history_thin=1,
model_name='earm_smallest_dreamzs_5chain2',
verbose=True)
# Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'earm_smallest_dreamzs_5chain_sampled_params_chain_' + str(chain) +
'_' + str(total_iterations), sampled_params[chain])
np.save(
'earm_smallest_dreamzs_5chain_logps_chain_' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
#Check convergence and continue sampling if not converged
GR = Gelman_Rubin(sampled_params)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(
'earm_smallest_dreamzs_5chain_GelmanRubin_iteration_' +
str(total_iterations) + '.txt', GR)
old_samples = sampled_params
if np.any(GR > 1.2):
starts = [sampled_params[chain][-1, :] for chain in range(nchains)]
while not converged:
total_iterations += niterations
sampled_params, log_ps = run_dream(
parameters=sampled_parameter_names,
likelihood=likelihood,
niterations=niterations,
nchains=nchains,
def DREAM_fit(model, priors_list, posterior, start_params,
sampled_param_names, niterations, nchains, sim_name,
save_dir, custom_params={}, GR_cutoff=1.2, iteration_cutoff=1E7,
verbose=True, plot_posteriors=True):
"""
The DREAM fitting algorithm as implemented in run_dream(), plus decorations
for saving run parameters, checking convergence, and post fitting analysis.
"""
converged = False
total_iterations = niterations
np.save(save_dir + os.sep + 'param_names.npy', sampled_param_names)
with open(save_dir + os.sep + 'init_params.pkl', 'wb') as f:
pickle.dump(dict(model.parameters), f)
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(priors_list, posterior,
start=start_params,
niterations=niterations,
nchains=nchains,
multitry=True, parallel=True,
gamma_levels=4, adapt_gamma=True,
history_thin=1, model_name=sim_name,
verbose=verbose)
# Save sampling output (sampled param values and their corresponding logps)
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name+str(chain) + '_' + str(total_iterations)), sampled_params[chain])
np.save(os.path.join(save_dir, sim_name+str(chain) + '_logPs_' + str(total_iterations)), log_ps[chain])
# Check convergence and continue sampling if not converged
GR = Gelman_Rubin(sampled_params)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + '_' + str(total_iterations) + '.txt'), GR)
old_samples = sampled_params
if np.any(GR > GR_cutoff):
starts = [sampled_params[chain][-1, :] for chain in range(nchains)]
while not converged:
total_iterations += niterations
sampled_params, log_ps = run_dream(priors_list, posterior,
start=starts,
niterations=niterations,
nchains=nchains,
multitry=True, parallel=True,
gamma_levels=4,
adapt_gamma=True,
history_thin=1,
model_name=sim_name,
verbose=verbose, restart=True)
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) + '_' + str(total_iterations)), sampled_params[chain])
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) + '_logPs_' + str(total_iterations)), log_ps[chain])
old_samples = [np.concatenate((old_samples[chain], sampled_params[chain])) for chain in range(nchains)]
GR = Gelman_Rubin(old_samples)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + '_' + str(total_iterations)+'.txt'), GR)
if np.all(GR < GR_cutoff) or total_iterations >= iteration_cutoff:
converged = True
log_ps = np.array(log_ps)
sampled_params = np.array(sampled_params)
try:
# Maximum posterior model:
max_in_each_chain = [np.argmax(chain) for chain in log_ps]
global_max_chain_idx = np.argmax([log_ps[chain][max_idx] for chain, max_idx in enumerate(max_in_each_chain)])
ml_params = sampled_params[global_max_chain_idx, max_in_each_chain[global_max_chain_idx]]
ml_model = {pname: 10 ** pvalue for pname, pvalue in zip(sampled_param_names, ml_params)}
print(ml_model,
file=open(os.path.join(save_dir, sim_name + '_ML_params.txt'), 'w'))
# Maximum posterior for each chain
ml_samples = [{pname: 10 ** pvalue for pname, pvalue in zip(sampled_param_names, sampled_params[chain_idx, max_in_each_chain[chain_idx]])} for chain_idx in range(nchains)]
print(ml_samples,
file=open(os.path.join(save_dir, sim_name + '_ML_samples.txt'), 'w'))
except IndexError:
print("IndexError finding maximum posterior parameters")
pass
try:
# Compute burn-in
total_iterations = len(old_samples[0])
burnin = int(total_iterations / 2)
samples = np.concatenate(list((old_samples[i][burnin:, :] for i in range(len(old_samples)))))
np.save(os.path.join(save_dir, sim_name+'_samples'), samples)
# Basic statistics
mean_parameters = np.mean(samples, axis=0)
median_parameters = np.median(samples, axis=0)
np.save(os.path.join(save_dir, 'mean_parameters'), mean_parameters)
np.save(os.path.join(save_dir, 'median_parameters'), median_parameters)
df = pd.DataFrame(samples, columns=sampled_param_names)
df.describe().to_csv(os.path.join(save_dir,
'descriptive_statistics.csv'))
if plot_posteriors:
# Prepare plot canvas
ndims = len(old_samples[0][0])
colors = sns.color_palette(n_colors=ndims)
priors_dict = dict(list(zip(sampled_param_names, priors_list)))
# computes the factors of ndims:
f1 = list(set(reduce(list.__add__, ([i, ndims//i] for i in range(1, int(ndims**0.5) + 1) if ndims % i == 0))))
ncols = f1[int(len(f1) / 2 - 1)]
nrows = f1[int(len(f1) / 2)]
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
# Plot posterior distributions
for dim, ax in enumerate(fig.axes):
