def test_HistoryMatching_set_expectations():
"test the set_expectations method of HistoryMatching"
# correct functionality
expectations = (np.array([0.]), np.array([0.1]), np.array([[2.]]))
hm = HistoryMatching()
hm.set_expectations(expectations)
for a, b in zip(hm.expectations, expectations):
assert_allclose(a, b)
# confirm ability to reset expectations
hm.set_expectations(None)
assert hm.expectations == None
# verify derivatives can be None
expectations = (np.array([0.]), np.array([0.1]), None)
hm = HistoryMatching()
hm.set_expectations(expectations)
for a, b in zip(hm.expectations[:-1], expectations[:-1]):
assert_allclose(a, b)
assert hm.expectations[2] is None
# negative variance
expectations = (np.array([0.]), np.array([-0.1]), np.array([[2.]]))
with pytest.raises(AssertionError):
hm.set_expectations(expectations)
# bad values for expectations
expectations = (np.array([0.1]), 1., 1.)
hm = HistoryMatching()
with pytest.raises(TypeError):
hm.set_expectations(expectations)
expectations = (np.array([0.1]), np.array([1., 2.]), 1.)
with pytest.raises(TypeError):
hm.set_expectations(expectations)
expectations = (np.array([1.]), np.array([1., 2.]), None)
with pytest.raises(ValueError):
hm.set_expectations(expectations)
def test_sanity_checks():
"test basic functioning of HistoryMatching"
# Create a gaussian process
x_training = np.array([[0.], [10.], [20.], [30.], [43.], [50.]])
y_training = get_y_simulated_1D(x_training)
gp = GaussianProcess(x_training, y_training)
np.random.seed(47)
gp.learn_hyperparameters()
# Define observation and implausibility threshold
obs = [-0.8, 0.0004]
# Coords to predict
n_rand = 2000
x_predict_min = -3
x_predict_max = 53
x_predict = np.random.rand(n_rand)
x_predict = np.sort(x_predict, axis=0)
x_predict *= (x_predict_max - x_predict_min)
x_predict += x_predict_min
x_predict = x_predict[:, None]
coords = x_predict
expectations = gp.predict(coords)
# Create History Matching Instance
print("---TEST INPUTS---")
print("No Args")
hm = HistoryMatching()
hm.status()
print("Obs Only a - list")
hm = HistoryMatching(obs=obs)
hm.status()
print("Obs only b - single-element list")
hm = HistoryMatching(obs=[3.])
hm.status()
print("Obs only c - single-value")
hm = HistoryMatching(obs=3.)
hm.status()
print("gp Only")
hm = HistoryMatching(gp=gp)
hm.status()
print("Coords only a - 2d ndarray")
hm = HistoryMatching(coords=coords)
hm.status()
print("Coords only b - 1d ndarray")
hm = HistoryMatching(coords=np.random.rand(n_rand))
hm.status()
print("Coords only c - list")
hm = HistoryMatching(coords=[a for a in range(n_rand)])
hm.status()
print("Expectation only")
hm = HistoryMatching(expectations=expectations)
hm.status()
print("Threshold Only")
hm = HistoryMatching(threshold=3.)
hm.status()
print("---TEST ASSIGNMENT---")
print("Assign gp")
hm = HistoryMatching(obs)
hm.status()
hm.set_gp(gp)
hm.status()
print("Assign Obs")
hm = HistoryMatching(gp)
hm.status()
hm.set_obs(obs)
hm.status()
print("Assign Coords")
hm = HistoryMatching()
hm.status()
hm.set_coords(coords)
hm.status()
print("Assign Expectations")
hm = HistoryMatching()
hm.status()
hm.set_expectations(expectations)
hm.status()
print("Assign Threshold")
hm = HistoryMatching()
hm.status()
hm.set_threshold(3.)
hm.status()
print("---TEST IMPLAUSABILIY---")
print("implausibility test a - no vars")
hm = HistoryMatching(obs=obs, gp=gp, coords=coords)
I = hm.get_implausibility()
print("implausibility test b - single value")
hm = HistoryMatching(obs=obs, gp=gp, coords=coords)
I = hm.get_implausibility(7.)