def test_history_correct_after_sampling_simple_model(self):
"""Test that the history saved matches with the returned sampled parameter values for a one-dimensional test model."""
self.param, self.like = onedmodel()
model = Model(self.like, self.param)
step = Dream(model=model,
save_history=True,
history_thin=1,
model_name='test_history_correct',
adapt_crossover=False)
sampled_params, logps = run_dream(self.param,
self.like,
niterations=10,
nchains=5,
save_history=True,
history_thin=1,
model_name='test_history_correct',
adapt_crossover=False,
verbose=False)
history = np.load('test_history_correct_DREAM_chain_history.npy')
self.assertEqual(
len(history),
step.total_var_dimension *
((10 * 5 / step.history_thin) + step.nseedchains))
history_no_seedchains = history[(step.total_var_dimension *
step.nseedchains)::]
sorted_history = np.sort(history_no_seedchains)
sorted_sampled_params = np.sort(np.array(sampled_params).flatten())
for sampled_param, history_param in zip(sorted_history,
sorted_sampled_params):
self.assertEqual(sampled_param, history_param)
remove('test_history_correct_DREAM_chain_history.npy')
remove('test_history_correct_DREAM_chain_adapted_crossoverprob.npy')
remove('test_history_correct_DREAM_chain_adapted_gammalevelprob.npy')
def test_crossover_file_loading(self):
"""Test that when a crossover file is loaded the crossover values are set to the file values and not adapted."""
self.param, self.like = multidmodel()
old_crossovervals = np.array([.45, .20, .35])
np.save('testing_crossoverval_load_DREAM.npy', old_crossovervals)
model = Model(self.like, self.param)
dream = Dream(model=model,
crossover_file='testing_crossoverval_load_DREAM.npy',
save_history=True,
model_name='testing_crossover_load')
self.assertTrue(
np.array_equal(dream.CR_probabilities, old_crossovervals))
sampled_vals, logps = run_dream(
self.param,
self.like,
niterations=100,
nchains=3,
crossover_file='testing_crossoverval_load_DREAM.npy',
model_name='testing_crossover_load',
save_history=True,
verbose=False)
crossover_vals_after_sampling = np.load(
'testing_crossover_load_DREAM_chain_adapted_crossoverprob.npy')
self.assertIs(
np.array_equal(crossover_vals_after_sampling, old_crossovervals),
True)
remove('testing_crossover_load_DREAM_chain_adapted_crossoverprob.npy')
remove('testing_crossover_load_DREAM_chain_adapted_gammalevelprob.npy')
remove('testing_crossoverval_load_DREAM.npy')
remove('testing_crossover_load_DREAM_chain_history.npy')
def test_history_file_loading(self):
"""Test that when a history file is provided it is loaded and appended to the new history."""
self.param, self.like = onedmodel()
model = Model(self.like, self.param)
step = Dream(model=model)
old_history = np.array([1, 3, 5, 7, 9, 11])
step.save_history_to_disc(old_history, 'testing_history_load_')
sampled_params, logps = run_dream(self.param, self.like, niterations=3, nchains=3, history_thin=1, history_file='testing_history_load_DREAM_chain_history.npy', save_history=True, model_name='test_history_loading', verbose=False)
new_history = np.load('test_history_loading_DREAM_chain_history.npy')
self.assertEqual(len(new_history), (len(old_history.flatten())+(3*step.total_var_dimension*3)))
new_history_seed = new_history[:len(old_history.flatten())]
new_history_seed_reshaped = new_history_seed.reshape(old_history.shape)
self.assertIs(np.array_equal(old_history, new_history_seed_reshaped), True)
added_history = new_history[len(old_history.flatten())::]
sorted_history = np.sort(added_history)
sorted_sampled_params = np.sort(np.array(sampled_params).flatten())
for sampled_param, history_param in zip(sorted_history, sorted_sampled_params):
self.assertEqual(sampled_param, history_param)
remove('testing_history_load_DREAM_chain_history.npy')
remove('testing_history_load_DREAM_chain_adapted_crossoverprob.npy')
remove('testing_history_load_DREAM_chain_adapted_gammalevelprob.npy')
remove('test_history_loading_DREAM_chain_adapted_crossoverprob.npy')
remove('test_history_loading_DREAM_chain_adapted_gammalevelprob.npy')
remove('test_history_loading_DREAM_chain_history.npy')
def test_boundaries_obeyed_aftersampling(self):
"""Test that boundaries are respected if included."""
