def plot_acceptance_heatmap(Ns, Ds, acc):
masked_array = np.ma.array(acc, mask=np.isnan(acc))
cmap = matplotlib.cm.jet
cmap.set_bad('w', 1.)
plt.pcolor(Ns, Ds, masked_array, cmap=cmap)
plt.yscale("log")
plt.xlim([np.min(Ns), np.max(Ns)])
plt.ylim([np.min(Ds), np.max(Ds)])
plt.xlabel(r"$n$")
plt.ylabel(r"$d$")
plt.colorbar()
def plot_banana_result_mean_N_D(results, D, stat_idx, normalise_by_time=False,
**kwargs):
fun = lambda x:None
Ds, Ns, _ = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 0])
_, _, time_taken_set_up = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 1])
_, _, time_taken_sampling = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, stat_idx])
_, _, avg = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 25)
_, _, lower = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 75)
_, _, upper = gen_sparse_2d_array_from_dict(results, fun)
D_ind = Ds == D
time_total = time_taken_sampling[:,D_ind]# + time_taken_set_up[:,D_ind]
time_total = time_total.ravel()
print_table(time_total, "Time total")
normaliser = time_total if normalise_by_time else 1.0
avg = avg[:, D_ind].ravel()/normaliser
err = np.array([np.abs(avg-lower[:, D_ind].ravel()/normaliser),
np.abs(avg-upper[:, D_ind].ravel()/normaliser)])
plt.plot(Ns, avg, kwargs['color'])
plt.errorbar(Ns, avg, err, color=kwargs['color'])
plt.grid(True)
try:
plt.title(kwargs['title'])
except KeyError:
pass
try:
plt.ylim(kwargs['ylim'])
except KeyError:
pass
try:
plt.xlim(kwargs['xlim'])
except KeyError:
pass
try:
plt.xlabel(kwargs['xlabel'])
except KeyError:
pass
def plot_banana_result_mean_D(results, D, stat_idx, normalise_by_time=False,
**kwargs):
fun = lambda x:None
Ds, _ = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,0])
_, time_taken_set_up = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,1])
_, time_taken_sampling = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,stat_idx])
_, avg = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 25)
_, lower = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 75)
_, upper = gen_sparse_1d_array_from_dict(results, fun)
D_ind = Ds == D
time_total = time_taken_sampling[D_ind] + time_taken_set_up[D_ind]
time_total = time_total.ravel()
print_table(time_total, "Time total")
normaliser = time_total if normalise_by_time else 1.0
avg = avg[D_ind].ravel()/normaliser
xlim=plt.xlim()
plt.plot(xlim, [avg,avg], color=kwargs['color'])
plt.plot(xlim, [lower,lower], '--', color=kwargs['color'])
plt.plot(xlim, [upper,upper], '--', color=kwargs['color'])
plt.grid(True)
try:
plt.title(kwargs['title'])
except KeyError:
pass
try:
plt.ylim(kwargs['ylim'])
except KeyError:
pass
try:
plt.xlabel(kwargs['xlabel'])
except KeyError:
pass
def plot_trajectory_result_mean_fixed_N(fname, N):
results = result_dict_from_file(fname)
# acc_mean, acc_est_mean, vol, vol_est, steps_taken
fun = lambda x: np.mean(x[:, 1])
Ds, Ns, avg_accept_est_mean = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 0])
_, _, avg_accept_mean = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 25)
_, _, avg_accept_est_lower_25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 75)
_, _, avg_accept_est_upper_25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 5)
_, _, avg_accept_est_lower_5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 95)
_, _, avg_accept_est_upper_95 = gen_sparse_2d_array_from_dict(results, fun)
N_ind = np.where(Ns == N)[0][0]
plt.figure()
plt.plot(Ds, avg_accept_mean[:, N_ind], 'r')
plt.plot(Ds, avg_accept_est_mean[:, N_ind], 'b')
plt.plot(Ds, avg_accept_est_lower_25[:, N_ind], 'b-.')
plt.plot(Ds, avg_accept_est_lower_5[:, N_ind], color="grey")
plt.plot(Ds, avg_accept_est_upper_95[:, N_ind], color="grey")
plt.fill_between(Ds,
avg_accept_est_lower_5[:, N_ind],
avg_accept_est_upper_95[:, N_ind],
color="grey",
alpha=.5)
plt.plot(Ds, avg_accept_est_upper_25[:, N_ind], 'b-.')
