def test_localopt_error_saving():
_, sim_specs_0, gen_specs_0, _, _ = setup.hist_setup1()
H = np.zeros(4,
dtype=gen_out + [('f', float), ('fvec', float, 2),
('returned', bool)])
H['x'] = np.random.uniform(0, 1, (4, 2))
H['f'] = np.random.uniform(0, 1, 4)
H['returned'] = True
H['local_pt'][1:] = True
gen_specs_0['user']['initial_sample_size'] = 1
gen_specs_0['user']['localopt_method'] = 'scipy_COBYLA'
gen_specs_0['user']['tol'] = 0.1
gen_specs_0['user']['ub'] = np.ones(2)
gen_specs_0['user']['lb'] = np.zeros(2)
persis_info_1 = {
'run_order': {
0: [1, 2, 3]
},
'old_runs': {},
'total_runs': 0,
'rand_stream': np.random.RandomState(1)
}
try:
al.aposmm_logic(H, persis_info_1, gen_specs_0, _)
except Exception as e:
assert e.args[
0] == 'Exit code is 0, but x_new was not updated in local opt run 0 after 3 evaluations.\nSaving run information to: run_0_abort.pickle\nWorker crashing!'
else:
assert 0
def test_trim_H():
hist, _, _, _, _ = setup.hist_setup1(13)
hist.index = 10
H = hist.trim_H()
assert np.array_equal(H, wrs), "Array does not match expected"
assert hist.given_count == 0
assert hist.index == 10
assert hist.sim_count == 0
def test_grow_H():
hist, _, _, _, _ = setup.hist_setup1(3)
new_rows = 7
hist.grow_H(k=new_rows)
assert np.array_equal(hist.H, wrs), "Array does not match expected"
assert hist.given_count == 0
assert hist.index == 0
assert hist.sim_count == 0
def test_term_test_1():
# termination_test should be True when we want to stop
# sh - Should separate the tests
# Test 1
hist, sim_specs, gen_specs, exit_criteria, al = setup.hist_setup1()
mgr = man.Manager(hist, libE_specs, al, sim_specs, gen_specs,
exit_criteria)
assert not mgr.term_test()
def test_exception_raising():
hist, sim_specs_0, gen_specs_0, exit_criteria_0, alloc = setup.hist_setup1(n=2)
hist.H['returned'] = 1
for method in ['LN_SBPLX', 'pounders', 'scipy_COBYLA']:
gen_specs_0['user']['localopt_method'] = method
out = al.advance_local_run(hist.H, gen_specs_0['user'], 0, 0, {'run_order': {0: [0, 1]}})
assert out[0] == 0, "Failed like it should have"
def test_exception_raising():
hist, sim_specs_0, gen_specs_0, exit_criteria_0, alloc = setup.hist_setup1()
hist.H['returned'] = 1
for method in ['LN_SBPLX','pounders']:
gen_specs_0['localopt_method'] = method
try:
al.advance_localopt_method(hist.H, gen_specs_0, 0, 0, {'run_order': {0:[0,1]}})
except:
assert 1, "Failed like it should have"
else:
assert 0, "Failed like it should have"
def test_failing_localopt_method():
hist, sim_specs_0, gen_specs_0, exit_criteria_0, alloc = setup.hist_setup1()
hist.H['returned'] = 1
gen_specs_0['user']['localopt_method'] = 'BADNAME'
try:
al.advance_local_run(hist.H, gen_specs_0['user'], 0, 0, {'run_order': {0: [0, 1]}})
except al.APOSMMException:
assert 1, "Failed like it should have"
else:
assert 0, "Didn't fail like it should have"
def test_hist_init_1A_H0():
hist, _, _, _, _ = setup.hist_setup1(sim_max=2, H0_in=wrs_H0)
# Compare by column
for field in exp_H0_H.dtype.names:
np.array_equal(hist.H[field], exp_H0_H[field])
#These dont work for numpy structured arrays
#assert np.array_equiv(hist.H, exp_H0_H), "Array does not match expected"
#assert np.array_equal(hist.H, exp_H0_H), "Array does not match expected"
assert hist.given_count == 3
assert hist.index == 3
assert hist.sim_count == 3
assert len(hist.H) == 5
def test_persis_apossm_localopt_test():
_, _, gen_specs_0, _, _ = setup.hist_setup1()
H = np.zeros(4, dtype=[('f', float), ('returned', bool)])
gen_specs_0['user']['localopt_method'] = 'BADNAME'
gen_specs_0['user']['ub'] = np.ones(2)
gen_specs_0['user']['lb'] = np.zeros(2)
try:
al.aposmm(H, {}, gen_specs_0, libE_specs)
except NotImplementedError:
assert 1, "Failed because method is unknown."