p = sampled_param_names[dim]
sns.histplot(samples[:, dim], color=colors[dim], ax=ax, kde=True, stat='density')
xrange = np.arange(priors_dict[p][0] - 3 * priors_dict[p][1],
priors_dict[p][0] + 3 * priors_dict[p][1], 0.01)
yrange = norm.pdf(xrange, priors_dict[p][0], priors_dict[p][1])
ax.plot(xrange, yrange, 'k--')
ax.set_xlabel(p)
ax.set_ylabel(None)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
plt.savefig(os.path.join(save_dir, sim_name + 'posteriors.pdf'))
plt.close()
# Create pairplot
g = sns.pairplot(df)
for i, j in zip(*np.triu_indices_from(g.axes, 1)):
g.axes[i, j].set_visible(False)
g.savefig(os.path.join(save_dir, 'corner_plot.png'))
except (ImportError, OSError, AttributeError, TypeError):
pass
# Clean up stray files
try:
shutil.move(os.path.join(os.getcwd(), os.sep, '*_DREAM_chain_*.*'),
save_dir)
except FileNotFoundError:
pass
def fit_with_DREAM(sim_name, parameter_dict, likelihood):
original_params = [parameter_dict[k] for k in parameter_dict.keys()]
priors_list = []
for p in original_params:
priors_list.append(SampledParam(norm, loc=np.log(p), scale=1.0))
# Set simulation parameters
niterations = 10000
converged = False
total_iterations = niterations
nchains = 5
# Make save directory
today = datetime.now()
save_dir = "PyDREAM_" + today.strftime('%d-%m-%Y') + "_" + str(niterations)
os.makedirs(os.path.join(os.getcwd(), save_dir), exist_ok=True)
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(priors_list, likelihood, start=np.log(original_params),
niterations=niterations, nchains=nchains, multitry=False,
gamma_levels=4, adapt_gamma=True, history_thin=1, model_name=sim_name,
verbose=True)
# Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name + str(chain) + '_' + str(total_iterations)), sampled_params[chain])
np.save(os.path.join(save_dir, sim_name + str(chain) + '_' + str(total_iterations)), log_ps[chain])
# Check convergence and continue sampling if not converged
GR = Gelman_Rubin(sampled_params)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + str(total_iterations) + '.txt'), GR)
old_samples = sampled_params
if np.any(GR > 1.2):
starts = [sampled_params[chain][-1, :] for chain in range(nchains)]
while not converged:
total_iterations += niterations
sampled_params, log_ps = run_dream(priors_list, likelihood, start=starts, niterations=niterations,
nchains=nchains, multitry=False, gamma_levels=4, adapt_gamma=True,
history_thin=1, model_name=sim_name, verbose=True, restart=True)
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) + '_' + str(total_iterations)),
sampled_params[chain])
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) + '_' + str(total_iterations)),
log_ps[chain])
old_samples = [np.concatenate((old_samples[chain], sampled_params[chain])) for chain in range(nchains)]
GR = Gelman_Rubin(old_samples)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + '_' + str(total_iterations) + '.txt'), GR)
if np.all(GR < 1.2):
converged = True
try:
# Plot output
total_iterations = len(old_samples[0])
burnin = int(total_iterations / 2)
samples = np.concatenate(list((old_samples[i][burnin:, :] for i in range(len(old_samples)))))
np.save(os.path.join(save_dir, sim_name+'_samples'), samples)
ndims = len(old_samples[0][0])
colors = sns.color_palette(n_colors=ndims)
for dim in range(ndims):
fig = plt.figure()
sns.distplot(samples[:, dim], color=colors[dim])
fig.savefig(os.path.join(save_dir, sim_name + '_dimension_' + str(dim) + '_' + list(parameter_dict.keys())[dim]+ '.pdf'))
# Convert to dataframe
df = pd.DataFrame(samples, columns=parameter_dict.keys())
g = sns.pairplot(df)
for i, j in zip(*np.triu_indices_from(g.axes, 1)):
g.axes[i,j].set_visible(False)
g.savefig(os.path.join(save_dir, 'corner_plot.pdf'))
# Basic statistics
mean_parameters = np.mean(samples, axis=0)
median_parameters = np.median(samples, axis=0)
np.save(os.path.join(save_dir, 'mean_parameters'), mean_parameters)
np.save(os.path.join(save_dir, 'median_parameters'), median_parameters)
df.describe().to_csv(os.path.join(save_dir, 'descriptive_statistics.csv'))
except ImportError:
pass
return 0
converged = False
if __name__ == '__main__':
sampled_params, log_ps = run_dream(
parameters=sampled_params_list,
likelihood=likelihood,
niterations=niterations,
nchains=nchains,
multitry=False,
gamma_levels=6,
nCR=6,
snooker_=0.4,
adapt_gamma=False,
history_thin=1,
model_name='dreamzs_5chain_NEv2_Sage_test_NM',
verbose=True)
total_iterations = niterations
# Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'dreamzs_5chain_NEv2_Sage_test_NM_sampled_params_chain_' +
str(chain) + '_' + str(total_iterations), sampled_params[chain])
np.save(
'dreamzs_5chain_NEv2_Sage_test_NM_logps_chain_' + str(chain) +
'_' + str(total_iterations), log_ps[chain])
GR = Gelman_Rubin(sampled_params)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(
'dreamzs_5chain_NEv2_Sage_test_NM_GelmanRubin_iteration_' +
str(total_iterations) + '.txt', GR)
def DREAM_fit(model, priors_list, posterior, start_params,
sampled_param_names, niterations, nchains, sim_name,
save_dir, custom_params={}, GR_cutoff=1.2):
"""
The DREAM fitting algorithm as implemented in run_dream(), plus decorations
for saving run parameters, checking convergence, and post fitting analysis.
