def add_observed_node(self, node, out_message, K):
filename=str(out_message)+ "->" + str(node) + "_obs_kernel" + self.filename_suffix + ".txt"
self.lines[-1]+=str(out_message) + " " + filename + "\t"
if self.pathname is not "":
filename=self.pathname + os.sep + filename
savetxt(filename, K)
def add_edge(self, node, out_message, matrix_name, K):
filename=str(out_message)+ "->" + str(node) + "_" + matrix_name + self.filename_suffix + ".txt"
self.lines[-1]+=matrix_name + " " + filename + " "
if self.pathname is not "":
filename=self.pathname + os.sep + filename
savetxt(filename, K)
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin and dimension are the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
dim = self.experiments[
0].mcmc_chain.mcmc_sampler.distribution.dimension
# collect all thinned samples of all chains in here
merged_samples = zeros((0, dim))
for i in range(len(self.experiments)):
lines.append("Processing chain %d" % i)
# discard samples before burn in
lines.append("Discarding burnin of %d" % burnin)
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# thin out by factor and store thinned samples
indices = arange(0, len(burned_in), self.thinning_factor)
lines.append("Thinning by factor of %d, giving %d samples" \
% (self.thinning_factor, len(indices)))
thinned = burned_in[indices, :]
merged_samples = vstack((merged_samples, thinned))
# dump merged samples to disc
fname = self.experiments[0].name + "_merged_samples.txt"
lines.append("Storing %d samples in file %s" %
(len(merged_samples), fname))
savetxt(fname, merged_samples)
return lines
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin and dimension are the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
dim = self.experiments[0].mcmc_chain.mcmc_sampler.distribution.dimension
# collect all thinned samples of all chains in here
merged_samples = zeros((0, dim))
for i in range(len(self.experiments)):
lines.append("Processing chain %d" % i)
# discard samples before burn in
lines.append("Discarding burnin of %d" % burnin)
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# thin out by factor and store thinned samples
indices = arange(0, len(burned_in), self.thinning_factor)
lines.append("Thinning by factor of %d, giving %d samples" \
% (self.thinning_factor, len(indices)))
thinned = burned_in[indices, :]
merged_samples = vstack((merged_samples, thinned))
# dump merged samples to disc
fname = self.experiments[0].name + "_merged_samples.txt"
lines.append("Storing %d samples in file %s" % (len(merged_samples), fname))
savetxt(fname, merged_samples)
return lines
print "precomputing systems for messages from observed nodes"
graphlab_lines.lines.append(os.linesep + "# edges with observed targets")
for node, observation in observations.items():
for out_message in graph[node]:
edge=(out_message, node)
graphlab_lines.new_edge_observed_target(node, out_message)
data_source=data[edge][0]
data_target=data[edge][1]
Ks_chol, Is, Rs, Ws=incomplete_cholesky(data_source, kernel, eta)
Kt_chol, It, Rt, Wt=incomplete_cholesky(data_target, kernel, eta)
Qs,Rs,Ps=qr(Ws.dot(Ws.T)+Ks_chol+eye(shape(Ks_chol)[0])*reg_lambda, pivoting=True)
Qt,Rt,Pt=qr(Wt.dot(Wt.T)+Kt_chol+eye(shape(Kt_chol)[0])*reg_lambda, pivoting=True)
savetxt(graphlab_lines.add_edge(node, out_message,"Q_s"), Qs)
savetxt(graphlab_lines.add_edge(node, out_message,"R_s"), Rs)
savetxt(graphlab_lines.add_edge(node, out_message,"P_s"), Ps)
savetxt(graphlab_lines.add_edge(node, out_message,"Q_t"), Qt)
savetxt(graphlab_lines.add_edge(node, out_message,"R_t"), Rt)
savetxt(graphlab_lines.add_edge(node, out_message,"P_t"), Pt)
savetxt(graphlab_lines.add_edge(node, out_message,"W"), Ws.dot(Wt.T))
print "precomputing systems for messages from non-observed nodes"
graphlab_lines.lines.append(os.linesep + "# edges with non-observed targets")
for edge in edges:
# exclude edges which involve observed nodes
is_edge_target_observed=len(Set(observations.keys()).intersection(Set(edge)))>0
if not is_edge_target_observed:
def write_input_data_to_disk(full_path_id, data, times):
with open(full_path_id, 'wb') as output_data:
for i, frame in enumerate(data):
separator = '@' + str(times[i] - times[0])
savetxt(output_data, frame, delimiter=',', fmt='%u')
savetxt(output_data, [separator], fmt='%s')
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin is the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
quantiles = zeros((len(self.experiments), len(self.ref_quantiles)))
norm_of_means = zeros(len(self.experiments))
acceptance_rates = zeros(len(self.experiments))
# ess_0 = zeros(len(self.experiments))
# ess_1 = zeros(len(self.experiments))
# ess_minima = zeros(len(self.experiments))
# ess_medians = zeros(len(self.experiments))
# ess_maxima = zeros(len(self.experiments))
times = zeros(len(self.experiments))
for i in range(len(self.experiments)):
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# use precomputed quantiles if they match with the provided ones
if hasattr(self.experiments[i], "ref_quantiles") and \
hasattr(self.experiments[i], "quantiles") and \
allclose(self.ref_quantiles, self.experiments[i].ref_quantiles):
quantiles[i, :] = self.experiments[i].quantiles
else:
try:
quantiles[i, :] = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(\
burned_in, self.ref_quantiles)
except NotImplementedError:
print "skipping quantile computations, distribution does", \
"not support it."
