lars
4da624bfcf
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.
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
"""Shared pytest fixtures and test setup for the tatami package.
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``tatami.traveltimes`` raises at import time if ``GOOGLE_MAPS_API_KEY`` is unset,
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so we set a dummy value here (conftest is imported before any test module, and
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therefore before the package is imported) to make the modules importable without
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a real API key. Tests never hit the live API; the HTTP layer is mocked.
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"""
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import os
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os.environ.setdefault("GOOGLE_MAPS_API_KEY", "test-key")
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import numpy as np
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import pandas as pd
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import pytest
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from tatami.classes import Participant
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@pytest.fixture(autouse=True)
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def _seed_rng():
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"""Make the randomized steps (shuffles, annealing) deterministic per test."""
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np.random.seed(1234)
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def make_participants(
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n: int, kitchen_sizes: list[float] | None = None
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) -> list[Participant]:
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"""Create ``n`` participants with distinct addresses and uuids."""
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if kitchen_sizes is None:
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kitchen_sizes = [10.0] * n
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return [
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Participant(
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name=f"P{i}",
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address=f"address {i}",
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phone=f"phone {i}",
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kitchen_size=kitchen_sizes[i],
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allergies="",
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)
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for i in range(n)
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]
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def make_timedelta_matrix(
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participants: list[Participant], seconds: np.ndarray
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) -> pd.DataFrame:
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"""Build a uuid-indexed square matrix of ``pd.Timedelta`` from a seconds array.
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Mirrors the shape produced by ``get_participant_distance_matrix``: object dtype
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cells holding ``pd.Timedelta`` values, indexed and columned by participant uuid.
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"""
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uuids = [p.uuid for p in participants]
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matrix = pd.DataFrame(index=pd.Index(uuids), columns=pd.Index(uuids), dtype=object)
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for i, u in enumerate(uuids):
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for j, v in enumerate(uuids):
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matrix.at[u, v] = pd.to_timedelta(f"{int(seconds[i][j])}s")
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return matrix
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@pytest.fixture
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def participants_factory():
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return make_participants
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@pytest.fixture
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def matrix_factory():
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return make_timedelta_matrix
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