self.param, self.like = multidmodel_uniform()
model = Model(self.like, self.param)
step = Dream(model=model,
save_history=True,
history_thin=1,
model_name='test_boundaries',
adapt_crossover=False,
hardboundaries=True,
nverbose=10)
sampled_params, logps = run_dream(self.param,
self.like,
niterations=1000,
nchains=5,
save_history=True,
history_thin=1,
model_name='test_boundaries',
adapt_crossover=False,
verbose=True,
hardboundaries=True,
nverbose=10)
history = np.load('test_boundaries_DREAM_chain_history.npy')
variables = model.sampled_parameters
dim = 0
for var in variables:
interval = var.interval()
dim += var.dsize
lowerbound = interval[0]
upperbound = interval[1]
npoints = int(len(history) / float(dim))
reshaped_history = np.reshape(history, (npoints, dim))
print('reshaped history: ', reshaped_history)
print('upper ', upperbound, ' and lower ', lowerbound)
print('lower bounds: ', (reshaped_history < lowerbound).any())
print('upper bounds: ', (reshaped_history > upperbound).any())
print('disobeyed lower: ',
reshaped_history[reshaped_history < lowerbound])
print('disobeyed upper: ',
reshaped_history[reshaped_history > upperbound])
self.assertFalse((reshaped_history < lowerbound).any())
self.assertFalse((reshaped_history > upperbound).any())
remove('test_boundaries_DREAM_chain_adapted_crossoverprob.npy')
remove('test_boundaries_DREAM_chain_adapted_gammalevelprob.npy')
remove('test_boundaries_DREAM_chain_history.npy')
def test_robertson_example(self):
"""Test that the Robertson example runs and returns values of the expected shape."""
nchains = robertson_kwargs['nchains']
robertson_kwargs['niterations'] = 100
robertson_kwargs['verbose'] = False
robertson_kwargs['save_history'] = False
#Check likelihood fxn runs
logp = robertson_like([3, 8, .11])
#Check sampling runs and gives output of expected shape
sampled_params, logps = run_dream(**robertson_kwargs)
self.assertEqual(len(sampled_params), nchains)
self.assertEqual(len(sampled_params[0]), 100)
self.assertEqual(len(sampled_params[0][0]), 3)
self.assertEqual(len(logps), nchains)
self.assertEqual(len(logps[0]), 100)
self.assertEqual(len(logps[0][0]), 1)
def test_ndimgaussian_example(self):
"""Test that the n-dimensional gaussian example runs and returns values of the expected shape."""
nchains = ndimgauss_kwargs['nchains']
ndimgauss_kwargs['niterations'] = 100
ndimgauss_kwargs['verbose'] = False
ndimgauss_kwargs['save_history'] = False
#Check likelihood fxn runs
logp = ndimgauss_like(np.random.random_sample((200, )) * 10)
#Check sampling runs and gives output of expected shape
sampled_params, logps = run_dream(**ndimgauss_kwargs)
self.assertEqual(len(sampled_params), nchains)
self.assertEqual(len(sampled_params[0]), 100)
self.assertEqual(len(sampled_params[0][0]), 200)
self.assertEqual(len(logps), nchains)
self.assertEqual(len(logps[0]), 100)
self.assertEqual(len(logps[0][0]), 1)
remove('ndim_gaussian_seed.npy')
def test_CORM_example(self):
"""Test that the CORM example runs and returns values of the expected shape."""