# quantiles should be about average error rather than average quantile
quantiles[i, :] = abs(quantiles[i, :] - self.ref_quantiles)
dim = self.experiments[
i].mcmc_chain.mcmc_sampler.distribution.dimension
norm_of_means[i] = norm(mean(burned_in, 0))
acceptance_rates[i] = mean(
self.experiments[i].mcmc_chain.accepteds[burnin:])
# dump burned in samples to disc
# sample_filename=self.experiments[0].experiment_dir + self.experiments[0].name + "_burned_in.txt"
# savetxt(sample_filename, burned_in)
# store minimum ess for every experiment
#ess_per_covariate = asarray([RCodaTools.ess_coda(burned_in[:, cov_idx]) for cov_idx in range(dim)])
# ess_per_covariate = asarray([0 for _ in range(dim)])
# ess_0=ess_per_covariate[0]
# ess_1=ess_per_covariate[1]
# ess_minima[i] = min(ess_per_covariate)
# ess_medians[i] = median(ess_per_covariate)
# ess_maxima[i] = max(ess_per_covariate)
# save chain time needed
ellapsed = self.experiments[i].mcmc_chain.mcmc_outputs[0].times
times[i] = int(round(sum(ellapsed)))
mean_quantiles = mean(quantiles, 0)
std_quantiles = std(quantiles, 0)
sqrt_num_trials = sqrt(len(self.experiments))
# print median kernel width sigma
#sigma=GaussianKernel.get_sigma_median_heuristic(burned_in.T)
#lines.append("median kernel sigma: "+str(sigma))
lines.append("quantiles:")
for i in range(len(self.ref_quantiles)):
lines.append(
str(mean_quantiles[i]) + " +- " +
str(std_quantiles[i] / sqrt_num_trials))
lines.append("norm of means:")
lines.append(
str(mean(norm_of_means)) + " +- " +
str(std(norm_of_means) / sqrt_num_trials))
lines.append("acceptance rate:")
lines.append(
str(mean(acceptance_rates)) + " +- " +
str(std(acceptance_rates) / sqrt_num_trials))
# lines.append("ess dimension 0:")
# lines.append(str(mean(ess_0)) + " +- " + str(std(ess_0)/sqrt_num_trials))
#
# lines.append("ess dimension 1:")
# lines.append(str(mean(ess_1)) + " +- " + str(std(ess_1)/sqrt_num_trials))
#
# lines.append("minimum ess:")
# lines.append(str(mean(ess_minima)) + " +- " + str(std(ess_minima)/sqrt_num_trials))
#
# lines.append("median ess:")
# lines.append(str(mean(ess_medians)) + " +- " + str(std(ess_medians)/sqrt_num_trials))
#
# lines.append("maximum ess:")
# lines.append(str(mean(ess_maxima)) + " +- " + str(std(ess_maxima)/sqrt_num_trials))
lines.append("times:")
lines.append(
str(mean(times)) + " +- " + str(std(times) / sqrt_num_trials))
# mean as a function of iterations, normalised by time
step = round(
(self.experiments[0].mcmc_chain.mcmc_params.num_iterations -
burnin) / 5)
iterations = arange(
self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin,
step=step)
running_means = zeros(len(iterations))
running_errors = zeros(len(iterations))
for i in arange(len(iterations)):
# norm of mean of chain up
norm_of_means_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(
burnin + iterations[i] + 1 + step), :]
norm_of_means_yet[j] = norm(mean(samples_yet, 0))
running_means[i] = mean(norm_of_means_yet)
error_level = 1.96
running_errors[i] = error_level * std(norm_of_means_yet) / sqrt(
len(norm_of_means_yet))
ioff()
figure()
plot(iterations, running_means * mean(times))
fill_between(iterations, (running_means - running_errors)*mean(times), \
(running_means + running_errors)*mean(times), hold=True, color="gray")
# make sure path to save exists
try:
os.makedirs(self.experiments[0].experiment_dir)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
savefig(self.experiments[0].experiment_dir + self.experiments[0].name +
