def run(name, params_dict, mu=0.0):
simulation_parameters = get_implicit_lasso_simulation(**params_dict)
# Add mu attribute to simulation_parameters.
setattr(simulation_parameters, 'mu', mu)
# Add a correlation matrix if mu is not 0.0.
if mu != 0.0:
simulation_parameters.covariates_kwargs_dict['covariance_matrix'] = \
mu * np.ones((d, d)) + (1.0 - mu) * np.identity(d)
start = time.time()
results = core.run_in_parallel(
runs_per_device,
processes_per_device,
simulation_parameters,
pytorch_configs)
# Do not save the covariance matrix.
simulation_parameters.covariates_kwargs_dict = {}
utils.save_simulation_output(
simulation_name, results, simulation_parameters)
end = time.time()
print("Execution time for id: ", run_id, ":",
end - start, "seconds.")
def run(name, params_dict):
simulation_parameters = get_implicit_lasso_simulation(**params_dict)
start = time.time()
results = core.run_in_parallel(runs_per_device, processes_per_device,
simulation_parameters, pytorch_configs)
utils.save_simulation_output(simulation_name, results,
simulation_parameters)
end = time.time()
print("Execution time for id: ", run_id, ":", end - start, "seconds.")
def run(name, params_dict, paths_simulation=False):
simulation_parameters = get_implicit_lasso_simulation(**params_dict)
start = time.time()
if paths_simulation:
# This is the last simulation, the one used for plotting gd and
# lasso paths.
results = core.run_in_parallel(
1,
1,
simulation_parameters,
[pytorch_configs[0]])
else:
results = core.run_in_parallel(
runs_per_device,
processes_per_device,
simulation_parameters,
pytorch_configs)
utils.save_simulation_output(
simulation_name, results, simulation_parameters)
end = time.time()
print("Execution time for id: ", run_id, ":",
end - start, "seconds.")
simulation_name = output_dir + "alphas/run_" + str(run_id)
simulation_parameters = get_implicit_lasso_simulation(
alpha=alpha,
observe_parameters=0,
noise_std=noise_std,
observers_frequency=10,
run_glmnet=0,
store_glmnet_path=0,
**default_params)
# Start our simulations.
start = time.time()
results = core.run_in_parallel(runs_per_device,
processes_per_device,
simulation_parameters,
pytorch_configs)
utils.save_simulation_output(simulation_name, results,
simulation_parameters)
end = time.time()
print("Execution time for id: ", run_id, ":", end - start,
"seconds.")
run_id += 1
# Now we perform simulatinos for saving the parameter paths.
alphas = 10.0**(np.array([-3.0, -12.0]))
run_id = 0
print("Starting simulations for saving parameter paths")
for alpha in alphas:
for noise_std in noise_stds:
simulation_name = output_dir + "paths/run_" + str(run_id)
simulation_parameters = get_implicit_lasso_simulation(
alpha=alpha,
noise_std=noise_std,