nchains = corm_kwargs['nchains']
corm_kwargs['niterations'] = 100
corm_kwargs['verbose'] = False
#Check likelihood fxn works
logp = corm_like([-5, -3, .1, 10, 8, 4, .33, -.58, 99, 1, 0, 11])
#Check entire algorithm will run and give results of the expected shape
sampled_params, logps = run_dream(**corm_kwargs)
self.assertEqual(len(sampled_params), nchains)
self.assertEqual(len(sampled_params[0]), 100)
self.assertEqual(len(sampled_params[0][0]), 12)
self.assertEqual(len(logps), nchains)
self.assertEqual(len(logps[0]), 100)
self.assertEqual(len(logps[0][0]), 1)
remove('corm_dreamzs_5chain_DREAM_chain_adapted_crossoverprob.npy')
remove('corm_dreamzs_5chain_DREAM_chain_adapted_gammalevelprob.npy')
remove('corm_dreamzs_5chain_DREAM_chain_history.npy')
def test_mixturemodel_example(self):
"""Test that the mixture model example runs and returns values of the expected shape."""
nchains = mix_kwargs['nchains']
mix_kwargs['niterations'] = 100
mix_kwargs['verbose'] = False
mix_kwargs['save_history'] = False
#Check likelihood fxn works
logp = mix_like(np.array([1, -9, 3, .04, 2, -8, 11, .001, -1, 10]))
#Check that sampling runs and gives output of expected shape
sampled_params, logps = run_dream(**mix_kwargs)
self.assertEqual(len(sampled_params), nchains)
self.assertEqual(len(sampled_params[0]), 100)
self.assertEqual(len(sampled_params[0][0]), 10)
self.assertEqual(len(logps), nchains)
self.assertEqual(len(logps[0]), 100)
self.assertEqual(len(logps[0][0]), 1)
remove('mixturemodel_seed.npy')
sampled_parameter_names = [parameters_to_sample]
niterations = 100
converged = False
total_iterations = niterations
nchains = 5
if __name__ == '__main__':
#Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(sampled_parameter_names,
likelihood,
niterations=niterations,
nchains=nchains,
multitry=8,
gamma_levels=4,
adapt_gamma=True,
history_thin=1,
model_name='EF5',
verbose=True,
parallel=True)
#Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'EF5_sampled_params_chain_' + str(chain) + '_' +
str(total_iterations), sampled_params[chain])
np.save('EF5_logps_chain_' + str(chain) + '_' + str(total_iterations),
log_ps[chain])
# Check convergence and continue sampling if not converged
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
return cost
# Run pydream
niterations = 10000
nchains = 4
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,
hardboundaries=True,
snooker_=0.4,
adapt_gamma=False,
history_thin=1,
model_name='dreamzs_5chain_dirichlet',
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_dirichlet_sampled_params_chain_' + str(chain)+'_'+str(total_iterations), sampled_params[chain])
np.save('dreamzs_5chain_dirichletlogps_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_dirichlet_GelmanRubin_iteration_'+str(total_iterations)+'.txt', GR)
sampled_parameter_names = [parameters_to_sample]
niterations = 10
converged = False
total_iterations = niterations
nchains = 5
if __name__ == '__main__':
#Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(
sampled_parameter_names,
likelihood,
niterations=niterations,
nchains=nchains,
multitry=False,
gamma_levels=4,
adapt_gamma=True,
history_thin=1,
model_name='robertson_nopysb_dreamzs_5chain',
verbose=True)
#Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'robertson_nopysb_dreamzs_5chain_sampled_params_chain_' +
str(chain) + '_' + str(total_iterations), sampled_params[chain])
np.save(
'robertson_nopysb_dreamzs_5chain_logps_chain_' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
return total_cost
# Run pydream
niterations = 10000
nchains = 5
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])
sim_name = 'mixed_IFN'
if __name__ == '__main__':
# 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=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)),
sp_kf_diff_nev1_nev2 = SampledParam(uniform, loc=np.log10(0.05), scale=2.5)
sampled_params_list.append(sp_kf_diff_nev1_nev2)
sp_kr_diff_nev1_nev2 = SampledParam(uniform, loc=np.log10(0.05), scale=2.5)
sampled_params_list.append(sp_kr_diff_nev1_nev2)
sp_kf_diff_nev2_nonNe = SampledParam(uniform, loc=np.log10(0.05), scale=2.5)
sampled_params_list.append(sp_kf_diff_nev2_nonNe)
