MLE conversion complete

.gitignore

@@ -125,13 +125,13 @@ venv.bak/
 .idea
 workspace/output/
 output
-test/optimize/test_fit
+test/mle/test_fit
 test/test_files/text/
 test/
-test_autofit/optimize/test_fit/
+test_autofit/mle/test_fit/
 test_autofit/test_files/text/psycopg2-binary==2.8.1
 test_autofit/test_files/text/
-fit/test_autofit/optimize/test_fit
+fit/test_autofit/mle/test_fit
 *.DS_Store
 
 test_autofit/config/priors/old
@@ -157,7 +157,7 @@ test_autofit/samples.csv
 __MACOSX
 *.swp
 test/autofit/test_fit
-# Byte-compiled / optimized / DLL files
+# Byte-compiled / mled / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
@@ -264,13 +264,13 @@ venv.bak/
 .idea
 workspace/output/
 output
-test/optimize/test_fit
+test/mle/test_fit
 test/test_files/text/
 test/
-test_autofit/optimize/test_fit/
+test_autofit/mle/test_fit/
 test_autofit/test_files/text/psycopg2-binary==2.8.1
 test_autofit/test_files/text/
-fit/test_autofit/optimize/test_fit
+fit/test_autofit/mle/test_fit
 *.DS_Store
 
 test_autofit/config/priors/old

autofit/__init__.py

@@ -75,11 +75,11 @@
 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.optimize.drawer.search import Drawer
-from .non_linear.search.optimize.bfgs.search import BFGS
-from .non_linear.search.optimize.bfgs.search import LBFGS
-from .non_linear.search.optimize.pyswarms.search.globe import PySwarmsGlobal
-from .non_linear.search.optimize.pyswarms.search.local import PySwarmsLocal
+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).
-- ``optimizer.yaml``: Settings default behaviour of optimizer non-linear searches (e.g. PySwarms).
+- ``mle.yaml``: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. PySwarms).

autofit/config/non_linear/optimize.yaml → autofit/config/non_linear/mle.yaml

@@ -1,11 +1,11 @@
 # Configuration files that customize the default behaviour of non-linear searches.
 
-# **PyAutoFit** supports the following optimizer algorithms:
+# **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 optimizers method's own readthedocs.
+# determined by consulting that method's own readthedocs.
 
 PySwarmsGlobal:
   run:

autofit/config/visualize/plots_search.yaml

@@ -2,6 +2,6 @@ nest:
   corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?
 mcmc:
   corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?
-optimize:
+mle:
   subplot_parameters: true   # Output a subplot of the best-fit parameters of the model?
   log_likelihood_vs_iteration: true  # Output a plot of the log likelihood versus iteration number?

autofit/interpolator/covariance.py

@@ -195,8 +195,8 @@ def _relationships_for_value(
         """
         analysis = self._analysis_for_value(value)
         model = self.model(path_relationship_map=path_relationship_map or {})
-        optimizer = DynestyStatic()
-        result = optimizer.fit(model=model, analysis=analysis)
+        search = DynestyStatic()
+        result = search.fit(model=model, analysis=analysis)
         return result.instance
 
     def __getitem__(self, value: Equality) -> float:

autofit/mock.py

@@ -2,7 +2,7 @@
 from autofit.non_linear.mock.mock_result import MockResult
 from autofit.non_linear.mock.mock_result import MockResultGrid
 from autofit.non_linear.mock.mock_search import MockSearch
-from autofit.non_linear.mock.mock_search import MockOptimizer
+from autofit.non_linear.mock.mock_search import MockMLE
 from autofit.non_linear.mock.mock_samples_summary import MockSamplesSummary
 from autofit.non_linear.mock.mock_samples import MockSamples
 from autofit.non_linear.mock.mock_samples import MockSamplesNest

autofit/non_linear/mock/mock_search.py

@@ -157,13 +157,13 @@ def perform_update(self, model, analysis, during_analysis, search_internal=None)
         )
 
 
-class MockOptimizer(MockSearch):
+class MockMLE(MockSearch):
     def __init__(self, **kwargs):
         super().__init__(fit_fast=False, **kwargs)
 
     @property
     def samples_cls(self):
-        return MockOptimizer
+        return MockMLE
 
     def project(
         self, factor_approx: FactorApproximation, status: Status = Status()

autofit/non_linear/paths/directory.py

@@ -358,7 +358,7 @@ def save_all(self, search_config_dict=None, info=None):
         try:
             info_start = self.search.initializer.info_from_model(model=self.model)
             self._save_model_start_point(info=info_start)
-        except NotImplementedError:
+        except (NotImplementedError, AttributeError):
             pass
 
         self.save_json("model", to_dict(self.model))

autofit/non_linear/plot/optimize_plotters.py → autofit/non_linear/plot/mle_plotters.py

@@ -8,7 +8,7 @@
 
 logger = logging.getLogger(__name__)
 
-class OptimizePlotter(SamplesPlotter):
+class MLEPlotter(SamplesPlotter):
 
     def subplot_parameters(self, use_log_y : bool = False, use_last_50_percent : bool = False, **kwargs):
         """

autofit/non_linear/search/optimize/abstract_optimize.py → autofit/non_linear/search/mle/abstract_mle.py

@@ -7,22 +7,22 @@
 from autofit.non_linear.analysis import Analysis
 from autofit.non_linear.search.abstract_search import NonLinearSearch
 from autofit.non_linear.samples import Samples
-from autofit.non_linear.plot.optimize_plotters import OptimizePlotter
+from autofit.non_linear.plot.mle_plotters import MLEPlotter
 from autofit.non_linear.plot.output import Output
 
