for which to calculate the total travel time. Should be of length n. + distance_matrix (np.ndarray): 2D array where ``distance_matrix[i][j]`` is + the travel time (seconds) from group ``i`` to group ``j``. Shape + ``(n+1, n+1)``; the last row/column is the after-party location. + solution (list[int]): Permutation mapping each slot to a group index. """ - n = len(group_indices) + n = len(solution) after_party_idx = n # Since distance_matrix is (n+1)x(n+1) - total_time = 0 + total_time = 0.0 total_meetings = n - for group_index in group_indices: - a, b, c = get_courses(group_index, total_meetings) - total_time += distance_matrix[a][b] - total_time += distance_matrix[b][c] - total_time += distance_matrix[c][after_party_idx] + for slot in range(n): + a, b, c = get_courses(slot, total_meetings) + ga, gb, gc = solution[a], solution[b], solution[c] + total_time += distance_matrix[ga][gb] + total_time += distance_matrix[gb][gc] + total_time += distance_matrix[gc][after_party_idx] return total_time diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..90cb90a --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,67 @@ +"""Shared pytest fixtures and test setup for the tatami package. + +``tatami.traveltimes`` raises at import time if ``GOOGLE_MAPS_API_KEY`` is unset, +so we set a dummy value here (conftest is imported before any test module, and +therefore before the package is imported) to make the modules importable without +a real API key. Tests never hit the live API; the HTTP layer is mocked. +""" + +import os + +os.environ.setdefault("GOOGLE_MAPS_API_KEY", "test-key") + +import numpy as np +import pandas as pd +import pytest + +from tatami.classes import Participant + + +@pytest.fixture(autouse=True) +def _seed_rng(): + """Make the randomized steps (shuffles, annealing) deterministic per test.""" + np.random.seed(1234) + + +def make_participants( + n: int, kitchen_sizes: list[float] | None = None +) -> list[Participant]: + """Create ``n`` participants with distinct addresses and uuids.""" + if kitchen_sizes is None: + kitchen_sizes = [10.0] * n + return [ + Participant( + name=f"P{i}", + address=f"address {i}", + phone=f"phone {i}", + kitchen_size=kitchen_sizes[i], + allergies="", + ) + for i in range(n) + ] + + +def make_timedelta_matrix( + participants: list[Participant], seconds: np.ndarray +) -> pd.DataFrame: + """Build a uuid-indexed square matrix of ``pd.Timedelta`` from a seconds array. + + Mirrors the shape produced by ``get_participant_distance_matrix``: object dtype + cells holding ``pd.Timedelta`` values, indexed and columned by participant uuid. + """ + uuids = [p.uuid for p in participants] + matrix = pd.DataFrame(index=pd.Index(uuids), columns=pd.Index(uuids), dtype=object) + for i, u in enumerate(uuids): + for j, v in enumerate(uuids): + matrix.at[u, v] = pd.to_timedelta(f"{int(seconds[i][j])}s") + return matrix + + +@pytest.fixture +def participants_factory(): + return make_participants + + +@pytest.fixture +def matrix_factory(): + return make_timedelta_matrix diff --git a/tests/test_classes.py b/tests/test_classes.py new file mode 100644 index 0000000..2048b73 --- /dev/null +++ b/tests/test_classes.py @@ -0,0 +1,141 @@ +"""Tests for the core domain model: Participant and Group.""" + +import datetime as dt + +import numpy as np +import pytest + +from tatami.classes import Group, Participant +from conftest import make_participants, make_timedelta_matrix + + +class TestParticipant: + def test_uuid_is_unique(self): + a = Participant("A", "addr", "", 5, "") + b = Participant("A", "addr", "", 5, "") + assert a.uuid != b.uuid + + @pytest.mark.parametrize( + "kitchen_size,minutes", + [(10, 0), (7, 9), (0, 30), (5, 15)], + ) + def