Bruno Postle
393183b356
§13.7 flagged edge-too-long as harbor's top fail class. Dissection showed the bulk are a leaf-sharing REPRESENTATION ARTIFACT: a share=k leaf aggregates k same-code rooms, so its walls run ~k× the flat 8 m cap purely for being big — the same §13.3 leak (size/missing relaxed for shared leaves) on the wall measure, since edge_cost/outside_edge_cost ignored leaf.share. Fix: Fitness._edge_cap(*leaves) scales the 8 m cap by the largest type-guarded leaf_share among adjoining leaves, mirroring quality_size's k×target; non-shared leaves keep the flat cap so genuine narrow/oversize pathologies stay flagged. Gated behind a share_edge_cap config knob (SHAREEDGE env), default OFF so the §13.x controls reproduce. A/B (full Phase-8 stack, staged, 20k evals, seeds 0/1/2): control reproduces §13.7 (maple 80.3 exact, harbor 34.7≈34.0); share-aware arm maple 80.3→74.0 (−7.9%), harbor 34.7→31.0 (−10.6%), zero regressions across 6 seeds. Positive and monotone-harmless (only ever removes a false-positive fail). Verdict: recommend default-ON; follow-up issue flips the default + rebaselines the floor. Tests: 6 new unit tests for _edge_cap (221 pass). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JygRv4n2dcyDQqMiDRe7TN
326 lines
11 KiB
Python
326 lines
11 KiB
Python
"""Unit tests for fitness.py quality terms and helpers (oracle-free)."""
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import pytest
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from homemaker_layout import dom, geometry
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from homemaker_layout.dom import Node
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from homemaker_layout.fitness import (
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CONF_DEFAULTS,
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COST_DEFAULTS,
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FAIL_THRESHOLD,
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Fitness,
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_leaf_grade,
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gaussian,
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)
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def _leaf(type_: str, size: float = 4.0) -> Node:
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"""Undivided level-root leaf with a square plot of side `size`."""
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geometry.clear_cache()
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return Node(
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node=[[0.0, 0.0], [size, 0.0], [size, size], [0.0, size]],
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type=type_,
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)
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# --------------------------------------------------------------------------- #
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# gaussian
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# --------------------------------------------------------------------------- #
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def test_gaussian_peak_returns_a():
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assert gaussian(5.0, 1.0, 5.0, 1.0) == pytest.approx(1.0)
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def test_gaussian_peak_scales_by_a():
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assert gaussian(3.0, 2.5, 3.0, 1.0) == pytest.approx(2.5)
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def test_gaussian_one_sigma_uses_truncated_e():
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# Urb uses e=2.718281828, not math.e; at one sigma the factor is e^-0.5
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e = 2.718281828
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expected = e ** -0.5
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assert gaussian(6.0, 1.0, 5.0, 1.0) == pytest.approx(expected, rel=1e-9)
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def test_gaussian_symmetry():
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assert gaussian(4.0, 1.0, 5.0, 1.0) == pytest.approx(gaussian(6.0, 1.0, 5.0, 1.0))
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# --------------------------------------------------------------------------- #
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# Fitness.conf / cost
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# --------------------------------------------------------------------------- #
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def test_conf_falls_back_to_defaults():
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assert Fitness().conf("value_inside") == CONF_DEFAULTS["value_inside"]
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def test_conf_override_wins():
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assert Fitness(conf={"value_inside": 999.0}).conf("value_inside") == 999.0
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def test_conf_unknown_key_returns_none():
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assert Fitness().conf("no_such_key") is None
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def test_cost_falls_back_to_defaults():
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assert Fitness().cost("inside") == COST_DEFAULTS["inside"]
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def test_cost_override_wins():
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assert Fitness(cost={"inside": 42.0}).cost("inside") == 42.0
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def test_cost_unknown_key_returns_zero():
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assert Fitness().cost("no_such_key") == 0.0
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# --------------------------------------------------------------------------- #
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# get_space_params lookup chain
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# --------------------------------------------------------------------------- #
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def test_get_space_params_circulation_size():
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assert Fitness().get_space_params("C", "size") == CONF_DEFAULTS["size_circulation"]
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def test_get_space_params_outside_width():
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assert Fitness().get_space_params("O", "width") == CONF_DEFAULTS["width_outside"]
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def test_get_space_params_sahn_proportion():
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assert Fitness().get_space_params("S", "proportion") == CONF_DEFAULTS["proportion_outside"]
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def test_get_space_params_inside_falls_back_to_inside_defaults():
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assert Fitness().get_space_params("k1", "proportion") == CONF_DEFAULTS["proportion_inside"]
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assert Fitness().get_space_params("k1", "size") == CONF_DEFAULTS["size_inside"]
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def test_get_space_params_named_space_overrides_default():
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f = Fitness(conf={"spaces": {"k1": {"size": [20.0, 4.0]}}})
