def run(casename):
fh = Dataset("../data/" + casename + ".nc", mode="r")
lons = fh.variables["lon"][:]
lats = fh.variables["lat"][:]
depths = fh.variables["elevation"][:]
# Rescale data and flip depths to have anomalies as minima
lons = MinMaxScaler().fit_transform(lons.reshape(-1, 1))
lats = MinMaxScaler().fit_transform(lats.reshape(-1, 1))
tmp = StandardScaler().fit_transform(depths.reshape(-1, 1))
depths = -tmp.reshape(depths.shape)
if casename in ["cuba", "challenger", "izu", "izu2", "izu3"]:
depths = -depths
domain = [[0, 1], [0, 1]]
inputs = UniformInputs(domain)
inputs = GaussianInputs(domain, [0.5, 0.5], [0.01, 0.01])
noise_var = 0.0
interpolant = interpolate.interp2d(lons, lats, depths, kind="cubic")
my_map = BlackBox(map_def, args=(interpolant, ), noise_var=noise_var)
idx_max = np.unravel_index(depths.argmin(), depths.shape)
true_xmin = [lons[idx_max[1]], lats[idx_max[0]]]
true_ymin = np.min(depths)
record_time = np.linspace(0, 15, 226)
n_trials = 50
n_jobs = 40
pts = inputs.draw_samples(n_samples=int(1e5), sample_method="uni")
yy = my_map.evaluate(pts, parallel=True, include_noise=False)
y_list = [yy] * len(record_time)
pt_list = [custom_KDE(yy, weights=inputs.pdf(pts))] * len(record_time)
true_ymin_list = [true_ymin] * len(record_time)
true_xmin_list = [true_xmin] * len(record_time)
metric = [(rmse, dict(pts=pts, y_list=y_list, t_list=record_time)),
(mll, dict(pts=pts, y_list=y_list, t_list=record_time)),
(log_pdf, dict(pts=pts, pt_list=pt_list, t_list=record_time)),
(distmin_model,
dict(true_xmin_list=true_xmin_list, t_list=record_time)),
(regret_tmap,
dict(true_ymin_list=true_ymin_list,
tmap=my_map,
t_list=record_time))]
X_pose = (0, 0, np.pi / 4)
planner = PathPlanner(inputs.domain, look_ahead=0.2, turning_radius=0.02)
acq_list = ["US_IW", "US_LW", "IVR_IW", "IVR_LW"]
for acq in acq_list:
print("Benchmarking " + acq)
b = BenchmarkerPath(my_map, acq, planner, X_pose, record_time, inputs,
metric)
result = b.run_benchmark(n_trials,
n_jobs=n_jobs,
filename=casename + "_" + acq)
def test_blackbox_shape_2D():
n_init = 10
domain = [[-1., 2.], [-1., 2.]]
inputs = UniformInputs(domain)
X = inputs.draw_samples(n_init, "lhs")
Ys = BlackBox(map_def2D).evaluate(X)
Yp = BlackBox(map_def2D).evaluate(X, parallel=True)
np.testing.assert_allclose(Ys, Yp)
def __init__(self,
function,
domain,
ymin,
xmin,
args={},
kwargs={},
noise_var=0.0,
rescale_X=False):
self.ymin = ymin
self.bounds = domain
self.function = function
if rescale_X:
self.xmin = self.rescale_X(xmin)
domain = [[0, 1]] * len(domain)
self.my_map = BlackBox(self.function_sc,
args=args,
kwargs=kwargs,
noise_var=noise_var)
else:
self.xmin = xmin
self.my_map = BlackBox(self.function,
args=args,
kwargs=kwargs,
noise_var=noise_var)
self.inputs = UniformInputs(domain)
def main():
ndim = 10
x2, y2 = 1, 0.3
x = np.linspace(0, x2, ndim + 2)
x_pts = x[1:-1]
xc, yc = cycloid(x2, y2, N=200)
tc = travel_time(xc, yc) # Best ever
ff = interpolate.interp1d(xc, yc)
noise_var = 1e-2
my_map = BlackBox(map_def, args=(x, y2, tc), noise_var=noise_var)
domain = [[0.05, 2 * y2] for n in np.arange(0, ndim)]
inputs = UniformInputs(domain)
prefix = "tclog_noise2_"
n_init = 10
n_iter = 300
n_trials = 40
n_jobs = 40
true_ymin = 0.0
metric = [("regret_tmap", dict(true_ymin=true_ymin, tmap=my_map)),
("regret_obs", dict(true_ymin=true_ymin))]
acq_list = ["EI", "PI", "LCB", "LCB_LW", "LCB_LWraw", "IVR_BO", "IVR_LWBO"]
for acq in acq_list:
print("Benchmarking " + acq)
b = Benchmarker(my_map, acq, n_init, n_iter, inputs, metric=metric)
result = b.run_benchmark(n_trials,
n_jobs=n_jobs,
filename=prefix + acq)
def main():
np.random.seed(2)
n_init = 10
domain = [[-1., 2.]]
inputs = UniformInputs(domain)
X = inputs.draw_samples(n_init, "lhs")
Ys = BlackBox(map_def1D).evaluate(X)
Yp = BlackBox(map_def1D).evaluate(X, parallel=True)
OptimalDesign(X, Ys, BlackBox(map_def1D), inputs)
OptimalDesign(X, Yp, BlackBox(map_def1D), inputs)
print(X, Ys, Yp)
domain = [[-1., 2.], [-1., 2.]]
inputs = UniformInputs(domain)
X = inputs.draw_samples(n_init, "lhs")
Ys = BlackBox(map_def2D).evaluate(X)
Yp = BlackBox(map_def2D).evaluate(X, parallel=True)
OptimalDesign(X, Ys, BlackBox(map_def2D), inputs)
OptimalDesign(X, Yp, BlackBox(map_def2D), inputs)
print(X, Ys, Yp)