test_penalty_scales_with_kitchen_size(self, kitchen_size, minutes): + p = Participant("A", "addr", "", kitchen_size, "") + assert p.get_penalty() == dt.timedelta(minutes=minutes) + + def test_after_party_time_combines_penalty_and_travel(self): + people = make_participants(2, kitchen_sizes=[7.0, 10.0]) + # 600s travel between the two participants. + matrix = make_timedelta_matrix(people, np.array([[0, 600], [600, 0]])) + after_party = Group(members=[people[1]]) + result = people[0].get_after_party_time(matrix, after_party) + assert result == dt.timedelta(minutes=9) + dt.timedelta(seconds=600) + + def test_dict_roundtrip_fields(self): + p = Participant("Alice", "addr", "555", 8, "peanuts") + d = p.dict() + assert d == { + "uuid": p.uuid, + "name": "Alice", + "address": "addr", + "phone": "555", + "kitchen_size": 8, + "allergies": "peanuts", + } + + +class TestGroup: + def test_default_main_member_is_first(self): + people = make_participants(3) + group = Group(members=people) + assert group.main_member is people[0] + + def test_explicit_main_member_index(self): + people = make_participants(3) + group = Group(members=people, main_member=2) + assert group.main_member is people[2] + + def test_add_member_appends_without_changing_main(self): + people = make_participants(2) + group = Group(members=[people[0]]) + group.add_member(people[1]) + assert people[1] in group.members + assert group.main_member is people[0] + + def test_add_member_can_promote_to_main(self): + people = make_participants(2) + group = Group(members=[people[0]]) + group.add_member(people[1], main_member=True) + assert group.main_member is people[1] + + def test_set_hosts_sorts_by_course(self): + people = make_participants(3) + groups = [Group(members=[p]) for p in people] + groups[0].set_course("dessert") + groups[1].set_course("starter") + groups[2].set_course("main") + host = Group(members=[make_participants(1)[0]]) + host.set_hosts([groups[0], groups[1], groups[2]]) + assert [g.course for g in host.hosts] == ["starter", "main", "dessert"] + + def test_get_total_time_sums_legs_and_penalties(self): + # Three host groups (penalties 0) + after party; verify the summed route. + people = make_participants(4, kitchen_sizes=[10, 10, 10, 10]) + seconds = np.array( + [ + [0, 100, 0, 0, 50], + [0, 0, 200, 0, 0], + [0, 0, 0, 0, 300], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + ] + ) + # build matrix including an after party participant + after_party_p = make_participants(1)[0] + all_people = people + [after_party_p] + full = make_timedelta_matrix(all_people, seconds) + + starter = Group(members=[people[0]]) + starter.set_course("starter") + main = Group(members=[people[1]]) + main.set_course("main") + dessert = Group(members=[people[2]]) + dessert.set_course("dessert") + after_party = Group(members=[after_party_p]) + + visitor = Group(members=[people[3]]) + visitor.set_hosts([starter, main, dessert]) + total = visitor.get_total_time(full, after_party) + # legs: starter->main (100) + main->dessert (200) + dessert->afterparty (300) + assert total == dt.timedelta(seconds=600) + + def test_get_total_time_raises_without_hosts(self): + group = Group(members=make_participants(1)) + after_party = Group(members=make_participants(1)) + with pytest.raises(ValueError): + group.get_total_time(None, after_party) + + def test_get_guests_collects_visiting_members(self): + people = make_participants(4) + host = Group(members=[people[0]]) + guest_a = Group(members=[people[1]]) + guest_b = Group(members=[people[2], people[3]]) + # get_guests returns early when the host has no hosts of its own, so give it + # a (self-)host as it would have in a real plan. + host.set_hosts([host]) + guest_a.set_hosts([host]) + guest_b.set_hosts([host]) + guests = host.get_guests([guest_a, guest_b]) + guest_uuids = {p.uuid for p in guests} + assert guest_uuids == {people[1].uuid, people[2].uuid, people[3].uuid} + + def test_dict_serializes_uuids(self): + people = make_participants(2) + group = Group(members=people) + host = Group(members=make_participants(1)) + host.set_course("starter") + group.set_hosts([host]) + d = group.dict() + assert d["main_member"] == people[0].uuid + assert d["members"] == [p.uuid for p in people] + assert d["hosts"] == [host.uuid] diff --git a/tests/test_masterplan.py b/tests/test_masterplan.py new file mode 100644 index 0000000..d0d852a --- /dev/null +++ b/tests/test_masterplan.py @@ -0,0 +1,141 @@ +"""Integration tests for the masterplan pipeline (group building + assignment).""" + +import numpy as np +import pytest + +from tatami.classes import Group +from tatami.traveltimes import reduce_distance_matrix +from tatami.tatami_masterplan import ( + assign_courses, + fast_total_time, + get_after_party_group, + get_courses, + get_masterplan, + load_csv_to_participants, +) +from conftest import make_participants, make_timedelta_matrix + + +def full_matrix(participants, after_party_group, seed=0): + """Synthetic uuid-indexed Timedelta matrix over participants + after party.""" + all_people = participants + [after_party_group.main_member] + rng = np.random.default_rng(seed) + n = len(all_people) + seconds = rng.integers(60, 1200, size=(n, n)).astype(float) + np.fill_diagonal(seconds, 0) + return make_timedelta_matrix(all_people, seconds) + + +class TestGetAfterPartyGroup: + def test_single_member_with_no_penalty(self): + group = get_after_party_group("party street 1") + assert len(group.members) == 1 + assert group.main_member.address == "party street 1" + assert group.main_member.get_penalty().total_seconds() == 0 + + +class TestLoadCsv: + def test_parses_tab_separated_file(self, tmp_path): + csv = tmp_path / "config.csv" + csv.write_text( + "name\taddress\tphone\tkitchen_size\tallergies\n" + "Alice\tStreet 1\t111\t8\tnone\n" + "Bob\tStreet 2\t222\t5\tpeanuts\n" + ) + participants = load_csv_to_participants(str(csv)) + assert [p.name for p in participants] == ["Alice", "Bob"] + assert participants[0].address == "Street 1" + assert participants[1].kitchen_size == 5 + + +class TestAssignCourses: + def test_assigns_cycling_courses_and_hosts(self): + groups = [Group(members=[p]) for p in make_participants(6)] + courses = ["starter", "main", "dessert"] * 2 + assign_courses(groups, courses) + assert [g.course for g in groups] == courses + for i, group in enumerate(groups): + expected_host_uuids = {groups[j].uuid for j in get_courses(i, len(groups))} + assert {h.uuid for h in group.hosts} == expected_host_uuids + assert group.uuid in {h.uuid for h in group.hosts} + + +class TestGetMasterplan: + @pytest.mark.parametrize("n", [6, 12, 18]) + def test_group_count_and_coverage(self, n): + participants = make_participants(n, kitchen_sizes=list(np.linspace(0, 10, n))) + after_party = get_after_party_group("party street") + matrix = full_matrix(participants, after_party) + + group_dicts, participant_dicts = get_masterplan( + participants, matrix, after_party + ) + + expected_groups = 3 * (n // 6) + assert len(group_dicts) == expected_groups + assert len(participant_dicts) == n + + # Every participant ends up in exactly one group. + assigned = [uuid for g in group_dicts for uuid in g["members"]] + assert len(assigned) == n + assert set(assigned) == {p.uuid for p in participants} + + def test_topology_of_output(self): + participants = make_participants(18, kitchen_sizes=list(np.linspace(0, 10, 18))) + after_party = get_after_party_group("party street") + matrix = full_matrix(participants, after_party) + + group_dicts, _ = get_masterplan(participants, matrix, after_party) + by_uuid = {g["uuid"]: g for g in group_dicts} + + # Courses are balanced across the three slots. + course_counts = {} + for g in group_dicts: + course_counts[g["course"]] = course_counts.get(g["course"], 0) + 1 + assert course_counts == {"starter": 3, "main": 3, "dessert": 3} + + for g in group_dicts: + host_courses = sorted(by_uuid[h]["course"] for h in g["hosts"]) + assert host_courses == ["dessert", "main", "starter"] + assert g["uuid"] in g["hosts"] # a group hosts its own course + + def test_does_not_mutate_input_participant_order(self): + participants = make_participants(12) + original_order = list(participants) + after_party = get_after_party_group("party street") + matrix = full_matrix(participants, after_party) + get_masterplan(participants, matrix, after_party) + assert participants == original_order + + +class TestCostConsistency: + def test_fast_total_time_matches_group_get_total_time(self): + # The optimizer cost (fast_total_time on the penalty-baked reduced matrix) + # must equal the sum of per-group route times computed independently by + # Group.get_total_time on the raw matrix. + n = 6 + participants = make_participants(n, kitchen_sizes=[10, 8, 6, 4, 2, 0]) + after_party = get_after_party_group("party street") + groups = [Group(members=[p]) for p in participants] + courses = ["starter", "main", "dessert"] * (n // 3) + assign_courses(groups, courses) + + full = full_matrix(participants, after_party, seed=5) + + reduced = reduce_distance_matrix(full, [*groups, after_party]) + reduced_seconds = np.array( + [[pd_to_seconds(x) for x in row] for row in reduced.to_numpy()], + dtype=float, + ) + optimizer_cost = fast_total_time(reduced_seconds, list(range(n))) + + independent_cost = sum( + g.get_total_time(full, after_party).total_seconds() for g in groups + ) + assert optimizer_cost == pytest.approx(independent_cost) + + +def pd_to_seconds(value): + import pandas as pd + + return pd.Timedelta(value).total_seconds() diff --git a/tests/test_routing.py b/tests/test_routing.py new file mode 100644 index 0000000..0413c6c --- /dev/null +++ b/tests/test_routing.py @@ -0,0 +1,153 @@ +"""Tests for the dinner-rotation topology and the route-cost / optimization logic. + +These cover the two areas most prone to subtle bugs: the ``get_courses`` rotation +(who hosts whom) and ``fast_total_time`` / ``simulated_annealing`` (the route cost +and its optimization). +""" + +import itertools +from collections import Counter + +import numpy as np +import pytest + +from tatami.tatami_masterplan import ( + fast_total_time, + get_courses, + simulated_annealing, +) + +# Group counts must be multiples of 3 (one slot per course); test a range of sizes. +GROUP_COUNTS = [3, 6, 9, 12, 15] + + +def asymmetric_matrix(n: int, seed: int = 0) -> np.ndarray: + """Random asymmetric (n+1)x(n+1) cost matrix, last index = after party.""" + rng = np.random.default_rng(seed) + matrix = rng.integers(60, 1200, size=(n + 1, n + 1)).astype(float) + np.fill_diagonal(matrix, 0) + return matrix + + +class TestGetCourses: + @pytest.mark.parametrize("n", GROUP_COUNTS) + def test_group_is_its_own_first_host(self, n): + # A group always hosts the course it cooks, so it appears in its own host list. + for i in range(n): + assert i in get_courses(i, n) + + @pytest.mark.parametrize("n", GROUP_COUNTS) + def test_each_group_visits_three_distinct_courses(self, n): + for i in range(n): + hosts = get_courses(i, n) + assert len(hosts) == 3 + assert len(set(hosts)) == 3, "a group must visit three distinct hosts" + # The three hosts must cook three different courses (course == index % 3). + assert {h % 3 for h in hosts} == {0, 1, 2} + + @pytest.mark.parametrize("n", GROUP_COUNTS) + def