"_running_mean.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_Y.txt", \
running_means*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_errors.txt", \
running_errors*mean(times))
# dont produce quantile convergence plots here for now
"""# quantile convergence of a single one
desired_quantile=0.5
running_quantiles=zeros(len(iterations))
running_quantile_errors=zeros(len(iterations))
for i in arange(len(iterations)):
quantiles_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
# just compute one quantile for now
quantiles_yet[j]=self.experiments[j].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(samples_yet, \
array([desired_quantile]))
quantiles_yet[j]=abs(quantiles_yet[j]-desired_quantile)
running_quantiles[i] = mean(quantiles_yet)
error_level = 1.96
running_quantile_errors[i] = error_level * std(quantiles_yet) / sqrt(len(quantiles_yet))
ioff()
figure()
plot(iterations, running_quantiles*mean(times))
fill_between(iterations, (running_quantiles - running_quantile_errors)*mean(times), \
(running_quantiles + running_quantile_errors)*mean(times), hold=True, color="gray")
plot([iterations.min(),iterations.max()], [desired_quantile*mean(times) for _ in range(2)])
title(str(desired_quantile)+"-quantile convergence")
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_Y.txt", \
running_quantiles*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_errors.txt", \
running_quantile_errors*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_reference.txt", \
[desired_quantile*mean(times)])
"""
# add latex table line
# latex_lines = []
# latex_lines.append("Sampler & Acceptance & ESS2 & Norm(mean) & ")
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('%.1f' % self.ref_quantiles[i] + "-quantile")
# if i < len(self.ref_quantiles) - 1:
# latex_lines.append(" & ")
# latex_lines.append("\\\\")
# lines.append("".join(latex_lines))
#
# latex_lines = []
# latex_lines.append(self.experiments[0].mcmc_chain.mcmc_sampler.__class__.__name__)
# latex_lines.append('$%.3f' % mean(acceptance_rates) + " \pm " + '%.3f$' % (std(acceptance_rates)/sqrt_num_trials))
# latex_lines.append('$%.3f' % mean(norm_of_means) + " \pm " + '%.3f$' % (std(norm_of_means)/sqrt_num_trials))
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('$%.3f' % mean_quantiles[i] + " \pm " + '%.3f$' % (std_quantiles[i]/sqrt_num_trials))
#
#
# lines.append(" & ".join(latex_lines) + "\\\\")
return lines
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin is the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
quantiles = zeros((len(self.experiments), len(self.ref_quantiles)))
norm_of_means = zeros(len(self.experiments))
acceptance_rates = zeros(len(self.experiments))
# ess_0 = zeros(len(self.experiments))
# ess_1 = zeros(len(self.experiments))
# ess_minima = zeros(len(self.experiments))
# ess_medians = zeros(len(self.experiments))
# ess_maxima = zeros(len(self.experiments))
times = zeros(len(self.experiments))
for i in range(len(self.experiments)):
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# use precomputed quantiles if they match with the provided ones
if hasattr(self.experiments[i], "ref_quantiles") and \
hasattr(self.experiments[i], "quantiles") and \
allclose(self.ref_quantiles, self.experiments[i].ref_quantiles):
quantiles[i, :] = self.experiments[i].quantiles
else:
try:
quantiles[i, :] = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(\
burned_in, self.ref_quantiles)
except NotImplementedError:
print "skipping quantile computations, distribution does", \
"not support it."
