65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
|
|
"""Inner-loop search tests against a fake evaluator (no perl, no oracle)."""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from homemaker import innerloop
|
||
|
|
from homemaker.oracle import Score
|
||
|
|
|
||
|
|
|
||
|
|
class FakeEvaluator:
|
||
|
|
"""Duck-typed OracleEvaluator over an analytic objective."""
|
||
|
|
|
||
|
|
def __init__(self, fn):
|
||
|
|
self.fn = fn
|
||
|
|
self.n_evals = 0
|
||
|
|
self.n_oracle_calls = 0
|
||
|
|
|
||
|
|
def evaluate(self, xs):
|
||
|
|
self.n_evals += len(xs)
|
||
|
|
self.n_oracle_calls += 1
|
||
|
|
return [Score(fitness=self.fn(np.asarray(x)), fails="") for x in xs]
|
||
|
|
|
||
|
|
|
||
|
|
def concave(x):
|
||
|
|
# maximum 1.0 at 0.3 in every coordinate
|
||
|
|
return float(1.0 - np.sum((x - 0.3) ** 2))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("search", [innerloop.compass_search, innerloop.cma_search])
|
||
|
|
def test_search_converges_on_concave(search):
|
||
|
|
ev = FakeEvaluator(concave)
|
||
|
|
r = search(ev, np.full(4, 0.7), budget=400)
|
||
|
|
assert r.fitness > 0.999
|
||
|
|
assert np.allclose(r.x, 0.3, atol=0.05)
|
||
|
|
assert r.x0_fitness == pytest.approx(concave(np.full(4, 0.7)))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("search", [innerloop.compass_search, innerloop.cma_search])
|
||
|
|
def test_search_respects_budget_and_bounds(search):
|
||
|
|
seen = []
|
||
|
|
|
||
|
|
def spy(x):
|
||
|
|
seen.append(x.copy())
|
||
|
|
return concave(x)
|
||
|
|
|
||
|
|
ev = FakeEvaluator(spy)
|
||
|
|
r = search(ev, np.full(3, 0.5), budget=60)
|
||
|
|
# one batch may run slightly over, but never a whole extra cycle
|
||
|
|
assert r.n_evals == ev.n_evals <= 60 + 3 * 10
|
||
|
|
assert all((x >= innerloop._EPS - 1e-12).all() and (x <= 1 - innerloop._EPS + 1e-12).all()
|
||
|
|
for x in seen)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compass_never_returns_worse_than_start():
|
||
|
|
# a hostile objective: best at the start, everything else worse
|
||
|
|
x0 = np.full(3, 0.5)
|
||
|
|
|
||
|
|
def hostile(x):
|
||
|
|
return -float(np.sum(np.abs(x - x0)))
|
||
|
|
|
||
|
|
ev = FakeEvaluator(hostile)
|
||
|
|
r = innerloop.compass_search(ev, x0, budget=100)
|
||
|
|
assert r.fitness == pytest.approx(0.0)
|
||
|
|
assert np.allclose(r.x, x0)
|