Bruno Postle
cf634ae949
Adds src/homemaker_layout/cpsat.py (OR-Tools CP-SAT) as an exact alternative to operators._assign_adjacency_aware's greedy/beam room-code placement, wired in as assign_solver="greedy"|"cpsat" (EXPERIMENTAL, default "greedy", byte-identical to before) through constructive_topology/lift_base_to_storeys/ driver.search, plus a new operators.mutate_reassign in-search repair operator (driver.search's enable_reassign=False default, mirrors enable_ruin_recreate). Both found and fixed a resize-fragility bug (a second CP-SAT pass against settled geometry, operators._cpsat_relabel_settled) and a CP-SAT symmetry-blowup stall (explicit interchangeable-code grouping). Seeder-level A/B on harbor-house is a solid, low-noise positive (~13% fewer real fitness-scored secondary-adjacency fails, 10 seeds). Full driver.search A/B is only pilot-scale (budget=3000 vs the bead's own 20k target) and inconclusive -- both flags stay default-off pending a larger-N confirmation. Full writeup: DESIGN.md §37.7. Bead left in_progress (own acceptance criteria not fully met); homemaker-py-5bv tracks the deferred post-collapse repair item. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
103 lines
4.7 KiB
Python
103 lines
4.7 KiB
Python
"""Tests for the exact CP-SAT room-code labelling solver (homemaker-py-2g7.5).
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``cpsat.solve_room_labels`` is dom/geometry-independent (same decoupled-
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testability convention as ``operators._beam_place_rooms``, exercised in
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``test_operators.py::test_beam_place_rooms_is_deterministic_given_inputs``),
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so these tests use plain hashable keys except where a direct comparison
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against the existing beam/greedy heuristic requires real ``dom.Node``
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objects (``_beam_place_rooms`` reads a neighbour's ``.type`` attribute for
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already-fixed context).
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"""
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from homemaker_layout import cpsat, dom, operators
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class _Req:
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def __init__(self, adjacency):
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self.adjacency = adjacency
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def test_empty_inputs_return_empty_dict():
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assert cpsat.solve_room_labels([], [], {}, {}, {}) == {}
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assert cpsat.solve_room_labels(["s1"], [], {}, {}, {}) == {}
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assert cpsat.solve_room_labels([], ["a"], {}, {}, {}) == {}
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def test_determinism():
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slots = ["s1", "s2", "s3"]
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codes = ["a", "b", "c"]
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reqs = {"a": _Req(["b"]), "b": _Req(["a"]), "c": _Req([])}
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neighbors = {"s1": {"s2"}, "s2": {"s1", "s3"}, "s3": {"s2"}}
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r1 = cpsat.solve_room_labels(slots, codes, reqs, neighbors, {})
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r2 = cpsat.solve_room_labels(slots, codes, reqs, neighbors, {})
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assert r1 == r2
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def test_fixed_context_credits_adjacency_without_a_decision_neighbour():
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# a single slot with no room-slot neighbours at all, but a fixed
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# (already-typed) circulation neighbour "c" — the requirement must be
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# creditable purely from context_types, no decision variable involved.
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reqs = {"k1": _Req(["c"])}
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result = cpsat.solve_room_labels(
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["s1"], ["k1"], reqs, {"s1": set()}, {"s1": {"c"}})
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assert result == {"s1": "k1"}
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def test_drops_least_constrained_code_when_over_capacity():
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# more codes than slots: the code with a real adjacency requirement is
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# kept over the unconstrained one, same priority the greedy path's
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# hardest-first ordering uses (_n_secondary).
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reqs = {"a": _Req(["b"]), "b": _Req([])}
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result = cpsat.solve_room_labels(["s1"], ["b", "a"], reqs, {"s1": set()}, {})
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assert result == {"s1": "a"}
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def test_finds_globally_optimal_labelling_beam_search_misses():
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"""Hand-built counter-example (same "adversarial hand-built graph"
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convention as test_collapse_global.py's
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test_two_opt_polish_escapes_jacobi_plateau): a hub H (already typed
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"r") connects to four leaves L1-L4; L1-L2 also has its own direct
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edge. "s" and "t" each need only a "r" neighbour — satisfiable from
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ANY leaf, since every leaf touches the hub. "p" and "q" need EACH
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OTHER as a neighbour — only satisfiable via the one non-hub edge,
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L1-L2.
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All four codes tie at exactly one secondary-adjacency requirement, so
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the beam/greedy heuristic (``operators._beam_place_rooms``,
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beam_width=1 reproduces the plain greedy pass) processes them in
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whatever order the caller's shuffle produced. Given the order
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s, t, p, q, the degree/id tie-break greedily claims the special L1-L2
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edge for s and t (who don't need it — they're satisfiable everywhere),
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stranding p and q on L3/L4 with no edge between them: 2 of their 4
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combined requirements met. CP-SAT reasons globally and finds the
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assignment that satisfies all 4/4, regardless of processing order.
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"""
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H = dom.Node(type="r")
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L1, L2, L3, L4 = (dom.Node(type=None) for _ in range(4))
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slots = [L1, L2, L3, L4]
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nbrs = {H: {L1, L2, L3, L4}, L1: {H, L2}, L2: {H, L1}, L3: {H}, L4: {H}}
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deg = {n: len(ns) for n, ns in nbrs.items()}
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idx = {L1: 0, L2: 1, L3: 2, L4: 3}
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dominated = set(slots)
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reqs = {"r": _Req(["s", "t"]), "s": _Req(["r"]), "t": _Req(["r"]),
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"p": _Req(["q"]), "q": _Req(["p"])}
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codes = ["s", "t", "p", "q"]
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placed = operators._beam_place_rooms(
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codes, slots, dominated, deg, idx, lambda s: nbrs[s], reqs,
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beam_width=1)
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for leaf, code in placed.items():
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leaf.type = code
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p_leaf = next(leaf for leaf, code in placed.items() if code == "p")
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q_leaf = next(leaf for leaf, code in placed.items() if code == "q")
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assert q_leaf not in nbrs[p_leaf], (
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"expected the greedy heuristic to strand p/q apart in this setup")
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neighbors_among_slots = {L1: {L2}, L2: {L1}, L3: set(), L4: set()}
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context_types = {s: {"r"} for s in slots} # every leaf touches the hub
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result = cpsat.solve_room_labels(
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slots, codes, reqs, neighbors_among_slots, context_types)
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p_slot = next(s for s, c in result.items() if c == "p")
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q_slot = next(s for s, c in result.items() if c == "q")
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assert q_slot in neighbors_among_slots[p_slot], (
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"CP-SAT should place p/q on the one edge that satisfies both")
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