Source code for lenstronomy.Sampling.sampler

__author__ = ['sibirrer', 'ajshajib', 'dgilman']

import time

import numpy as np
from lenstronomy.Sampling.Samplers.pso import ParticleSwarmOptimizer
from lenstronomy.Util import sampling_util
from lenstronomy.Sampling.Pool.pool import choose_pool
from scipy.optimize import minimize

__all__ = ['Sampler']


[docs]class Sampler(object): """ class which executes the different sampling methods Available are: MCMC with emcee and comsoHammer and a Particle Swarm Optimizer. This are examples and depending on your problem, you might find other/better solutions. Feel free to sample with your convenient sampler! """ def __init__(self, likelihoodModule): """ :param likelihoodModule: instance of LikelihoodModule class """ self.chain = likelihoodModule self.lower_limit, self.upper_limit = self.chain.param_limits
[docs] def simplex(self, init_pos, n_iterations, method, print_key='SIMPLEX'): """ :param init_pos: starting point for the optimization :param n_iterations: maximum number of iterations :param method: the optimization method, default is 'Nelder-Mead' :return: the best fit for the lens model using the optimization routine specified by method """ print('Performing the optimization using algorithm:', method) time_start = time.time() #negativelogL = lambda x: -1 * self.chain.logL(x) result = minimize(self.chain.negativelogL, x0=init_pos, method=method, options={'maxiter': n_iterations, 'disp': True}) logL = self.chain.logL(result['x']) kwargs_return = self.chain.param.args2kwargs(result['x']) print(-logL * 2 / (max(self.chain.effective_num_data_points(**kwargs_return), 1)), 'reduced X^2 of best position') print(logL, 'logL') print(self.chain.effective_num_data_points(**kwargs_return), 'effective number of data points') print(kwargs_return.get('kwargs_lens', None), 'lens result') print(kwargs_return.get('kwargs_source', None), 'source result') print(kwargs_return.get('kwargs_lens_light', None), 'lens light result') print(kwargs_return.get('kwargs_ps', None), 'point source result') print(kwargs_return.get('kwargs_special', None), 'special param result') time_end = time.time() print(time_end - time_start, 'time used for ', print_key) print('===================') return result['x']
[docs] def pso(self, n_particles, n_iterations, lower_start=None, upper_start=None, threadCount=1, init_pos=None, mpi=False, print_key='PSO'): """ Return the best fit for the lens model on catalogue basis with particle swarm optimizer. :param n_particles: number of particles in the sampling process :param n_iterations: number of iterations of the swarm :param lower_start: numpy array, lower end parameter of the values of the starting particles :param upper_start: numpy array, upper end parameter of the values of the starting particles :param threadCount: number of threads in the computation (only applied if mpi=False) :param init_pos: numpy array, position of the initial best guess model :param mpi: bool, if True, makes instance of MPIPool to allow for MPI execution :param print_key: string, prints the process name in the progress bar (optional) :return: kwargs_result (of best fit), [lnlikelihood of samples, positions of samples, velocity of sampels) """ if lower_start is None or upper_start is None: lower_start, upper_start = np.array(self.lower_limit), np.array(self.upper_limit) print("PSO initialises its particles with default values") else: lower_start = np.maximum(lower_start, self.lower_limit) upper_start = np.minimum(upper_start, self.upper_limit) pool = choose_pool(mpi=mpi, processes=threadCount, use_dill=True) if mpi is True and pool.is_master(): print('MPI option chosen for PSO.') pso = ParticleSwarmOptimizer(self.chain.logL, lower_start, upper_start, n_particles, pool=pool) if init_pos is None: init_pos = (upper_start - lower_start) / 2 + lower_start pso.set_global_best(init_pos, [0]*len(init_pos), self.chain.logL(init_pos)) if pool.is_master(): print('Computing the %s ...' % print_key) time_start = time.time() result, [chi2_list, pos_list, vel_list] = pso.optimize(n_iterations) if pool.is_master(): kwargs_return = self.chain.param.args2kwargs(result) print(pso.global_best.fitness * 2 / (max( self.chain.effective_num_data_points(**kwargs_return), 1)), 'reduced X^2 of best position') print(pso.global_best.fitness, 'logL') print(self.chain.effective_num_data_points(**kwargs_return), 'effective number of data points') print(kwargs_return.get('kwargs_lens', None), 'lens result') print(kwargs_return.get('kwargs_source', None), 'source result') print(kwargs_return.get('kwargs_lens_light', None), 'lens light result') print(kwargs_return.get('kwargs_ps', None), 'point source result') print(kwargs_return.get('kwargs_special', None), 'special param result') time_end = time.time() print(time_end - time_start, 'time used for ', print_key) print('===================') return result, [chi2_list, pos_list, vel_list]
[docs] def mcmc_emcee(self, n_walkers, n_run, n_burn, mean_start, sigma_start, mpi=False, progress=False, threadCount=1, initpos=None, backup_filename=None, start_from_backup=False): """ Run MCMC with emcee. For details, please have a look at the documentation of the emcee packager. :param n_walkers: number of walkers in the emcee process :type n_walkers: integer :param n_run: number of sampling (after burn-in) of the emcee :type n_run: integer :param n_burn: number of burn-in iterations (those will not be saved in the output sample) :type n_burn: integer :param mean_start: mean of the parameter position of the initialising sample :type mean_start: numpy array of length the number of parameters :param sigma_start: spread of the parameter values (uncorrelated in each dimension) of the initialising sample :type sigma_start: numpy array of length the number of parameters :param mpi: if True, initializes an MPIPool to allow for MPI execution of the sampler :type mpi: bool :param progress: if True, prints the progress bar :type progress: bool :param threadCount: number of threats in multi-processing (not applicable for MPI) :type threadCount: integer :param initpos: initial walker position to start sampling (optional) :type initpos: numpy array of size num param x num walkser :param backup_filename: name of the HDF5 file where sampling state is saved (through emcee backend engine) :type backup_filename: string :param start_from_backup: if True, start from the state saved in `backup_filename`. Otherwise, create a new backup file with name `backup_filename` (any already existing file is overwritten!). :type start_from_backup: bool :return: samples, ln likelihood value of samples :rtype: numpy 2d array, numpy 1d array """ import emcee num_param, _ = self.chain.param.num_param() if initpos is None: initpos = sampling_util.sample_ball_truncated(mean_start, sigma_start, self.lower_limit, self.upper_limit, size=n_walkers) pool = choose_pool(mpi=mpi, processes=threadCount, use_dill=True) if backup_filename is not None: backend = emcee.backends.HDFBackend(backup_filename, name="lenstronomy_mcmc_emcee") if pool.is_master(): print("Warning: All samples (including burn-in) will be saved in backup file '{}'.".format(backup_filename)) if start_from_backup: initpos = None n_run_eff = n_run else: n_run_eff = n_burn + n_run backend.reset(n_walkers, num_param) if pool.is_master(): print("Warning: backup file '{}' has been reset!".format(backup_filename)) else: backend = None n_run_eff = n_burn + n_run time_start = time.time() sampler = emcee.EnsembleSampler(n_walkers, num_param, self.chain.logL, pool=pool, backend=backend) sampler.run_mcmc(initpos, n_run_eff, progress=progress) flat_samples = sampler.get_chain(discard=n_burn, thin=1, flat=True) dist = sampler.get_log_prob(flat=True, discard=n_burn, thin=1) if pool.is_master(): print('Computing the MCMC...') print('Number of walkers = ', n_walkers) print('Burn-in iterations: ', n_burn) print('Sampling iterations (in current run):', n_run_eff) time_end = time.time() print(time_end - time_start, 'time taken for MCMC sampling') return flat_samples, dist