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['s1'] - x_0['s1'])
def distance1(x, x_0):
return np.sqrt((x['s2'] - x_0['s2'])**2)
for distance in [AdaptivePNormDistance(log_file=weights_file),
AdaptiveAggregatedDistance(
[distance0, distance1], 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}
expected = distance.weights
for key, val in expected.items():
if isinstance(val, np.ndarray):
expected[key] = val.tolist()
assert weights == expected
def test_adaptiveaggregateddistance():
abc = MockABC([{'s0': -1, 's1': -1}, {'s0': -1, 's1': 0}])
x_0 = {'s0': 0, 's1': 0}
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)
scale_functions = [span, mean, median]
for scale_function in scale_functions:
distance = AdaptiveAggregatedDistance([distance0, distance1],
scale_function=scale_function)
distance.initialize(0, abc.sample_from_prior, x_0=x_0, total_sims=0)
val = distance(abc.sumstats[0], abc.sumstats[1], t=0)
assert isinstance(val, float)
assert (distance.weights[0] != [1, 1]).any()
def test_adaptiveaggregateddistance_calibration():
abc = MockABC([{'s0': -1, 's1': -1}, {'s0': -1, 's1': 0}])
x_0 = {'s0': 0, 's1': 0}
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)
scale_functions = [span, mean, median]
initial_weights = np.array([2, 3])
for scale_function in scale_functions:
distance = AdaptiveAggregatedDistance(
[distance0, distance1],
scale_function=scale_function,
initial_weights=initial_weights,
)
distance.initialize(0, abc.sample_from_prior, x_0=x_0, total_sims=0)
val = distance(abc.sumstats[0], abc.sumstats[1], t=0)
assert isinstance(val, float)
assert (distance.weights[0] == initial_weights).all()
distance.update(1, abc.sample_from_prior, total_sims=0)
assert (distance.weights[1] != distance.weights[0]).all()