"""
converged = False
total_iterations = niterations
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(priors_list, posterior,
start=start_params,
niterations=niterations,
nchains=nchains,
multitry=False,
gamma_levels=4, adapt_gamma=True,
history_thin=1, model_name=sim_name,
verbose=True)
# Save sampling output (sampled param values and their corresponding logps)
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name+str(chain) + '_' +
str(total_iterations)), sampled_params[chain])
np.save(os.path.join(save_dir, sim_name+str(chain) + '_' +
str(total_iterations)), log_ps[chain])
# Check convergence and continue sampling if not converged
GR = Gelman_Rubin(sampled_params)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + str(total_iterations) +
'.txt'), GR)
old_samples = sampled_params
if np.any(GR > GR_cutoff):
starts = [sampled_params[chain][-1, :] for chain in range(nchains)]
while not converged:
total_iterations += niterations
sampled_params, log_ps = run_dream(priors_list, posterior,
start=starts,
niterations=niterations,
nchains=nchains, multitry=False,
gamma_levels=4,
adapt_gamma=True,
history_thin=1,
model_name=sim_name,
verbose=True, restart=True)
for chain in range(len(sampled_params)):
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) +
'_' + str(total_iterations)),
sampled_params[chain])
np.save(os.path.join(save_dir, sim_name + '_' + str(chain) +
'_' + str(total_iterations)),
log_ps[chain])
old_samples = [np.concatenate((old_samples[chain],
sampled_params[chain])) for chain in range(nchains)]
GR = Gelman_Rubin(old_samples)
print('At iteration: ', total_iterations, ' GR = ', GR)
np.savetxt(os.path.join(save_dir, sim_name + '_' +
str(total_iterations)+'.txt'), GR)
if np.all(GR < GR_cutoff):
converged = True
log_ps = np.array(log_ps)
sampled_params = np.array(sampled_params)
try:
# Maximum posterior model:
max_in_each_chain = [np.argmax(chain) for chain in log_ps]
global_max_chain_idx = np.argmax([log_ps[chain][max_idx] for
chain, max_idx in
enumerate(max_in_each_chain)])
ml_params = sampled_params[global_max_chain_idx,
max_in_each_chain[global_max_chain_idx]]
ml_model = {pname: 10 ** pvalue for pname, pvalue in
zip(sampled_param_names, ml_params)}
print(ml_model,
file=open(os.path.join(save_dir, sim_name +
'_ML_params.txt'), 'w'))
except IndexError:
print("IndexError finding maximum posterior parameters")
pass
try:
# Plot output
total_iterations = len(old_samples[0])
burnin = int(total_iterations / 2)
samples = np.concatenate(list((old_samples[i][burnin:, :] for
i in range(len(old_samples)))))
np.save(os.path.join(save_dir, sim_name+'_samples'), samples)
ndims = len(old_samples[0][0])
colors = sns.color_palette(n_colors=ndims)
for dim in range(ndims):
sns.distplot(samples[:, dim], color=colors[dim])
plt.savefig(os.path.join(save_dir, sim_name + '_dimension_' +
str(dim) + '_' +
sampled_param_names[dim] +
'.pdf'))
# Convert to dataframe
df = pd.DataFrame(samples, columns=sampled_param_names)
g = sns.pairplot(df)
for i, j in zip(*np.triu_indices_from(g.axes, 1)):
g.axes[i, j].set_visible(False)
g.savefig(os.path.join(save_dir, 'corner_plot.pdf'))
# Basic statistics
mean_parameters = np.mean(samples, axis=0)
median_parameters = np.median(samples, axis=0)
np.save(os.path.join(save_dir, 'mean_parameters'), mean_parameters)
np.save(os.path.join(save_dir, 'median_parameters'), median_parameters)
df.describe().to_csv(os.path.join(save_dir,
'descriptive_statistics.csv'))
except (ImportError, OSError):
pass
# Clean up stray files
try:
shutil.move(os.path.join(os.getcwd(), '*_DREAM_chain_*.*'),
save_dir)
except FileNotFoundError:
pass
