def test_store_weights():
"""Test whether storing distance weights works."""
abc = MockABC([{
's1': -1,
's2': -1,
's3': -1
}, {
's1': -1,
's2': 0,
's3': 1
}])
x_0 = {'s1': 0, 's2': 0, 's3': 1}
weights_file = tempfile.mkstemp(suffix=".json")[1]
print(weights_file)
def distance0(x, x_0):
return abs(x['s0'] - x_0['s0'])
def distance1(x, x_0):
return np.sqrt((x['s1'] - x_0['s1'])**2)
for distance in [
AdaptivePNormDistance(log_file=weights_file),
AdaptiveAggregatedDistance([distance0, distance1],
log_file=weights_file)
]:
distance = AdaptivePNormDistance(log_file=weights_file)
distance.initialize(0, abc.sample_from_prior, x_0=x_0)
distance.update(1, abc.sample_from_prior)
distance.update(2, abc.sample_from_prior)
weights = load_dict_from_json(weights_file)
assert set(weights.keys()) == {0, 1, 2}
assert weights == distance.weights
def test_adaptivepnormdistance_initial_weights():
abc = MockABC([{
's1': -1,
's2': -1,
's3': -1
}, {
's1': -1,
's2': 0,
's3': 1
}])
x_0 = {'s1': 0, 's2': 0, 's3': 1}
# first test that for PNormDistance, the weights stay constant
initial_weights = {'s1': 1, 's2': 2, 's3': 3}
dist_f = AdaptivePNormDistance(p=2, initial_scale_weights=initial_weights)
dist_f.initialize(0, abc.sample_from_prior, x_0=x_0, total_sims=0)
assert (dist_f.scale_weights[0] == np.array([1, 2, 3])).all()
# call distance function
d = dist_f(abc.sumstats[0], abc.sumstats[1], t=0)
expected = pow(sum([(2 * 1)**2, (3 * 2)**2]), 1 / 2)
assert expected == d
# check updating works
dist_f.update(1, abc.sample_from_prior, total_sims=0)
assert (dist_f.scale_weights[1] != dist_f.scale_weights[0]).any()
def test_adaptivepnormdistance_initial_weights():
abc = MockABC([{
's1': -1,
's2': -1,
's3': -1
}, {
's1': -1,
's2': 0,
's3': 1
}])
x_0 = {'s1': 0, 's2': 0, 's3': 1}
# first test that for PNormDistance, the weights stay constant
initial_weights = {'s1': 1, 's2': 2, 's3': 3}
dist_f = AdaptivePNormDistance(initial_weights=initial_weights)
dist_f.initialize(0, abc.sample_from_prior, x_0=x_0)
assert dist_f.weights[0] == initial_weights
# call distance function
d = dist_f(abc.sample_from_prior()[0], abc.sample_from_prior()[1], t=0)
expected = pow(sum([(2 * 1)**2, (3 * 2)**2]), 1 / 2)
assert expected == d
# check updating works
dist_f.update(1, abc.sample_from_prior)
assert dist_f.weights[1] != dist_f.weights[0]