diff --git a/config/non_linear/mle.yaml b/config/non_linear/mle.yaml index 365b911..25c0be5 100755 --- a/config/non_linear/mle.yaml +++ b/config/non_linear/mle.yaml @@ -2,53 +2,10 @@ # **PyAutoFit** supports the following maximum likelihood estimator (MLE) algorithms: -# - PySwarms: https://github.com/ljvmiranda921/pyswarms / https://pyswarms.readthedocs.io/en/latest/index.html # Settings in the [search], [run] and [options] entries are specific to each nested algorithm and should be # determined by consulting that method's own readthedocs. -PySwarmsGlobal: - run: - iters: 2000 - search: - cognitive: 0.5 - ftol: -.inf - inertia: 0.9 - n_particles: 50 - social: 0.3 - initialize: # The method used to generate where walkers are initialized in parameter space {prior | ball}. - method: ball # priors: samples are initialized by randomly drawing from each parameter's prior. ball: samples are initialized by randomly drawing unit values from a narrow uniform distribution. - ball_lower_limit: 0.49 # The lower limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method. - ball_upper_limit: 0.51 # The upper limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method. - parallel: - number_of_cores: 1 # The number of cores the search is parallelized over by default, using Python multiprocessing. - printing: - silence: false # If True, the default print output of the non-linear search is silcened and not printed by the Python interpreter. - iterations_per_full_update: 500 # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow. - iterations_per_quick_update: 500 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit. - remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable). -PySwarmsLocal: - run: - iters: 2000 - search: - cognitive: 0.5 - ftol: -.inf - inertia: 0.9 - minkowski_p_norm: 2 - n_particles: 50 - number_of_k_neighbors: 3 - social: 0.3 - initialize: # The method used to generate where walkers are initialized in parameter space {prior | ball}. - method: ball # priors: samples are initialized by randomly drawing from each parameter's prior. ball: samples are initialized by randomly drawing unit values from a narrow uniform distribution. - ball_lower_limit: 0.49 # The lower limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method. - ball_upper_limit: 0.51 # The upper limit of the uniform distribution unit values are drawn from when initializing walkers using the ball method. - parallel: - number_of_cores: 1 # The number of cores the search is parallelized over by default, using Python multiprocessing. - printing: - silence: false # If True, the default print output of the non-linear search is silcened and not printed by the Python interpreter. - iterations_per_full_update: 500 # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow. - iterations_per_quick_update: 500 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit. - remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable). LBFGS: search: tol: null diff --git a/config/non_linear/nest.yaml b/config/non_linear/nest.yaml index ac061d4..324654c 100755 --- a/config/non_linear/nest.yaml +++ b/config/non_linear/nest.yaml @@ -2,7 +2,6 @@ # - Nautilus https://https://github.com/johannesulf/nautilus / https://nautilus-sampler.readthedocs.io/en/stable/index.html # - Dynesty: https://github.com/joshspeagle/dynesty / https://dynesty.readthedocs.io/en/latest/index.html -# - UltraNest: https://github.com/JohannesBuchner/UltraNest / https://johannesbuchner.github.io/UltraNest/readme.html # Settings in the [search] and [run] entries are specific to each nested algorithm and should be determined by # consulting that MCMC method's own readthedocs. Nautilus: @@ -90,51 +89,3 @@ DynestyDynamic: printing: silence: false # If True, the default print output of the non-linear search is silenced and not printed by the Python interpreter. -UltraNest: - search: - draw_multiple: true - ndraw_max: 65536 - ndraw_min: 128 - num_bootstraps: 30 - num_test_samples: 2 - resume: true - run_num: null - storage_backend: hdf5 - vectorized: false - warmstart_max_tau: -1.0 - run: - cluster_num_live_points: 40 - dkl: 0.5 - dlogz: 0.5 - frac_remain: 0.01 - insertion_test_window: 10 - insertion_test_zscore_threshold: 2 - lepsilon: 0.001 - log_interval: null - max_iters: null - max_ncalls: null - max_num_improvement_loops: -1.0 - min_ess: 400 - min_num_live_points: 400 - show_status: true - update_interval_ncall: null - update_interval_volume_fraction: 0.8 - viz_callback: auto - stepsampler: - adaptive_nsteps: false - log: false - max_nsteps: 1000 - nsteps: 25 - region_filter: false - scale: 1.0 - stepsampler_cls: null - initialize: # The method used to generate where walkers are initialized in parameter space {prior}. - method: prior # priors: samples are initialized by randomly drawing from each parameter's prior. - parallel: - number_of_cores: 1 # The number of cores the search is parallelized over by default, using Python multiprocessing. - printing: - silence: false # If True, the default print output of the non-linear search is silenced and not printed by the Python interpreter. - - iterations_per_full_update: 1000 # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow. - iterations_per_quick_update: 500 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit. - remove_state_files_at_end: true # Whether to remove the savestate of the seach (e.g. the Emcee hdf5 file) at the end to save hard-disk space (results are still stored as PyAutoFit pickles and loadable). diff --git a/config/visualize/plots_search.yaml b/config/visualize/plots_search.yaml index 0915cfb..7e61f95 100755 --- a/config/visualize/plots_search.yaml +++ b/config/visualize/plots_search.yaml @@ -10,15 +10,6 @@ emcee: trajectories: true # Output Emcee trajectories figure during a non-linear search fit? nautilus: cornerplot: true # Output Nautilus cornerplot figure during a non-linear search fit? -pyswarms: - contour: true # Output PySwarms contour figure during a non-linear search fit? - cost_history: true # Output PySwarms cost_history figure during a non-linear search fit? - time_series: true # Output PySwarms time_series figure during a non-linear search fit? - trajectories: true # Output PySwarms trajectories figure during a non-linear search fit? -ultranest: - cornerplot: true # Output Ultranest cornerplot figure during a non-linear search fit? - runplot: true # Output Ultranest runplot figure during a non-linear search fit? - traceplot: true # Output Ultranest traceplot figure during a non-linear search fit? zeus: corner: true # Output Zeus corner figure during a non-linear search fit? likelihood_series: true # Output Zeus likelihood series figure during a non-linear search fit?