else:
assert 0
def test_update_history_x_out():
hist, _, _, _, _ = setup.hist_setup1()
# First update a single point
hist.update_history_x_out(q_inds=0, sim_worker=2)
# Check updated values for point and counts
assert hist.H['given'][0] == True
assert hist.H['sim_worker'][0] == 2
assert hist.given_count == 1
# Check some unchanged values for point and counts
assert hist.index == 0
assert hist.sim_count == 0
hist.H['returned'][0] == False
hist.H['allocated'][0] == False
hist.H['f'][0] == 0.0
hist.H['sim_id'][0] == -1
# Check the rest of H is unaffected
assert np.array_equal(hist.H[1:10],
wrs[1:10]), "H Array slice does not match expected"
# Update two further consecutive points
my_qinds = np.arange(1, 3)
hist.update_history_x_out(q_inds=my_qinds, sim_worker=3)
# Check updated values for point and counts
assert np.all(hist.H['given'][0:3]) # Include previous point
assert np.all(hist.H['sim_worker'][my_qinds] == 3)
assert hist.given_count == 3
# Update three further non-consecutive points
my_qinds = np.array([4, 7, 9])
hist.update_history_x_out(q_inds=my_qinds, sim_worker=4)
#Try to avoid tautological testing - compare columns
assert np.array_equal(
hist.H['given'],
np.array(
[True, True, True, False, True, False, False, True, False, True]))
assert np.array_equal(hist.H['sim_worker'],
np.array([2, 3, 3, 0, 4, 0, 0, 4, 0, 4]))
assert np.all(hist.H['returned'] == False) # Should still be unaffected.
# Check counts
assert hist.given_count == 6
assert hist.index == 0 # In real case this would be ahead.....
assert hist.sim_count == 0
def test_declare_opt():
hist, sim_specs_0, gen_specs_0, exit_criteria_0, alloc = setup.hist_setup1(n=2)
try:
al.update_history_optimal(hist.H['x_on_cube'][0]+1, hist.H, np.arange(0, 10))
except AssertionError:
assert 1, "Failed because the best point is not in H"
else:
assert 0
hist.H['x_on_cube'][1] += np.finfo(float).eps
hist.H['f'][1] -= np.finfo(float).eps
# Testing case where point near x_opt is slightly better.
al.update_history_optimal(hist.H['x_on_cube'][0], hist.H, np.arange(0, 10))
assert np.sum(hist.H['local_min']) == 2
def test_update_history_optimal():
hist, _, _, _, _ = setup.hist_setup1(n=2)
H = hist.H
H['returned'] = True
H['sim_id'] = range(len(H))
H['f'][0] = -1e-8
H['x_on_cube'][-1] = 1e-10
# Perturb x_opt point to test the case where the reported minimum isn't
# exactly in H. Also, a point in the neighborhood of x_opt has a better
# function value.
opt_ind = al.update_history_optimal(H['x_on_cube'][-1]+1e-12, 1, H, np.arange(len(H)))
assert opt_ind == 9, "Wrong point declared minimum"
def test_initialize_APOSMM():
hist, sim_specs_0, gen_specs_0, exit_criteria_0, alloc = setup.hist_setup1(
)
al.initialize_APOSMM(hist.H, gen_specs_0)
def test_hist_init_1():
hist, _, _, _, _ = setup.hist_setup1()
assert np.array_equal(hist.H, wrs), "Array does not match expected"
assert hist.given_count == 0
assert hist.index == 0
assert hist.sim_count == 0
def test_update_history_f_vec():
hist, sim_specs, _, _, _ = setup.hist_setup1()
exp_fs = [0.0] * 10
exp_fvecs = [[0.0, 0.0, 0.0]] * 10
# First update a single point
size = 1
sim_ids = 0 # First row to be filled
sim_ids = np.atleast_1d(sim_ids)
calc_out = np.zeros(size, dtype=sim_specs['out'])
a = np.array([[1, 2, 3], [-1, 1, 4]])
calc_out['f'] = sim_specs['sim_f'](a) #np.linalg.norm
calc_out['fvec'] = sim_specs['sim_f'](a, axis=0) #np.linalg.norm
exp_fs[0] = calc_out['f'][0]
exp_fvecs[0] = calc_out['fvec'][0]
D_recv = {
'calc_out': calc_out,
'persis_info': {},
'libE_info': {
'H_rows': sim_ids
},
'calc_status': WORKER_DONE,
'calc_type': 2
}
hist.update_history_f(D_recv)
assert isclose(exp_fs[0], hist.H['f'][0])
assert np.allclose(exp_fvecs[0], hist.H['fvec'][0])
assert np.all(hist.H['returned'][0:1])
assert np.all(hist.H['returned'][1:10] == False) #Check the rest
assert hist.sim_count == 1
assert hist.given_count == 0 # In real case this would be ahead.....
