def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size)
return instance
def get_OracleKernelAdaptiveLangevin_instance(D, target_log_pdf):
step_size = 1.
m = 500
N = 5000
Z = sample_banana(N, D, bananicity, V)
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=.001, m=m, D=D)
surrogate.fit(Z)
if False:
param_bounds = {'sigma': [-2, 3]}
bo = BayesOptSearch(surrogate, Z, param_bounds)
best_params = bo.optimize()
surrogate.set_parameters_from_dict(best_params)
if False:
sigma = 1. / gamma_median_heuristic(Z)
surrogate.set_parameters_from_dict({'sigma': sigma})
logger.info("kernel exp family uses %s" % surrogate.get_parameters())
if False:
import matplotlib.pyplot as plt
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(surrogate, Z, Xs, Ys)
plt.show()
instance = OracleKernelAdaptiveLangevin(D, target_log_pdf, surrogate,
step_size)
return instance
D=D,
bananicity=bananicity,
V=V,
num_benchmark_samples=num_benchmark_samples,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
mmd=mmd,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
time_taken=time_taken,
)
if False:
import matplotlib.pyplot as plt
visualize_scatter_2d(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, OracleKernelAdaptiveLangevin):
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(sampler.surrogate, samples, Xs, Ys)
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
plt.title("Drift norms %s" % sampler.get_name())
plt.hist(sampler.forward_drift_norms)
plt.show()
surrogate = KernelExpFiniteGaussian(sigma=sigma, lmbda=lmbda, m=m, D=benchmark_samples.shape[1])
surrogate.fit(benchmark_samples)
fake = empty_class()
def replace_2(x_2d, a, i, j):
a = a.copy()
a[i] = x_2d[0]
a[j] = x_2d[1]
return a
for i in range(benchmark_samples.shape[1]):
for j in range(benchmark_samples.shape[1]):
if i == j:
continue
fake.log_pdf = lambda x_2d: surrogate.log_pdf(replace_2(x_2d, true_mean, i, j))
fake.grad = lambda x_2d: surrogate.grad(replace_2(x_2d, true_mean, i, j))
visualise_fit_2d(fake, benchmark_samples[:, [i, j]],
Xs=np.linspace(benchmark_samples[:, i].min(), benchmark_samples[:, i].max(), 30),
Ys=np.linspace(benchmark_samples[:, j].min(), benchmark_samples[:, j].max(), 30),
)
plt.show()
plt.plot(log2_sigmas, Js_mean, 'b-')
plt.plot(log2_sigmas, Js_mean - 2 * np.sqrt(Js_var[i]), 'b--')
plt.plot(log2_sigmas, Js_mean + 2 * np.sqrt(Js_var[i]), 'b--')
plt.show()
def replace_2(x_2d, a, i, j):
a = a.copy()
a[i] = x_2d[0]
a[j] = x_2d[1]
return a
for i in range(benchmark_samples.shape[1]):
for j in range(benchmark_samples.shape[1]):
if i == j:
continue
fake.log_pdf = lambda x_2d: surrogate.log_pdf(
replace_2(x_2d, true_mean, i, j))
fake.grad = lambda x_2d: surrogate.grad(
replace_2(x_2d, true_mean, i, j))
visualise_fit_2d(
fake,
benchmark_samples[:, [i, j]],
Xs=np.linspace(benchmark_samples[:, i].min(),
benchmark_samples[:, i].max(), 30),
Ys=np.linspace(benchmark_samples[:, j].min(),
benchmark_samples[:, j].max(), 30),
)
plt.show()
plt.plot(log2_sigmas, Js_mean, 'b-')
plt.plot(log2_sigmas, Js_mean - 2 * np.sqrt(Js_var[i]), 'b--')
plt.plot(log2_sigmas, Js_mean + 2 * np.sqrt(Js_var[i]), 'b--')
plt.show()
sampler_name=sampler.get_name(),
D=D,
bananicity=bananicity,
V=V,
num_benchmark_samples=num_benchmark_samples,
population_size=population_size,
num_iter_per_particle=num_iter_per_particle,
mmd=mmd,
rmse_mean=rmse_mean,
rmse_cov=rmse_cov,
time_taken=time_taken,
)
if False:
import matplotlib.pyplot as plt
visualize_scatter_2d(samples)
plt.title("%s" % sampler.get_name())
if isinstance(sampler, OracleKernelAdaptiveLangevin):
Xs = np.linspace(-30, 30, 50)
Ys = np.linspace(-20, 40, 50)
visualise_fit_2d(sampler.surrogate, samples, Xs, Ys)
if isinstance(sampler, StaticLangevin):
plt.figure()
plt.grid(True)
plt.title("Drift norms %s" % sampler.get_name())
plt.hist(sampler.forward_drift_norms)
plt.show()
