Bruno Postle
f43de001fb
§13.8 verdict was positive and monotone-harmless, so default the share-aware edge-too-long cap to leaf_sharing when share_edge_cap is unset — mirrors the pll bal+share and §13.6 interior_outside default flips. Explicit share_edge_cap=False still reproduces the pre-flip control arm. - fitness.Fitness.__init__: cap defaults to self._leaf_sharing when the conf key is unset (None); explicit True/False honoured. - run_staged_search.py: pin conf["share_edge_cap"] = share_edge in both A/B arms so SHAREEDGE=0 stays a clean control post-flip. - tests: control arm now pins share_edge_cap=False; new test_edge_cap_defaults_on_under_leaf_sharing guards the flip. - DESIGN.md §13.9: rebaseline §13.x floor (maple 80.3→74.0, harbor 34.7→31.0). Non-sharing runs untouched: programme-house control re-score reproduces bit-for-bit. 222 tests pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
333 lines
11 KiB
Python
333 lines
11 KiB
Python
"""Unit tests for fitness.py quality terms and helpers (oracle-free)."""
|
||
|
||
import pytest
|
||
|
||
from homemaker_layout import dom, geometry
|
||
from homemaker_layout.dom import Node
|
||
from homemaker_layout.fitness import (
|
||
CONF_DEFAULTS,
|
||
COST_DEFAULTS,
|
||
FAIL_THRESHOLD,
|
||
Fitness,
|
||
_leaf_grade,
|
||
gaussian,
|
||
)
|
||
|
||
|
||
def _leaf(type_: str, size: float = 4.0) -> Node:
|
||
"""Undivided level-root leaf with a square plot of side `size`."""
|
||
geometry.clear_cache()
|
||
return Node(
|
||
node=[[0.0, 0.0], [size, 0.0], [size, size], [0.0, size]],
|
||
type=type_,
|
||
)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# gaussian
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_gaussian_peak_returns_a():
|
||
assert gaussian(5.0, 1.0, 5.0, 1.0) == pytest.approx(1.0)
|
||
|
||
|
||
def test_gaussian_peak_scales_by_a():
|
||
assert gaussian(3.0, 2.5, 3.0, 1.0) == pytest.approx(2.5)
|
||
|
||
|
||
def test_gaussian_one_sigma_uses_truncated_e():
|
||
# Urb uses e=2.718281828, not math.e; at one sigma the factor is e^-0.5
|
||
e = 2.718281828
|
||
expected = e ** -0.5
|
||
assert gaussian(6.0, 1.0, 5.0, 1.0) == pytest.approx(expected, rel=1e-9)
|
||
|
||
|
||
def test_gaussian_symmetry():
|
||
assert gaussian(4.0, 1.0, 5.0, 1.0) == pytest.approx(gaussian(6.0, 1.0, 5.0, 1.0))
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# Fitness.conf / cost
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_conf_falls_back_to_defaults():
|
||
assert Fitness().conf("value_inside") == CONF_DEFAULTS["value_inside"]
|
||
|
||
|
||
def test_conf_override_wins():
|
||
assert Fitness(conf={"value_inside": 999.0}).conf("value_inside") == 999.0
|
||
|
||
|
||
def test_conf_unknown_key_returns_none():
|
||
assert Fitness().conf("no_such_key") is None
|
||
|
||
|
||
def test_cost_falls_back_to_defaults():
|
||
assert Fitness().cost("inside") == COST_DEFAULTS["inside"]
|
||
|
||
|
||
def test_cost_override_wins():
|
||
assert Fitness(cost={"inside": 42.0}).cost("inside") == 42.0
|
||
|
||
|
||
def test_cost_unknown_key_returns_zero():
|
||
assert Fitness().cost("no_such_key") == 0.0
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# get_space_params lookup chain
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_get_space_params_circulation_size():
|
||
assert Fitness().get_space_params("C", "size") == CONF_DEFAULTS["size_circulation"]
|
||
|
||
|
||
def test_get_space_params_outside_width():
|
||
assert Fitness().get_space_params("O", "width") == CONF_DEFAULTS["width_outside"]
|
||
|
||
|
||
def test_get_space_params_sahn_proportion():
|
||
assert Fitness().get_space_params("S", "proportion") == CONF_DEFAULTS["proportion_outside"]
|
||
|
||
|
||
def test_get_space_params_inside_falls_back_to_inside_defaults():
|
||
assert Fitness().get_space_params("k1", "proportion") == CONF_DEFAULTS["proportion_inside"]
|
||
assert Fitness().get_space_params("k1", "size") == CONF_DEFAULTS["size_inside"]
|
||
|
||
|
||
def test_get_space_params_named_space_overrides_default():
|
||
f = Fitness(conf={"spaces": {"k1": {"size": [20.0, 4.0]}}})
|
||
assert f.get_space_params("k1", "size") == [20.0, 4.0]
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# quality_proportion
