homemaker-layout/experiments/validate_shapecurve_multistorey.py

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"""Multi-storey validation harness for shapecurve.py (homemaker-py-koo).
Same protocol as validate_shapecurve.py (DP feasibility vs NM search that
directly MINIMISES shape-fail count, shape-fail-count-only comparison -- see
that module's docstring for why), but for genuinely multi-storey topologies:
each trial starts from a 2-storey seed (``operators.mutate_level_add`` once on
a bare single-leaf plot) and grows leaves across BOTH storeys via
``driver.random_topology``'s ``mutate_divide`` loop (which picks candidate
leaves from every level uniformly, see ``operators._leaves``), so a trial
topology naturally contains a mix of below-inherited-fixed spines and
below-fixed-box/free-split fringe nodes on the upper storey -- exactly the
scenario homemaker-py-koo generalised the DP for.
Usage: python experiments/validate_shapecurve_multistorey.py [n_topologies] [nm_budget] [grid_n] [programme_dir]
"""
from __future__ import annotations
import copy
import sys
import time
import numpy as np
from homemaker_layout import dom, driver, fitness as fit_mod, geometry, innerloop, operators, solver
from homemaker_layout import shapecurve as sc
PROGRAMME_DIR = "examples/harbor-house"
_SHAPE_SUFFIXES = (" size", " width", " proportion")
class ShapeFailEvaluator(innerloop.NativeEvaluator):
"""Duplicated from validate_shapecurve.py (kept standalone-runnable, like
that script, rather than depending on package-relative imports): scores
-n_shape_fails (ties broken by the real fitness) so nm_search's greedy
hill-climb directly minimises shape-fail count instead of the full
aggregate objective -- see that module's docstring for why this is the
correct apples-to-apples comparison against what the DP claims to solve."""
def evaluate(self, xs):
results = []
for x in xs:
self.apply(x)
root_copy = copy.deepcopy(self.root)
score, fails = self._fit.score_with_fails(root_copy)
n_shape = sum(1 for f in fails if f.endswith(_SHAPE_SUFFIXES))
proxy_fitness = -n_shape + min(score, 0.999)
results.append(innerloop._NativeScore(fitness=proxy_fitness, fail_lines=fails))
self.n_evals += len(xs)
self.n_oracle_calls += 1
return results
def _two_storey_seed(programme_dir: str, rng: np.random.Generator, types: list[str]) -> dom.Node:
base = dom.load(f"{programme_dir}/init.dom")
two_storey, _ = operators.mutate_level_add(base, rng, types)
return two_storey
def main(n_topologies: int = 100, nm_budget: int = 100, grid_n: int = 150,
programme_dir: str = PROGRAMME_DIR) -> None:
conf, cost = fit_mod.load_config(programme_dir)
fit = fit_mod.Fitness(conf, cost)
types = sorted(fit.spaces.keys())
rng = np.random.default_rng(12345)
n_agree = 0
n_dp_feasible = 0
n_nm_feasible = 0
false_positive = 0 # DP says feasible, NM finds a shape fail
false_negative = 0 # DP says infeasible, NM reaches 0 shape fails anyway
dp_time_total = 0.0
nm_time_total = 0.0
rows = []
i = 0
attempts = 0
while i < n_topologies:
attempts += 1
n_leaves = int(rng.integers(3, 20))
seed = int(rng.integers(0, 2**31 - 1))
trng = np.random.default_rng(seed)
seed_root = _two_storey_seed(programme_dir, trng, types)
topo = driver.random_topology(seed_root, n_leaves, trng, types)
dom.link(topo)
if len(dom.levels(topo)) < 2:
continue
if len(solver.free_branches(topo)) < 2:
continue # not enough freedom for a meaningful multi-storey check
i += 1
t0 = time.time()
try:
dp_ok, info = sc.solve(topo, fit, grid_n=grid_n)
except Exception as exc: # noqa: BLE001 -- record and keep going
dp_ok, info = None, {"error": repr(exc)}
dp_time = time.time() - t0
dp_time_total += dp_time
t0 = time.time()
topo_nm = copy.deepcopy(topo)
geometry.clear_cache()
with ShapeFailEvaluator(topo_nm, programme_dir) as ev:
x0 = ev.x_current
if len(x0) == 0:
nm_shape_fails: list[str] = []
else:
r = innerloop.nm_search(ev, x0, budget=nm_budget)
nm_shape_fails = [f for f in r.fail_lines if f.endswith(_SHAPE_SUFFIXES)]
nm_time = time.time() - t0
nm_time_total += nm_time
nm_ok = len(nm_shape_fails) == 0
if dp_ok is None:
rows.append((i, n_leaves, seed, "ERROR", nm_ok, dp_time, nm_time, info.get("error")))
continue
if dp_ok:
n_dp_feasible += 1
if nm_ok:
n_nm_feasible += 1
agree = dp_ok == nm_ok
if agree:
n_agree += 1
else:
if dp_ok and not nm_ok:
false_positive += 1
else:
false_negative += 1
rows.append((i, n_leaves, seed, dp_ok, nm_ok, dp_time, nm_time, len(nm_shape_fails)))
print(f"topologies: {n_topologies} (attempts {attempts})")
print(f"agreement: {n_agree}/{n_topologies} = {100*n_agree/n_topologies:.1f}%")
print(f" false positive (DP feasible, NM shape-fails): {false_positive}")
print(f" false negative (DP infeasible, NM 0 shape-fails): {false_negative}")
print(f"DP feasible: {n_dp_feasible}/{n_topologies} NM 0-shape-fail: {n_nm_feasible}/{n_topologies}")
print(f"DP total time: {dp_time_total:.2f}s ({dp_time_total/n_topologies*1000:.1f} ms/topology)")
print(f"NM total time: {nm_time_total:.2f}s ({nm_time_total/n_topologies*1000:.1f} ms/topology)")
print(f"speedup: {nm_time_total/dp_time_total:.1f}x")
print("\nmismatches:")
for row in rows:
if row[3] != row[4] and row[3] != "ERROR":
print(" ", row)
print("\nerrors:")
for row in rows:
if row[3] == "ERROR":
print(" ", row)
if __name__ == "__main__":
n = int(sys.argv[1]) if len(sys.argv) > 1 else 100
budget = int(sys.argv[2]) if len(sys.argv) > 2 else 100
grid_n = int(sys.argv[3]) if len(sys.argv) > 3 else 150
prog_dir = sys.argv[4] if len(sys.argv) > 4 else PROGRAMME_DIR
main(n, budget, grid_n, prog_dir)