# quantiles should be about average error rather than average quantile
quantiles[i,:]=abs(quantiles[i,:]-self.ref_quantiles)
dim = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.dimension
norm_of_means[i] = norm(mean(burned_in, 0))
acceptance_rates[i] = mean(self.experiments[i].mcmc_chain.accepteds[burnin:])
# dump burned in samples to disc
# sample_filename=self.experiments[0].experiment_dir + self.experiments[0].name + "_burned_in.txt"
# savetxt(sample_filename, burned_in)
# store minimum ess for every experiment
#ess_per_covariate = asarray([RCodaTools.ess_coda(burned_in[:, cov_idx]) for cov_idx in range(dim)])
# ess_per_covariate = asarray([0 for _ in range(dim)])
# ess_0=ess_per_covariate[0]
# ess_1=ess_per_covariate[1]
# ess_minima[i] = min(ess_per_covariate)
# ess_medians[i] = median(ess_per_covariate)
# ess_maxima[i] = max(ess_per_covariate)
# save chain time needed
ellapsed = self.experiments[i].mcmc_chain.mcmc_outputs[0].times
times[i] = int(round(sum(ellapsed)))
mean_quantiles = mean(quantiles, 0)
std_quantiles = std(quantiles, 0)
sqrt_num_trials=sqrt(len(self.experiments))
# print median kernel width sigma
#sigma=GaussianKernel.get_sigma_median_heuristic(burned_in.T)
#lines.append("median kernel sigma: "+str(sigma))
lines.append("quantiles:")
for i in range(len(self.ref_quantiles)):
lines.append(str(mean_quantiles[i]) + " +- " + str(std_quantiles[i]/sqrt_num_trials))
lines.append("norm of means:")
lines.append(str(mean(norm_of_means)) + " +- " + str(std(norm_of_means)/sqrt_num_trials))
lines.append("acceptance rate:")
lines.append(str(mean(acceptance_rates)) + " +- " + str(std(acceptance_rates)/sqrt_num_trials))
# lines.append("ess dimension 0:")
# lines.append(str(mean(ess_0)) + " +- " + str(std(ess_0)/sqrt_num_trials))
#
# lines.append("ess dimension 1:")
# lines.append(str(mean(ess_1)) + " +- " + str(std(ess_1)/sqrt_num_trials))
#
# lines.append("minimum ess:")
# lines.append(str(mean(ess_minima)) + " +- " + str(std(ess_minima)/sqrt_num_trials))
#
# lines.append("median ess:")
# lines.append(str(mean(ess_medians)) + " +- " + str(std(ess_medians)/sqrt_num_trials))
#
# lines.append("maximum ess:")
# lines.append(str(mean(ess_maxima)) + " +- " + str(std(ess_maxima)/sqrt_num_trials))
lines.append("times:")
lines.append(str(mean(times)) + " +- " + str(std(times)/sqrt_num_trials))
# mean as a function of iterations, normalised by time
step = round((self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin)/5)
iterations = arange(self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin, step=step)
running_means = zeros(len(iterations))
running_errors = zeros(len(iterations))
for i in arange(len(iterations)):
# norm of mean of chain up
norm_of_means_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
norm_of_means_yet[j] = norm(mean(samples_yet, 0))
running_means[i] = mean(norm_of_means_yet)
error_level = 1.96
running_errors[i] = error_level * std(norm_of_means_yet) / sqrt(len(norm_of_means_yet))
ioff()
figure()
plot(iterations, running_means*mean(times))
fill_between(iterations, (running_means - running_errors)*mean(times), \
(running_means + running_errors)*mean(times), hold=True, color="gray")
# make sure path to save exists
try:
os.makedirs(self.experiments[0].experiment_dir)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_Y.txt", \
running_means*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_errors.txt", \
running_errors*mean(times))
# dont produce quantile convergence plots here for now
"""# quantile convergence of a single one
desired_quantile=0.5
running_quantiles=zeros(len(iterations))
running_quantile_errors=zeros(len(iterations))
for i in arange(len(iterations)):
quantiles_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
# just compute one quantile for now
quantiles_yet[j]=self.experiments[j].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(samples_yet, \
array([desired_quantile]))
quantiles_yet[j]=abs(quantiles_yet[j]-desired_quantile)
running_quantiles[i] = mean(quantiles_yet)
error_level = 1.96
running_quantile_errors[i] = error_level * std(quantiles_yet) / sqrt(len(quantiles_yet))
ioff()
figure()
plot(iterations, running_quantiles*mean(times))
fill_between(iterations, (running_quantiles - running_quantile_errors)*mean(times), \
(running_quantiles + running_quantile_errors)*mean(times), hold=True, color="gray")
plot([iterations.min(),iterations.max()], [desired_quantile*mean(times) for _ in range(2)])
title(str(desired_quantile)+"-quantile convergence")
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_Y.txt", \
running_quantiles*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_errors.txt", \
running_quantile_errors*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_reference.txt", \
[desired_quantile*mean(times)])
"""
# add latex table line
# latex_lines = []
# latex_lines.append("Sampler & Acceptance & ESS2 & Norm(mean) & ")
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('%.1f' % self.ref_quantiles[i] + "-quantile")
# if i < len(self.ref_quantiles) - 1:
# latex_lines.append(" & ")
# latex_lines.append("\\\\")
# lines.append("".join(latex_lines))
#
# latex_lines = []
# latex_lines.append(self.experiments[0].mcmc_chain.mcmc_sampler.__class__.__name__)
# latex_lines.append('$%.3f' % mean(acceptance_rates) + " \pm " + '%.3f$' % (std(acceptance_rates)/sqrt_num_trials))
# latex_lines.append('$%.3f' % mean(norm_of_means) + " \pm " + '%.3f$' % (std(norm_of_means)/sqrt_num_trials))
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('$%.3f' % mean_quantiles[i] + " \pm " + '%.3f$' % (std_quantiles[i]/sqrt_num_trials))
#
#
# lines.append(" & ".join(latex_lines) + "\\\\")
return lines