converged = False
sampled_params, log_ps = run_dream(parameters=sampled_params_list,
likelihood=likelihood,
niterations=niterations,
nchains=nchains,
start=starts,
multitry=False,
gamma_levels=6,
nCR=6,
p_gamma_unity=0.5,
adapt_gamma=True,
history_thin=1,
model_name='dreamzs_5chain_NEv2_Sage_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_NM_sampled_params_chain_' + str(chain) +
'_' + str(total_iterations), sampled_params[chain])
np.save(
'dreamzs_5chain_NEv2_Sage_NM_logps_chain_' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
scale=upper_bounds)
# Parameters for run_dream
n_iterations = 500
nchains = 3
if __name__ == '__main__':
total_iterations = n_iterations
converged = False
sampled_params, log_ps = core.run_dream(parameters_to_sample,
calculate_likelihood_dream_wm,
niterations=n_iterations,
nchains=nchains,
random_start=True,
start=None,
save_history=True,
adapt_gamma=False,
gamma_levels=1,
tempering=False,
multitry=False,
parallel=False,
model_name='wm_model')
# Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'wm_model_sampled_params_chain_' + str(chain) + '_' +
str(total_iterations), sampled_params[chain])
np.save(
'wm_model_logps_chain_' + str(chain) + '_' + str(total_iterations),
log_ps[chain])
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
starts = [m[chain] for chain in range(3)]
if __name__ == '__main__':
niterations = 50000
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
converged = False
total_iterations = niterations
nchains = 3
sampled_params, log_ps = run_dream([params],
likelihood,
niterations=niterations,
nchains=nchains,
start=starts,
start_random=False,
save_history=True,
history_file='mixturemodel_seed.npy',
multitry=5,
parallel=False)
for chain in range(len(sampled_params)):
np.save(
'mixmod_mtdreamzs_3chain_sampled_params_chain_' + str(chain) +
'_' + str(total_iterations), sampled_params[chain])
np.save(
'mixmod_mtdreamzs_3chain_logps_chain_' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
os.remove('mixturemodel_seed.npy')
params = FlatParam(test_value=mu)
if __name__ == '__main__':
niterations = 150000
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
converged = False
total_iterations = niterations
nchains = 3
sampled_params, log_ps = run_dream([params],
likelihood,
niterations=niterations,
nchains=nchains,
start=starts,
start_random=False,
save_history=True,
adapt_gamma=False,
gamma_levels=1,
tempering=False,
history_file='ndim_gaussian_seed.npy',
multitry=5,
parallel=False,
model_name='ndim_gaussian')
for chain in range(len(sampled_params)):
np.save(
'ndimgauss_mtdreamzs_3chain_sampled_params_chain_' + str(chain) +
'_' + str(total_iterations), sampled_params[chain])
np.save(
'ndimgauss_mtdreamzs_3chain_logps_chain_' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
optimization_method = 'MCMC' # If a name different from MCMC is given then scipy is invoked with optimization_method routine
if optimization_method == 'MCMC':
if __name__ == '__main__':
#Run DREAM sampling. Documentation of DREAM options is in Dream.py.
sampled_params, log_ps = run_dream(
parameters_to_sample,
likelihood,
niterations=niterations,
nchains=nchains,
multitry=True,
gamma_levels=4,
adapt_gamma=True,
parallel=True,
history_thin=200,
model_name='bsfit_nopysb_dreamzs_5chain',
verbose=True,
start=startchains)
#Save sampling output (sampled parameter values and their corresponding logps).
for chain in range(len(sampled_params)):
np.save(
'dream/bsfit_sampled_params_chain' + str(chain) + '_' +
str(total_iterations), sampled_params[chain])
np.save(
'dream/bsfit_logps_chain' + str(chain) + '_' +
str(total_iterations), log_ps[chain])
nchains = 4
niterations = 50000
if __name__ == '__main__':
# Run DREAM sampling. Documentation of DREAM options is in Dream.py.
converged = False
total_iterations = niterations
sampled_params, log_ps = run_dream(
parameters=sampled_parameter_names,
likelihood=likelihood,
niterations=niterations,
nchains=nchains,
multitry=False,
gamma_levels=4,
adapt_gamma=True,
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])
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