plt.plot(Ds, avg_accept_mean[:, N_ind], 'r')
plt.xscale("log")
plt.grid(True)
plt.xlim([Ds.min(), Ds.max()])
plt.xlabel(r"$d$")
plt.title(r"n=%d" % N)
ylim = plt.ylim()
plt.ylim([ylim[0], 1.01])
plt.legend(["HMC", "KMC median", "KMC 25\%-75\%", "KMC 5\%-95\%"],
loc="lower left")
fname_base = fname.split(".")[-2]
plt.savefig(fname_base + "_N=%d.eps" % N, axis_inches='tight')
def plot_fixed_D(results, results_hmc, D):
fun = lambda x: None
_, _ = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: np.mean(x[:, 0])
_, time_taken_set_up_hmc = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: np.mean(x[:, 1])
_, time_taken_sampling_hmc = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: np.mean(x[:, 2])
_, accept_hmc = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: np.mean(x[:, 3])
_, avg_quantile_error_hmc = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: np.mean(x[:, 4])
_, avg_ess_hmc = gen_sparse_1d_array_from_dict(results_hmc, fun)
fun = lambda x: None
Ds, Ns, _ = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 0])
_, _, time_taken_set_up = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 1])
_, _, time_taken_sampling = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 2])
_, _, accept = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 2], 25)
_, _, accept_lower25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 2], 75)
_, _, accept_upper75 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 2], 5)
_, _, accept_lower5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 2], 95)
_, _, accept_upper95 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 3])
_, _, avg_quantile_error = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 3], 25)
_, _, avg_quantile_error_lower25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 3], 75)
_, _, avg_quantile_error_upper75 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 3], 5)
_, _, avg_quantile_error_lower5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 3], 95)
_, _, avg_quantile_error_upper95 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 4])
_, _, avg_ess = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 4], 25)
_, _, avg_ess_lower25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 4], 75)
_, _, avg_ess_upper75 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 4], 5)
_, _, avg_ess_lower5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 4], 95)
_, _, avg_ess_upper95 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 5])
_, _, avg_norm_of_mean = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 5], 25)
_, _, avg_norm_of_mean_lower25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 5], 75)
_, _, avg_norm_of_mean_upper75 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 5], 5)
_, _, avg_norm_of_mean_lower5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 5], 95)
_, _, avg_norm_of_mean_upper95 = gen_sparse_2d_array_from_dict(results, fun)
D_ind = Ds == D
time_total = time_taken_sampling[:,D_ind] + time_taken_set_up[:,D_ind]
time_total_hmc = time_taken_sampling_hmc[D_ind] + time_taken_set_up_hmc[D_ind]
plt.figure()
plt.plot([Ns.min(), Ns.max()], [accept_hmc[D_ind], accept_hmc[D_ind]], 'r')
plt.plot(Ns, accept[:, D_ind], 'b')
plt.plot(Ns, accept_lower25[:, D_ind], 'b-.')
plt.plot(Ns, accept_upper75[:, D_ind], 'b-.')
plt.fill_between(Ns,
accept_lower5[:, D_ind].ravel(),
accept_upper95[:, D_ind].ravel(),
color="grey", alpha=.5)
plt.title("Avg. acc. prob.")
plt.ylim([0,1.1])
plt.grid(True)
plt.xlabel(r"$n$")
plt.figure()
plt.plot([Ns.min(), Ns.max()], [avg_quantile_error_hmc[D_ind], avg_quantile_error_hmc[D_ind]], 'r')
plt.plot(Ns, avg_quantile_error[:, D_ind])
plt.plot(Ns, avg_quantile_error_lower25[:, D_ind], 'b-.')
plt.plot(Ns, avg_quantile_error_upper75[:, D_ind], 'b-.')
plt.fill_between(Ns,
avg_quantile_error_lower5[:, D_ind].ravel(),
avg_quantile_error_upper95[:, D_ind].ravel(),
color="grey", alpha=.5)
plt.title("Avg. quantile error")
plt.grid(True)
plt.xlabel(r"$n$")
plt.figure()
# plt.plot([Ns.min(), Ns.max()], np.array([avg_ess_hmc[D_ind], avg_ess_hmc[D_ind]])/time_total_hmc, 'r')
plt.plot(Ns, avg_ess[:, D_ind]/time_total, 'b')
plt.plot(Ns, avg_ess_lower25[:, D_ind]/time_total, 'b-.')
plt.plot(Ns, avg_ess_upper75[:, D_ind]/time_total, 'b-.')