assert hist.index == 0 # In real case this would be ahead....
# Update two further consecutive points
size = 2
sim_ids = [1, 2] # First row to be filled
sim_ids = np.atleast_1d(sim_ids)
calc_out = np.zeros(size, dtype=sim_specs['out'])
a = np.array([[1, 3, 4], [-1, 2, 4]])
calc_out['f'][0] = sim_specs['sim_f'](a) #np.linalg.norm
exp_fs[1] = calc_out['f'][0]
calc_out['fvec'][0] = sim_specs['sim_f'](a, axis=0) #np.linalg.norm
exp_fvecs[1] = calc_out['fvec'][0]
a = np.array([[2, 4, 4], [-1, 3, 4]])
calc_out['f'][1] = sim_specs['sim_f'](a) #np.linalg.norm
exp_fs[2] = calc_out['f'][1]
calc_out['fvec'][1] = sim_specs['sim_f'](a, axis=0) #np.linalg.norm
exp_fvecs[2] = calc_out['fvec'][1]
D_recv = {
'calc_out': calc_out,
'persis_info': {},
'libE_info': {
'H_rows': sim_ids
},
'calc_status': WORKER_DONE,
'calc_type': 2
}
hist.update_history_f(D_recv)
assert np.allclose(exp_fs, hist.H['f'])
assert np.allclose(exp_fvecs, hist.H['fvec'])
assert np.all(hist.H['returned'][0:3])
assert np.all(hist.H['returned'][3:10] == False) #Check the rest
assert hist.sim_count == 3
assert hist.given_count == 0 # In real case this would be ahead.....
assert hist.index == 0 # In real case this would be ahead....
# Update two further consecutive points but with sub_array fvec componenets
size = 2
sim_ids = [3, 4] # First row to be filled
sim_ids = np.atleast_1d(sim_ids)
calc_out = np.zeros(size, dtype=[('f', float),
('fvec', float, 2)]) # Only two values
a = np.array([[1, 3, 4], [-1, 2, 4]])
calc_out['f'][0] = sim_specs['sim_f'](a) #np.linalg.norm
exp_fs[3] = calc_out['f'][0]
calc_out['fvec'][0][0], calc_out['fvec'][0][1], _ = sim_specs['sim_f'](
a, axis=0) #np.linalg.norm
exp_fvecs[3] = [0.0, 0.0,
0.0] # Point to a new array - so can fill in elements
exp_fvecs[3][:2] = calc_out['fvec'][0] # Change first two values
a = np.array([[2, 4, 4], [-1, 3, 4]])
calc_out['f'][1] = sim_specs['sim_f'](a) #np.linalg.norm
exp_fs[4] = calc_out['f'][1]
calc_out['fvec'][1][0], calc_out['fvec'][1][1], _ = sim_specs['sim_f'](
a, axis=0) #np.linalg.norm
exp_fvecs[4] = [0.0, 0.0,
0.0] # Point to a new array - so can fill in elements
exp_fvecs[4][:2] = calc_out['fvec'][1] # Change first two values
D_recv = {
'calc_out': calc_out,
'persis_info': {},
'libE_info': {
'H_rows': sim_ids
},
'calc_status': WORKER_DONE,
'calc_type': 2
}
hist.update_history_f(D_recv)
assert np.allclose(exp_fs, hist.H['f'])
assert np.allclose(exp_fvecs, hist.H['fvec'])
assert np.all(hist.H['returned'][0:5])
assert np.all(hist.H['returned'][5:10] == False) #Check the rest
assert hist.sim_count == 5
assert hist.given_count == 0 # In real case this would be ahead.....
assert hist.index == 0 # In real case this would be ahead....