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assert f.get_space_params("k1", "size") == [20.0, 4.0]
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# --------------------------------------------------------------------------- #
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# quality_proportion
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# --------------------------------------------------------------------------- #
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def test_quality_proportion_square_inside_returns_one():
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# aspect=1.0 < proportion_inside[0]=1.5 → 1.0
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assert Fitness().quality_proportion(_leaf("k1")) == pytest.approx(1.0)
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def test_quality_proportion_square_outside_returns_one():
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# aspect=1.0 < proportion_outside[0]=1.5 → 1.0
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assert Fitness().quality_proportion(_leaf("O")) == pytest.approx(1.0)
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def test_quality_proportion_square_circulation_returns_one():
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assert Fitness().quality_proportion(_leaf("C")) == pytest.approx(1.0)
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# --------------------------------------------------------------------------- #
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# quality_size
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# --------------------------------------------------------------------------- #
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def test_quality_size_outside_always_one():
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assert Fitness().quality_size(_leaf("O")) == 1.0
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def test_quality_size_sahn_always_one():
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assert Fitness().quality_size(_leaf("S")) == 1.0
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def test_quality_size_inside_at_peak():
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# size_inside=[16.0,3.5]; leaf is 4×4=16 m² → gaussian at peak → 1.0
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leaf = _leaf("k1", size=4.0)
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assert geometry.area(leaf) == pytest.approx(16.0)
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assert Fitness().quality_size(leaf) == pytest.approx(1.0)
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def test_quality_size_circulation_at_peak():
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# size_circulation=[0.0,14.0]; peak at 0, gaussian(area,1,0,14) → always <1 for area>0
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# Just verify it returns a value in [0,1]
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f = Fitness().quality_size(_leaf("C", size=4.0))
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assert 0.0 < f <= 1.0
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# --------------------------------------------------------------------------- #
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# quality_width
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# --------------------------------------------------------------------------- #
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def test_quality_width_wide_inside_returns_one():
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# width_inside=[4.0,1.0]; 10m side > 4.0 → 1.0
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assert Fitness().quality_width(_leaf("k1", size=10.0)) == pytest.approx(1.0)
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def test_quality_width_wide_circulation_returns_one():
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# width_circulation=[2.4,0.2]; 10m > 2.4 → 1.0
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assert Fitness().quality_width(_leaf("C", size=10.0)) == pytest.approx(1.0)
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def test_quality_width_wide_outside_ground_uses_gaussian():
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# outside at level 0 falls through to gaussian; 10m > width_outside[0]=3.0 → 1.0
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assert Fitness().quality_width(_leaf("O", size=10.0)) == pytest.approx(1.0)
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# --------------------------------------------------------------------------- #
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# quality_perpendicular
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# --------------------------------------------------------------------------- #
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def test_quality_perpendicular_rectangle_near_one():
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# All four corners of the square are pi/2; perpendicular formula gives ≈1
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leaf = _leaf("k1", size=4.0)
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result = Fitness().quality_perpendicular(leaf)
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assert result == pytest.approx(1.0, abs=1e-6)
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# --------------------------------------------------------------------------- #
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# value_rate
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# --------------------------------------------------------------------------- #
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def test_value_rate_outside_ground():
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leaf = _leaf("O")
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assert dom.level_of(leaf) == 0
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assert Fitness().value_rate(leaf) == pytest.approx(CONF_DEFAULTS["value_outside"])
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def test_value_rate_circulation():
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assert Fitness().value_rate(_leaf("C")) == pytest.approx(CONF_DEFAULTS["value_circulation"])
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def test_value_rate_inside():
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assert Fitness().value_rate(_leaf("k1")) == pytest.approx(CONF_DEFAULTS["value_inside"])
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# --------------------------------------------------------------------------- #
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# leaf_cost
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# --------------------------------------------------------------------------- #
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def test_leaf_cost_outside_bare():
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# not covered, not supported → outside rate × area
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leaf = _leaf("O", size=4.0) # area = 16.0
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assert Fitness().leaf_cost(leaf) == pytest.approx(COST_DEFAULTS["outside"] * 16.0)
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def test_leaf_cost_inside():
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leaf = _leaf("k1", size=4.0)
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assert Fitness().leaf_cost(leaf) == pytest.approx(COST_DEFAULTS["inside"] * 16.0)