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_quality_proportion_square_inside_returns_one():
|
||
# aspect=1.0 < proportion_inside[0]=1.5 → 1.0
|
||
assert Fitness().quality_proportion(_leaf("k1")) == pytest.approx(1.0)
|
||
|
||
|
||
def test_quality_proportion_square_outside_returns_one():
|
||
# aspect=1.0 < proportion_outside[0]=1.5 → 1.0
|
||
assert Fitness().quality_proportion(_leaf("O")) == pytest.approx(1.0)
|
||
|
||
|
||
def test_quality_proportion_square_circulation_returns_one():
|
||
assert Fitness().quality_proportion(_leaf("C")) == pytest.approx(1.0)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# quality_size
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_quality_size_outside_always_one():
|
||
assert Fitness().quality_size(_leaf("O")) == 1.0
|
||
|
||
|
||
def test_quality_size_sahn_always_one():
|
||
assert Fitness().quality_size(_leaf("S")) == 1.0
|
||
|
||
|
||
def test_quality_size_inside_at_peak():
|
||
# size_inside=[16.0,3.5]; leaf is 4×4=16 m² → gaussian at peak → 1.0
|
||
leaf = _leaf("k1", size=4.0)
|
||
assert geometry.area(leaf) == pytest.approx(16.0)
|
||
assert Fitness().quality_size(leaf) == pytest.approx(1.0)
|
||
|
||
|
||
def test_quality_size_circulation_at_peak():
|
||
# size_circulation=[0.0,14.0]; peak at 0, gaussian(area,1,0,14) → always <1 for area>0
|
||
# Just verify it returns a value in [0,1]
|
||
f = Fitness().quality_size(_leaf("C", size=4.0))
|
||
assert 0.0 < f <= 1.0
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# quality_width
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_quality_width_wide_inside_returns_one():
|
||
# width_inside=[4.0,1.0]; 10m side > 4.0 → 1.0
|
||
assert Fitness().quality_width(_leaf("k1", size=10.0)) == pytest.approx(1.0)
|
||
|
||
|
||
def test_quality_width_wide_circulation_returns_one():
|
||
# width_circulation=[2.4,0.2]; 10m > 2.4 → 1.0
|
||
assert Fitness().quality_width(_leaf("C", size=10.0)) == pytest.approx(1.0)
|
||
|
||
|
||
def test_quality_width_wide_outside_ground_uses_gaussian():
|
||
# outside at level 0 falls through to gaussian; 10m > width_outside[0]=3.0 → 1.0
|
||
assert Fitness().quality_width(_leaf("O", size=10.0)) == pytest.approx(1.0)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# quality_perpendicular
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_quality_perpendicular_rectangle_near_one():
|
||
# All four corners of the square are pi/2; perpendicular formula gives ≈1
|
||
leaf = _leaf("k1", size=4.0)
|
||
result = Fitness().quality_perpendicular(leaf)
|
||
assert result == pytest.approx(1.0, abs=1e-6)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# value_rate
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_value_rate_outside_ground():
|
||
leaf = _leaf("O")
|
||
assert dom.level_of(leaf) == 0
|
||
assert Fitness().value_rate(leaf) == pytest.approx(CONF_DEFAULTS["value_outside"])
|
||
|
||
|
||
def test_value_rate_circulation():
|
||
assert Fitness().value_rate(_leaf("C")) == pytest.approx(CONF_DEFAULTS["value_circulation"])
|
||
|
||
|
||
def test_value_rate_inside():
|
||
assert Fitness().value_rate(_leaf("k1")) == pytest.approx(CONF_DEFAULTS["value_inside"])
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# leaf_cost
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_leaf_cost_outside_bare():
|
||
# not covered, not supported → outside rate × area
|
||
leaf = _leaf("O", size=4.0) # area = 16.0
|
||
assert Fitness().leaf_cost(leaf) == pytest.approx(COST_DEFAULTS["outside"] * 16.0)
|
||
|
||
|
||
def test_leaf_cost_inside():
|
||
leaf = _leaf("k1", size=4.0)
|
||
assert Fitness().leaf_cost(leaf) == pytest.approx(COST_DEFAULTS["inside"] * 16.0)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# Share-aware edge-too-long cap (hph §13.7)
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def _shared_leaf(type_: str = "k1", k: int = 3) -> Node:
|
||
leaf = _leaf(type_)
|
||
leaf.share = k
|
||
leaf.share_type = type_
|
||
return leaf
|
||
|
||
|
||
def test_edge_cap_flat_by_default():
|
||
# no leaf_sharing → flat 8 m regardless of any share stamp
|
||
fit = Fitness()
|
||
assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(8.0)
|
||
|
||
|
||
def test_edge_cap_flat_when_lever_off_even_with_sharing():
