Remove UltraNest and PySwarms, clean up search tests

AGENTS.md

@@ -24,7 +24,7 @@ NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib python -m pytest t
 
 ## Key Architecture
 
-- **Non-linear searches** (`non_linear/search/`): MCMC (emcee), nested sampling (dynesty, nautilus), MLE (LBFGS, pyswarms)
+- **Non-linear searches** (`non_linear/search/`): MCMC (emcee), nested sampling (dynesty, nautilus), MLE (LBFGS, BFGS, drawer)
 - **Model composition** (`mapper/`): `af.Model`, `af.Collection`, prior distributions
 - **Analysis** (`non_linear/analysis/`): base `af.Analysis` class with `log_likelihood_function`
 - **Aggregator** (`aggregator/`): results aggregation across runs

CLAUDE.md

@@ -18,8 +18,8 @@ Shared utilities (e.g. `test_mode`, `jax_wrapper`) belong in autoconf.
 - `autofit/` - Main package
   - `non_linear/` - Non-linear search algorithms
     - `search/mcmc/` - MCMC (emcee, zeus)
-    - `search/mle/` - Maximum likelihood (LBFGS, pyswarms)
-    - `search/nest/` - Nested sampling (dynesty, nautilus, ultranest)
+    - `search/mle/` - Maximum likelihood (LBFGS, BFGS, drawer)
+    - `search/nest/` - Nested sampling (dynesty, nautilus)
     - `samples/` - Posterior samples handling
     - `paths/` - Output path management
     - `analysis/` - Analysis base classes
@@ -39,11 +39,10 @@ Shared utilities (e.g. `test_mode`, `jax_wrapper`) belong in autoconf.
 
 - `dynesty==2.1.4` - Nested sampling
 - `emcee>=3.1.6` - MCMC
-- `pyswarms==1.3.0` - Particle swarm optimisation
 - `scipy<=1.14.0` - Optimisation
 - `SQLAlchemy==2.0.32` - Database backend
 - `anesthetic==2.8.14` - Posterior analysis/plotting
-- Optional: `nautilus-sampler`, `ultranest`, `zeus-mcmc`, `getdist`
+- Optional: `nautilus-sampler`, `zeus-mcmc`, `getdist`
 
 ## Running Tests
 

autofit/__init__.py

@@ -82,12 +82,9 @@
 from .non_linear.search.nest.nautilus.search import Nautilus
 from .non_linear.search.nest.dynesty.search.dynamic import DynestyDynamic
 from .non_linear.search.nest.dynesty.search.static import DynestyStatic
-from .non_linear.search.nest.ultranest.search import UltraNest
 from .non_linear.search.mle.drawer.search import Drawer
 from .non_linear.search.mle.bfgs.search import BFGS
 from .non_linear.search.mle.bfgs.search import LBFGS
-from .non_linear.search.mle.pyswarms.search.globe import PySwarmsGlobal
-from .non_linear.search.mle.pyswarms.search.local import PySwarmsLocal
 from .non_linear.paths.abstract import AbstractPaths
 from .non_linear.paths import DirectoryPaths
 from .non_linear.paths import DatabasePaths

autofit/config/non_linear/README.rst

@@ -6,4 +6,4 @@ Files
 
 - ``mcmc.yaml``: Settings default behaviour of MCMC non-linear searches (e.g. Emcee).
 - ``nest.yaml``: Settings default behaviour of nested sampler non-linear searches (e.g. Dynesty).
-- ``mle.yaml``: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. PySwarms).
+- ``mle.yaml``: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. BFGS).

autofit/config/non_linear/mle.yaml

@@ -2,53 +2,9 @@
 
 # **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
+# Settings in the [search], [run] and [options] entries are specific to each 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).
 BFGS:
   search:
     tol: null

autofit/config/non_linear/nest.yaml

@@ -2,7 +2,6 @@
 
 # - Dynesty: https://github.com/joshspeagle/dynesty / https://dynesty.readthedocs.io/en/latest/index.html
 # - Nautilus https://https://github.com/johannesulf/nautilus / https://nautilus-sampler.readthedocs.io/en/stable/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.
@@ -87,47 +86,3 @@ Nautilus:
     force_x1_cpu: false             # Force Dynesty to not use Python multiprocessing Pool, which can fix issues on certain operating systems.
   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.

autofit/non_linear/samples/samples.py

@@ -39,7 +39,7 @@ def __init__(
         individual sample by the `NonLinearSearch` and return information on the likelihoods, errors, etc.
 
         This class stores samples of searches which provide maximum likelihood estimates of the  model-fit (e.g.
-        PySwarms, LBFGS).
+        LBFGS).
 
         Parameters
         ----------