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# --------------------------------------------------------------------------- #
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# Share-aware edge-too-long cap (hph §13.7)
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# --------------------------------------------------------------------------- #
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def _shared_leaf(type_: str = "k1", k: int = 3) -> Node:
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leaf = _leaf(type_)
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leaf.share = k
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leaf.share_type = type_
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return leaf
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def test_edge_cap_flat_by_default():
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# no leaf_sharing → flat 8 m regardless of any share stamp
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fit = Fitness()
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assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(8.0)
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def test_edge_cap_flat_when_lever_off_even_with_sharing():
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# leaf_sharing on but the hph lever off → still flat (control arm)
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fit = Fitness(conf={"leaf_sharing": True})
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assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(8.0)
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def test_edge_cap_scales_by_share_when_lever_on():
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fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
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assert fit._edge_cap(_shared_leaf(k=3)) == pytest.approx(24.0)
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def test_edge_cap_unshared_leaf_keeps_flat_cap():
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# a non-shared leaf (the narrow-sliver pathology) is never relaxed
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fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
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assert fit._edge_cap(_leaf("k1")) == pytest.approx(8.0)
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def test_edge_cap_stale_share_type_ignored():
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# retyped leaf whose stamp no longer matches type → share invalid → flat
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fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
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leaf = _shared_leaf("k1", k=3)
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leaf.type = "b1" # retyped; share_type still "k1"
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assert fit._edge_cap(leaf) == pytest.approx(8.0)
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def test_edge_cap_uses_largest_share_among_adjoining_leaves():
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# an interior wall takes the max share of the two leaves it separates
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fit = Fitness(conf={"leaf_sharing": True, "share_edge_cap": True})
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cap = fit._edge_cap(_leaf("k1"), _shared_leaf("b1", k=2))
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assert cap == pytest.approx(16.0)
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# --------------------------------------------------------------------------- #
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# Stair helpers
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# --------------------------------------------------------------------------- #
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def test_risers_number_exact_division():
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# 2.0 / 0.25 = 8.0 exactly → returns 8
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assert Fitness._risers_number(2.0, 0.25) == 8
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def test_risers_number_rounds_up():
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# 3.0 / 0.19 ≈ 15.789 → rounds up to 16
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assert Fitness._risers_number(3.0, 0.19) == 16
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def test_ideal_going_clamps_to_minimum():
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# riser=0.25 → going=0.125 < 0.22 → clamp
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assert Fitness._ideal_going(0.25) == 0.22
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def test_ideal_going_above_minimum():
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# riser=0.15 → going=0.325 > 0.22; result should be in valid range
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result = Fitness._ideal_going(0.15)
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assert result >= 0.22
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assert result <= 0.625
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# --------------------------------------------------------------------------- #
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# Graded high-fail objective (§11.4)
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# --------------------------------------------------------------------------- #
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def test_leaf_grade_no_failing_factors_is_zero():
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# All factors above FAIL_THRESHOLD → no proximity credit.
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assert _leaf_grade({"size": 0.9, "width": 1.0, "access": 1.0}) == 0.0
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def test_leaf_grade_credits_only_failing_factors():
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# Only size fails (0.05 < 0.1); credit = 0.05 / 0.1 = 0.5.
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g = _leaf_grade({"size": 0.05, "width": 0.5, "proportion": 1.0})
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assert g == pytest.approx(0.05 / FAIL_THRESHOLD)
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def test_leaf_grade_monotone_in_proximity():
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# A failing factor closer to the threshold scores higher (better).
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deep = _leaf_grade({"size": 0.01})
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shallow = _leaf_grade({"size": 0.09})
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assert shallow > deep
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def test_leaf_grade_sums_over_failing_factors():
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g = _leaf_grade({"size": 0.04, "width": 0.06, "access": 1.0})
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assert g == pytest.approx((0.04 + 0.06) / FAIL_THRESHOLD)
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def test_leaf_grade_ignores_non_graded_keys():
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# daylight is pinned and never a graded factor even if below threshold.
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assert _leaf_grade({"daylight": 0.0}) == 0.0
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