|
||
# leaf_sharing on but the hph lever explicitly off → still flat (control arm).
|
||
# Post-§13.8 the lever defaults ON under sharing, so the control must pin it.
|
||
fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": False})
|
||
assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(8.0)
|
||
|
||
|
||
def test_edge_cap_scales_by_share_when_lever_on():
|
||
fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
|
||
assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(24.0)
|
||
|
||
|
||
def test_edge_cap_defaults_on_under_leaf_sharing():
|
||
# §13.8 default flip: leaf_sharing on, lever unset → cap scales by share
|
||
fit = Fitness(conf={"leaf_sharing": True})
|
||
assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(24.0)
|
||
|
||
|
||
def test_edge_cap_unshared_leaf_keeps_flat_cap():
|
||
# a non-shared leaf (the narrow-sliver pathology) is never relaxed
|
||
fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
|
||
assert fit._edge_cap(_leaf("k1")) == pytest.approx(8.0)
|
||
|
||
|
||
def test_edge_cap_stale_share_type_ignored():
|
||
# retyped leaf whose stamp no longer matches type → share invalid → flat
|
||
fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
|
||
leaf = _shared_leaf("k1", k=3)
|
||
leaf.type = "b1" # retyped; share_type still "k1"
|
||
assert fit._edge_cap(leaf) == pytest.approx(8.0)
|
||
|
||
|
||
def test_edge_cap_uses_largest_share_among_adjoining_leaves():
|
||
# an interior wall takes the max share of the two leaves it separates
|
||
fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
|
||
cap = fit._edge_cap(_leaf("k1"), _shared_leaf("b1", k=2))
|
||
assert cap == pytest.approx(16.0)
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# Stair helpers
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_risers_number_exact_division():
|
||
# 2.0 / 0.25 = 8.0 exactly → returns 8
|
||
assert Fitness._risers_number(2.0, 0.25) == 8
|
||
|
||
|
||
def test_risers_number_rounds_up():
|
||
# 3.0 / 0.19 ≈ 15.789 → rounds up to 16
|
||
assert Fitness._risers_number(3.0, 0.19) == 16
|
||
|
||
|
||
def test_ideal_going_clamps_to_minimum():
|
||
# riser=0.25 → going=0.125 < 0.22 → clamp
|
||
assert Fitness._ideal_going(0.25) == 0.22
|
||
|
||
|
||
def test_ideal_going_above_minimum():
|
||
# riser=0.15 → going=0.325 > 0.22; result should be in valid range
|
||
result = Fitness._ideal_going(0.15)
|
||
assert result >= 0.22
|
||
assert result <= 0.625
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# Graded high-fail objective (§11.4)
|
||
# --------------------------------------------------------------------------- #
|
||
|
||
|
||
def test_leaf_grade_no_failing_factors_is_zero():
|
||
# All factors above FAIL_THRESHOLD → no proximity credit.
|
||
assert _leaf_grade({"size": 0.9, "width": 1.0, "access": 1.0}) == 0.0
|
||
|
||
|
||
def test_leaf_grade_credits_only_failing_factors():
|
||
# Only size fails (0.05 < 0.1); credit = 0.05 / 0.1 = 0.5.
|
||
g = _leaf_grade({"size": 0.05, "width": 0.5, "proportion": 1.0})
|
||
assert g == pytest.approx(0.05 / FAIL_THRESHOLD)
|
||
|
||
|
||
def test_leaf_grade_monotone_in_proximity():
|
||
# A failing factor closer to the threshold scores higher (better).
|
||
deep = _leaf_grade({"size": 0.01})
|
||
shallow = _leaf_grade({"size": 0.09})
|
||
assert shallow > deep
|
||
|
||
|
||
def test_leaf_grade_sums_over_failing_factors():
|
||
g = _leaf_grade({"size": 0.04, "width": 0.06, "access": 1.0})
|
||
assert g == pytest.approx((0.04 + 0.06) / FAIL_THRESHOLD)
|
||
|
||
|
||
def test_leaf_grade_ignores_non_graded_keys():
|
||
# daylight is pinned and never a graded factor even if below threshold.
|
||
assert _leaf_grade({"daylight": 0.0}) == 0.0
|