def test_calculate_population_energies(self):
# if popsize is 3 then the overall generation has size (6,)
solver = DifferentialEvolutionSolver(rosen, self.bounds, popsize=3)
solver._calculate_population_energies(solver.population)
solver._promote_lowest_energy()
assert_equal(np.argmin(solver.population_energies), 0)
# initial calculation of the energies should require 6 nfev.
assert_equal(solver._nfev, 6)
def test_calculate_population_energies(self):
# if popsize is 3 then the overall generation has size (6,)
solver = DifferentialEvolutionSolver(rosen, self.bounds, popsize=3)
solver._calculate_population_energies(solver.population)
solver._promote_lowest_energy()
assert_equal(np.argmin(solver.population_energies), 0)
# initial calculation of the energies should require 6 nfev.
assert_equal(solver._nfev, 6)
def test_impossible_constraint(self):
def constr_f(x):
return np.array([x[0] + x[1]])
nlc = NonlinearConstraint(constr_f, -np.inf, -1)
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
constraints=(nlc),
popsize=3,
seed=1)
# a UserWarning is issued because the 'trust-constr' polishing is
# attempted on the least infeasible solution found.
with warns(UserWarning):
res = solver.solve()
assert res.maxcv > 0
assert not res.success
# test _promote_lowest_energy works when none of the population is
# feasible. In this case the solution with the lowest constraint
# violation should be promoted.
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
constraints=(nlc),
polish=False)
next(solver)
assert not solver.feasible.all()
assert not np.isfinite(solver.population_energies).all()
# now swap two of the entries in the population
l = 20
cv = solver.constraint_violation[0]
solver.population_energies[[0, l]] = solver.population_energies[[l, 0]]
solver.population[[0, l], :] = solver.population[[l, 0], :]
solver.constraint_violation[[0, l], :] = (
solver.constraint_violation[[l, 0], :])
solver._promote_lowest_energy()
assert_equal(solver.constraint_violation[0], cv)
def test_impossible_constraint(self):
def constr_f(x):
return np.array([x[0] + x[1]])
nlc = NonlinearConstraint(constr_f, -np.inf, -1)
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
constraints=(nlc), popsize=3)
# a UserWarning is issued because the 'trust-constr' polishing is
# attempted on the least infeasible solution found.
with warns(UserWarning):
res = solver.solve()
assert_allclose(res.x, [0, 0], atol=1e-6)
assert res.maxcv > 0
assert not res.success
# test _promote_lowest_energy works when none of the population is
# feasible. In this case the solution with the lowest constraint
# violation should be promoted.
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
constraints=(nlc), polish=False)
next(solver)
assert not solver.feasible.all()
assert not np.isfinite(solver.population_energies).all()
# now swap two of the entries in the population
l = 20
cv = solver.constraint_violation[0]
solver.population_energies[[0, l]] = solver.population_energies[[l, 0]]
solver.population[[0, l], :] = solver.population[[l, 0], :]
solver.constraint_violation[[0, l], :] = (
solver.constraint_violation[[l, 0], :])
solver._promote_lowest_energy()
assert_equal(solver.constraint_violation[0], cv)
