def test_HistoryMatching_get_RO():
"test the get_RO method of HistoryMatching"
# correct functionality
hm = HistoryMatching(obs=[1., 1.],
expectations=(np.array([2., 10.]), np.array([0., 0.]),
np.array([[1., 2.]])))
hm.get_implausibility()
RO = hm.get_RO()
hm = HistoryMatching(obs=[1., 0.],
expectations=(np.array([2., 10.]), np.array([0., 0.]),
np.array([[1., 2.]])))
RO = hm.get_RO(1.)
assert RO == [1]
def demo_1D():
# 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 = fit_GP_MAP(gp)
# Define observation
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
# Compute GPE expectations
expectations = gp.predict(coords)
# Compute Implausbility
hm = HistoryMatching(obs=obs, expectations=expectations)
I = hm.get_implausibility()
NROY = hm.get_NROY()
RO = hm.get_RO()
print("Fraction of points ruled out {:6}".format(str(float(len(RO))/float(n_rand))))
# Plotting
if makeplots:
fig, axs = plt.subplots(2, 1, sharex=True)
fig.subplots_adjust(hspace=0)
x_hist_plot = [min(x_predict)[0], max(x_predict)[0]]
y_hist_plot = [obs[0], obs[0]]
y_hist_err = 3*np.sqrt(obs[1])
y_hist_up = [val + y_hist_err for val in y_hist_plot]
y_hist_dn = [val - y_hist_err for val in y_hist_plot]
axs[0].plot( # Horizontal line at value of y_obs
x_hist_plot,
y_hist_plot,
color = 'black',
label = 'observation'
)
axs[0].fill_between( # Error bounds on y_obs
x_hist_plot,
y_hist_dn,
y_hist_up,
color='black',
alpha=0.25
)
axs[0].plot( # Simulator output
coords,
get_y_simulated_1D(coords),
color = 'black',
label = 'simulator'
)
axs[0].scatter( # Training Data
x_training,
y_training,
marker = '.',
color = 'black',
label = 'Training Data',
s = 100
)
axs[0].plot( # GPE expectation
coords,
expectations[0],
color = 'red',
label = 'GPE'
)
axs[0].fill_between( # GPE uncertainty
coords[:,0],
expectations[0] - 3*np.sqrt(expectations[1]),
expectations[0] + 3*np.sqrt(expectations[1]),
color = 'red',
alpha = 0.5
)
axs[1].scatter( # Implausibility
coords,
I,
marker='.',
color='black'
)
axs[1].scatter(
coords[NROY],
I[NROY],
marker='.',
color='green'
)
axs[1].plot( # implausibility Threshold
x_hist_plot,
[3,3],
color = 'green',
label = 'implausibility threshold'
)
axs[0].set(
ylabel='Model Output f(x)'
)
axs[1].set(
xlabel='Imput Parameter x',
ylabel='Implausibility I(x)',
ylim=(-1, 21)
)
plt.savefig('histmatch_1D.png', bbox_inches = 'tight')
def demo_2D():
# Create a Gaussian Process
x_training = np.array([
[0., 0.],
[1.5, 1.5],
[3., 3.],
[0., 1.5],
[1.5, 0.],
[0., 3.],
[3., 0.],
[3., 1.5],
[1.5, 3.]
])
y_training = get_y_simulated_2D(x_training)
gp = GaussianProcess(x_training, y_training)
np.random.seed(47)
gp = fit_GP_MAP(gp)
# Define observation
obs = [0.1, 0.0004]
# Coords to predict
n_rand = 2000
a_predict_min = 0
a_predict_max = np.pi
b_predict_min = a_predict_min
b_predict_max = a_predict_max
a_predict = np.random.rand(n_rand) * (a_predict_max - a_predict_min) + a_predict_min
b_predict = np.random.rand(n_rand) * (b_predict_max - b_predict_min) + b_predict_min
x_predict = np.concatenate((a_predict[:,None], b_predict[:,None]), axis=1)
x_predict = np.concatenate((x_predict, x_training), axis=0)
coords = x_predict
# Compute GPE expectations
expectations = gp.predict(coords)
# Compute Implausbility
hm = HistoryMatching(obs=obs, expectations=expectations)
I = hm.get_implausibility()
NROY = hm.get_NROY()
RO = hm.get_RO()
print("Fraction of points ruled out {:6}".format(str(float(len(RO))/float(n_rand))))
# Plotting
if makeplots:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Axes3D.scatter( # Training Data
ax,
x_training[:,0],
x_training[:,1],
y_training,
color='black',
marker = '.',
s = 100
)
#Axes3D.scatter( # GPE prediction
# ax,
# coords[:,0],
# coords[:,1],
# expectations[0],
# color='red',
# marker='.',
# s=2
#)
Axes3D.scatter( # GPE prediction uncertainty
ax,
coords[:,0][RO],
coords[:,1][RO],
expectations[0][RO] + 3*np.sqrt(expectations[1][RO]),
color='red',
marker='.',
s=1
)
Axes3D.scatter(
ax,
coords[:,0][RO],
coords[:,1][RO],
expectations[0][RO] - 3*np.sqrt(expectations[1][RO]),
color='red',
marker='.',
s=1
)
Axes3D.scatter(
ax,
coords[:,0][NROY],
coords[:,1][NROY],
expectations[0][NROY] + 3*np.sqrt(expectations[1][NROY]),
color='green',
marker='.',
s=1
)
Axes3D.scatter(
ax,
coords[:,0][NROY],
coords[:,1][NROY],
expectations[0][NROY] - 3*np.sqrt(expectations[1][NROY]),
color='green',
marker='.',
s=1
)
Axes3D.set(
ax,
xlabel = 'Input parameter a',
ylabel = 'Input parameter b',
zlabel = 'Model output f(a, b)'
)
plt.savefig('histmatch_2D.png', bbox_inches = "tight")