plt.fill_between(Ns,
(avg_ess_lower5[:, D_ind]/time_total).ravel(),
(avg_ess_upper95[:, D_ind]/time_total).ravel(),
color="grey", alpha=.5)
plt.title("Avg. ESS/s")
plt.grid(True)
plt.xlabel(r"$n$")
plt.figure()
# plt.plot([Ns.min(), Ns.max()], [avg_quantile_error_hmc[D_ind], avg_quantile_error_hmc[D_ind]], 'r')
plt.plot(Ns, avg_norm_of_mean[:, D_ind])
plt.plot(Ns, avg_norm_of_mean_lower25[:, D_ind], 'b-.')
plt.plot(Ns, avg_norm_of_mean_upper75[:, D_ind], 'b-.')
plt.fill_between(Ns,
avg_norm_of_mean_lower5[:, D_ind].ravel(),
avg_norm_of_mean_upper95[:, D_ind].ravel(),
color="grey", alpha=.5)
plt.title(r"Avg. $\Vert \mathbb E \mathbf{x} \Vert$")
plt.grid(True)
plt.xlabel(r"$n$")
plt.show()
plot_array(Xs_p, Ys_p, np.exp(M))
plot_2d_trajectory(Ps_est)
plt.gca().xaxis.set_visible(False)
plt.gca().yaxis.set_visible(False)
plt.savefig(fname_base + "_momentum_kmc.eps", axis_inches="tight")
# ylim for Hamiltonian plots
ylim = [np.min([Hs.min(), Hs_est.min()]), np.max([Hs.max(), Hs_est.max()])]
plt.figure()
plt.title("Hamiltonian")
plt.plot(Hs)
plt.ylim(ylim)
plt.gca().xaxis.set_visible(False)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.xlabel("Leap-frog step")
plt.ylabel(r"$H(p,q)$")
plt.savefig(fname_base + "_hamiltonian_hmc.eps", axis_inches="tight")
plt.figure()
plt.title("Hamiltonian")
plt.plot(Hs_est)
plt.ylim(ylim)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().xaxis.set_visible(False)
plt.xlabel("Leap-frog step")
plt.ylabel(r"$H(p,q)$")
plt.savefig(fname_base + "_hamiltonian_kmc.eps", axis_inches="tight")
# ylim for acceptance plots
ylim = [np.exp(np.min([log_acc.min(), log_acc_est.min()])), 1.]
plot_2d_trajectory(Ps_est)
plt.gca().xaxis.set_visible(False)
plt.gca().yaxis.set_visible(False)
plt.tight_layout()
plt.savefig(fname_base + "_momentum_kmc.eps")
# ylim for Hamiltonian plots
ylim = [np.min([Hs.min(), Hs_est.min()]), np.max([Hs.max(), Hs_est.max()])]
plt.figure()
plt.title("Hamiltonian")
plt.plot(Hs)
plt.ylim(ylim)
plt.gca().xaxis.set_visible(False)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.xlabel("Leap-frog step")
plt.ylabel(r"$H(p,q)$")
plt.tight_layout()
plt.savefig(fname_base + "_hamiltonian_hmc.eps")
plt.figure()
plt.title("Hamiltonian")
plt.plot(Hs_est)
plt.ylim(ylim)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
plt.gca().xaxis.set_visible(False)
plt.xlabel("Leap-frog step")
plt.ylabel(r"$H(p,q)$")
plt.tight_layout()
plt.savefig(fname_base + "_hamiltonian_kmc.eps")
for j, N in enumerate(Ns):
logger.info(
"MMD of %d benchmark samples against %d MCMC samples" %
(len(benchmark_samples), N))
samples_so_far = result.samples[warmup:(warmup + N)]
MMDs[i, j] = k.estimateMMD(benchmark_samples, samples_so_far)
med = np.median(MMDs, 0)
lower = np.percentile(MMDs, 20, 0)
upper = np.percentile(MMDs, 80, 0)
plt.plot(Ns, med, "-", color=colors[alg_idx])
plt.plot(Ns, lower, '--', color=colors[alg_idx])
plt.plot(Ns, upper, '--', color=colors[alg_idx])
# err = np.array([np.abs(med-lower),np.abs(med-upper)])
# plt.plot(Ns, med, color=colors[alg_idx])
# plt.errorbar(Ns, med, err, color=colors[alg_idx])
plt.yscale("log")
line1 = Line2D([0, 0], [0, 0], color=colors[0])
line2 = Line2D([0, 0], [0, 0], color=colors[1])
line3 = Line2D([0, 0], [0, 0], color=colors[2])
plt.legend((line1, line2, line3), ["KMC", "KAMH", "RW"])
plt.ylabel(r"MMD from ground truth")
plt.xlabel("Iterations")
plt.grid(True)
plt.savefig("gp_target_results.eps", bbox_inches='tight')
plt.show()