def test_quadratic(self):
# test the quadratic function from object
solver = DifferentialEvolutionSolver(self.quadratic,
[(-100, 100)],
tol=0.02)
solver.solve()
assert_equal(np.argmin(solver.population_energies), 0)
def test_maxfun_stops_solve(self):
# test that if the maximum number of function evaluations is exceeded
# during initialisation the solver stops
solver = DifferentialEvolutionSolver(rosen, self.bounds, maxfun=1,
polish=False)
result = solver.solve()
assert_equal(result.nfev, 2)
assert_equal(result.success, False)
assert_equal(result.message,
'Maximum number of function evaluations has '
'been exceeded.')
# test that if the maximum number of function evaluations is exceeded
# during the actual minimisation, then the solver stops.
# Have to turn polishing off, as this will still occur even if maxfun
# is reached. For popsize=5 and len(bounds)=2, then there are only 10
# function evaluations during initialisation.
solver = DifferentialEvolutionSolver(rosen,
self.bounds,
popsize=5,
polish=False,
maxfun=40)
result = solver.solve()
assert_equal(result.nfev, 41)
assert_equal(result.success, False)
assert_equal(result.message,
'Maximum number of function evaluations has '
'been exceeded.')
def test_deferred_updating(self):
# check setting of deferred updating, with default workers
bounds = [(0., 2.), (0., 2.)]
solver = DifferentialEvolutionSolver(rosen, bounds, updating='deferred')
assert_(solver._updating == 'deferred')
assert_(solver._mapwrapper._mapfunc is map)
solver.solve()
def test_exp_runs(self):
# test whether exponential mutation loop runs
solver = DifferentialEvolutionSolver(rosen,
self.bounds,
strategy='best1exp',
maxiter=1)
solver.solve()
def test_maxiter_none_GH5731(self):
# Pre 0.17 the previous default for maxiter and maxfun was None.
# the numerical defaults are now 1000 and np.inf. However, some scripts
# will still supply None for both of those, this will raise a TypeError
# in the solve method.
solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=None,
maxfun=None)
solver.solve()
def test_maxiter_stops_solve(self):
# test that if the maximum number of iterations is exceeded
# the solver stops.
solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=1)
result = solver.solve()
assert_equal(result.success, False)
assert_equal(result.message,
'Maximum number of iterations has been exceeded.')
def test_constraint_solve(self):
def constr_f(x):
return np.array([x[0] + x[1]])
nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
constraints=(nlc))
# trust-constr warns if the constraint function is linear
with warns(UserWarning):
res = solver.solve()
assert constr_f(res.x) <= 1.9
assert res.success
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)
def test_quadratic(self):
# test the quadratic function from object
solver = DifferentialEvolutionSolver(self.quadratic, [(-100, 100)],
tol=0.02)
solver.solve()
assert_equal(np.argmin(solver.population_energies), 0)
def test_convergence(self):
solver = DifferentialEvolutionSolver(rosen, self.bounds, tol=0.2,
polish=False)
solver.solve()
assert_(solver.convergence < 0.2)
def test_quadratic(self):
# test the quadratic function from object
solver = DifferentialEvolutionSolver(self.quadratic,
[(-100, 100)],
tol=0.02)
solver.solve()
def test_quadratic(self):
# test the quadratic function from object
solver = DifferentialEvolutionSolver(self.quadratic, [(-100, 100)],
tol=0.02)
solver.solve()
def test_convergence(self):
solver = DifferentialEvolutionSolver(rosen, self.bounds, tol=0.2,
polish=False)
solver.solve()
assert_(solver.convergence < 0.2)
def test_best_solution_retrieval(self):
# test that the getter property method for the best solution works.
solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
result = solver.solve()
assert_almost_equal(result.x, solver.x)
def test_differential_evolution(self):
# test that the Jmin of DifferentialEvolutionSolver
# is the same as the function evaluation
solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
result = solver.solve()
assert_almost_equal(result.fun, self.quadratic(result.x))
def test_differential_evolution(self):
# test that the Jmin of DifferentialEvolutionSolver
# is the same as the function evaluation
solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
result = solver.solve()
assert_almost_equal(result.fun, self.quadratic(result.x))
def test_best_solution_retrieval(self):
# test that the getter property method for the best solution works.
solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
result = solver.solve()
assert_almost_equal(result.x, solver.x)
def test_converged(self):
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)])
solver.solve()
assert_(solver.converged())
def test_converged(self):
solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)])
solver.solve()
assert_(solver.converged())