test_hosts_returned_in_starter_main_dessert_order(self, n): + # get_courses sorts by index % 3 -> starter(0), main(1), dessert(2). + for i in range(n): + a, b, c = get_courses(i, n) + assert (a % 3, b % 3, c % 3) == (0, 1, 2) + + @pytest.mark.parametrize("n", GROUP_COUNTS) + def test_every_host_receives_exactly_three_groups(self, n): + # The defining invariant of a running dinner: every hosting location serves + # exactly three groups (itself + two guests) for its course. + received = Counter() + for i in range(n): + for host in get_courses(i, n): + received[host] += 1 + assert set(received.values()) == {3} + assert len(received) == n + + @pytest.mark.parametrize("n", GROUP_COUNTS) + def test_guests_at_each_host_cook_distinct_courses(self, n): + # Invert the relation: the (exactly three) groups that show up at host h for + # its course must each be responsible for a different course in the rotation. + hosts_of = {i: set(get_courses(i, n)) for i in range(n)} + for host in range(n): + guests = [g for g in range(n) if host in hosts_of[g]] + assert len(guests) == 3 + assert {g % 3 for g in guests} == {0, 1, 2} + + +class TestFastTotalTime: + def test_matches_hand_computed_value_n3(self): + # For n=3 every slot resolves to hosts (0, 1, 2), so the cost is + # 3 * (D[s0][s1] + D[s1][s2] + D[s2][afterparty]). + matrix = np.array( + [ + [0, 10, 20, 30], + [40, 0, 50, 60], + [70, 80, 0, 90], + [1, 2, 3, 0], # after-party row (unused as origin) + ], + dtype=float, + ) + solution = [0, 1, 2] + expected = 3 * (matrix[0][1] + matrix[1][2] + matrix[2][3]) + assert fast_total_time(matrix, solution) == pytest.approx(expected) + + def test_cost_depends_on_permutation(self): + # The whole point of the optimizer: different slot->group assignments must + # generally produce different costs on an asymmetric matrix. + matrix = asymmetric_matrix(6, seed=2) + costs = { + fast_total_time(matrix, list(p)) + for p in itertools.islice(itertools.permutations(range(6)), 50) + } + assert len(costs) > 1 + + def test_returns_float(self): + matrix = asymmetric_matrix(6, seed=2) + assert isinstance(fast_total_time(matrix, list(range(6))), float) + + +class TestSimulatedAnnealing: + @pytest.mark.parametrize("seed", [0, 1, 2, 3, 4]) + def test_never_returns_worse_than_optimum(self, seed): + # Sanity floor: the cost can never be below the true (brute-force) optimum. + n = 6 + matrix = asymmetric_matrix(n, seed=seed) + optimum = min( + fast_total_time(matrix, list(p)) for p in itertools.permutations(range(n)) + ) + result = simulated_annealing(matrix, list(range(n)), 1000, 0.99, 5000) + assert fast_total_time(matrix, result) >= optimum + + @pytest.mark.parametrize("seed", [0, 1, 2, 3, 4]) + def test_beats_random_baseline(self, seed): + # SA should reliably do better than the average random ordering. + n = 9 + matrix = asymmetric_matrix(n, seed=seed) + random_costs = [] + for _ in range(500): + perm = list(range(n)) + np.random.shuffle(perm) + random_costs.append(fast_total_time(matrix, perm)) + result = simulated_annealing(matrix, list(range(n)), 1000, 0.99, 5000) + assert fast_total_time(matrix, result) < np.mean(random_costs) + + def test_returns_valid_permutation(self): + n = 9 + matrix = asymmetric_matrix(n, seed=3) + result = simulated_annealing(matrix, list(range(n)), 1000, 0.99, 1000) + assert sorted(result) == list(range(n)) + + def test_handles_single_slot(self): + matrix = asymmetric_matrix(1, seed=0) + assert simulated_annealing(matrix, [0], 1000, 0.99, 100) == [0] + + def test_handles_empty(self): + matrix = np.zeros((1, 1)) + assert simulated_annealing(matrix, [], 1000, 0.99, 100) == [] + + def test_does_not_mutate_input_indices(self): + n = 6 + matrix = asymmetric_matrix(n, seed=1) + indices = list(range(n)) + simulated_annealing(matrix, indices, 1000, 0.99, 100) + assert indices == list(range(n)) diff --git a/tests/test_traveltimes.py b/tests/test_traveltimes.py new file mode 100644 index 0000000..0cafd59 --- /dev/null +++ b/tests/test_traveltimes.py @@ -0,0 +1,121 @@ +"""Tests for the Google Routes API wrapper (HTTP layer mocked).""" + +import datetime as dt + +import numpy as np +import pandas as pd +import pytest + +import tatami.traveltimes as tt +from tatami.classes import Group +from tatami.traveltimes import ( + get_distance_matrix, + get_participant_distance_matrix, + reduce_distance_matrix, +) +from conftest import make_participants, make_timedelta_matrix + + +class FakeResponse: + def __init__(self, status_code, json_data=None, text=""): + self.status_code = status_code + self._json = json_data + self.text = text + + def json(self): + return self._json + + +def route_matrix_payload(durations_seconds): + """Build a computeRouteMatrix-style flat list of elements from an n x n array.""" + payload = [] + n = len(durations_seconds) + for i in range(n): + for j in range(n): + payload.append( + { + "originIndex": i, + "destinationIndex": j, + "duration": f"{int(durations_seconds[i][j])}s", + "distanceMeters": int(durations_seconds[i][j]) * 4, + } + ) + return payload + + +@pytest.fixture +def mock_post(monkeypatch): + """Patch requests.post; returns a setter for the desired response.""" + state = {} + + def fake_post(url, json=None, headers=None): + return state["response"] + + monkeypatch.setattr(tt.requests, "post", fake_post) + + def set_response(response): + state["response"] = response + + return set_response + + +class TestGetDistanceMatrix: + def test_builds_duration_matrix(self, mock_post): + durations = np.array([[0, 120], [300, 0]]) + mock_post(FakeResponse(200, route_matrix_payload(durations))) + matrix = get_distance_matrix(["a", "b"]) + assert matrix.shape == (2, 2) + assert matrix.loc[0, 1] == pd.to_timedelta("120s") + assert matrix.loc[1, 0] == pd.to_timedelta("300s") + + def test_distance_meters_value(self, mock_post): + durations = np.array([[0, 120], [300, 0]]) + mock_post(FakeResponse(200, route_matrix_payload(durations))) + matrix = get_distance_matrix(["a", "b"], value="distanceMeters") + assert matrix.loc[0, 1] == 480.0 + + def test_empty_addresses_raises(self): + with pytest.raises(ValueError): + get_distance_matrix([]) + + def test_non_200_raises(self, mock_post): + mock_post(FakeResponse(500, text="boom")) + with pytest.raises(Exception, match="boom"): + get_distance_matrix(["a", "b"]) + + def test_invalid_value_raises(self, mock_post): + durations = np.array([[0, 120], [300, 0]]) + mock_post(FakeResponse(200, route_matrix_payload(durations))) + with pytest.raises(ValueError): + get_distance_matrix(["a", "b"], value="bogus") + + +class TestGetParticipantDistanceMatrix: + def test_labels_axes_with_uuids(self, mock_post): + participants = make_participants(2) + durations = np.array([[0, 120], [300, 0]]) + mock_post(FakeResponse(200, route_matrix_payload(durations))) + matrix = get_participant_distance_matrix(participants) + uuids = [p.uuid for p in participants] + assert list(matrix.index) == uuids + assert list(matrix.columns) == uuids + + +class TestReduceDistanceMatrix: + def test_selects_main_members_and_adds_penalty(self): + # group 0 main member has kitchen_size 7 -> 9 minute (540s) penalty. + people = make_participants(2, kitchen_sizes=[7.0, 10.0]) + seconds = np.array([[0, 100], [200, 0]]) + matrix = make_timedelta_matrix(people, seconds) + groups = [Group(members=[people[0]]), Group(members=[people[1]])] + + reduced = reduce_distance_matrix(matrix, groups) + + u0, u1 = people[0].uuid, 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