 
-class AbstractOptimizer(NonLinearSearch, ABC):
+class AbstractMLE(NonLinearSearch, ABC):
     @property
     def config_type(self):
-        return conf.instance["non_linear"]["optimize"]
+        return conf.instance["non_linear"]["mle"]
 
     @property
     def samples_cls(self):
         return Samples
 
     @property
     def plotter_cls(self):
-        return OptimizePlotter
+        return MLEPlotter
 
     def plot_start_point(
         self,
@@ -68,7 +68,7 @@ def plot_start_point(
     def plot_results(self, samples):
 
         def should_plot(name):
-            return conf.instance["visualize"]["plots_search"]["optimize"][name]
+            return conf.instance["visualize"]["plots_search"]["mle"][name]
 
         plotter = self.plotter_cls(
             samples=samples,

autofit/non_linear/search/optimize/bfgs/search.py → autofit/non_linear/search/mle/bfgs/search.py

@@ -4,7 +4,7 @@
 from autofit.database.sqlalchemy_ import sa
 
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
-from autofit.non_linear.search.optimize.abstract_optimize import AbstractOptimizer
+from autofit.non_linear.search.mle.abstract_mle import AbstractMLE
 from autofit.non_linear.analysis import Analysis
 from autofit.non_linear.fitness import Fitness
 from autofit.non_linear.initializer import AbstractInitializer
@@ -16,7 +16,7 @@
 import numpy as np
 
 
-class AbstractBFGS(AbstractOptimizer):
+class AbstractBFGS(AbstractMLE):
     __identifier_fields__ = ()
 
     method = None

autofit/non_linear/search/optimize/drawer/search.py → autofit/non_linear/search/mle/drawer/search.py

@@ -5,12 +5,12 @@
 
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.fitness import Fitness
-from autofit.non_linear.search.optimize.abstract_optimize import AbstractOptimizer
+from autofit.non_linear.search.mle.abstract_mle import AbstractMLE
 from autofit.non_linear.initializer import AbstractInitializer
 from autofit.non_linear.samples import Samples, Sample
 
 
-class Drawer(AbstractOptimizer):
+class Drawer(AbstractMLE):
     __identifier_fields__ = ("total_draws",)
 
     def __init__(

autofit/non_linear/search/optimize/pyswarms/search/abstract.py → autofit/non_linear/search/mle/pyswarms/search/abstract.py

@@ -7,7 +7,7 @@
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.fitness import Fitness
 from autofit.non_linear.initializer import AbstractInitializer
-from autofit.non_linear.search.optimize.abstract_optimize import AbstractOptimizer
+from autofit.non_linear.search.mle.abstract_mle import AbstractMLE
 from autofit.non_linear.samples.sample import Sample
 from autofit.non_linear.samples.samples import Samples
 
@@ -66,7 +66,7 @@ def __call__(self, parameters, *kwargs):
         return np.asarray(figure_of_merit_list)
 
 
-class AbstractPySwarms(AbstractOptimizer):
+class AbstractPySwarms(AbstractMLE):
     def __init__(
         self,
         name: Optional[str] = None,
@@ -79,7 +79,7 @@ def __init__(
         **kwargs
     ):
         """
-        A PySwarms Particle Swarm Optimizer global non-linear search.
+        A PySwarms Particle Swarm MLE global non-linear search.
 
         For a full description of PySwarms, checkout its Github and readthedocs webpages:
 

autofit/non_linear/search/optimize/pyswarms/search/globe.py → autofit/non_linear/search/mle/pyswarms/search/globe.py

@@ -2,7 +2,7 @@
 
 from autofit.database.sqlalchemy_ import sa
 from autofit.non_linear.initializer import AbstractInitializer
-from autofit.non_linear.search.optimize.pyswarms.search.abstract import AbstractPySwarms
+from autofit.non_linear.search.mle.pyswarms.search.abstract import AbstractPySwarms
 
 
 class PySwarmsGlobal(AbstractPySwarms):
@@ -25,7 +25,7 @@ def __init__(
             **kwargs
     ):
         """
-        A PySwarms Particle Swarm Optimizer global non-linear search.
+        A PySwarms Particle Swarm MLE global non-linear search.
 
         For a full description of PySwarms, checkout its Github and readthedocs webpages:
 

autofit/non_linear/search/optimize/pyswarms/search/local.py → autofit/non_linear/search/mle/pyswarms/search/local.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from autofit.database.sqlalchemy_ import sa
-from autofit.non_linear.search.optimize.pyswarms.search.abstract import AbstractPySwarms
+from autofit.non_linear.search.mle.pyswarms.search.abstract import AbstractPySwarms
 
 
 class PySwarmsLocal(AbstractPySwarms):
@@ -25,7 +25,7 @@ def __init__(
             **kwargs
     ):
         """
-        A PySwarms Particle Swarm Optimizer global non-linear search.
+        A PySwarms Particle Swarm MLE global non-linear search.
 
         For a full description of PySwarms, checkout its Github and readthedocs webpages:
 

autofit/plot/__init__.py

@@ -1,5 +1,5 @@
 from autofit.non_linear.plot.samples_plotters import SamplesPlotter
 from autofit.non_linear.plot.mcmc_plotters import MCMCPlotter
-from autofit.non_linear.plot.optimize_plotters import OptimizePlotter
+from autofit.non_linear.plot.mle_plotters import MLEPlotter
 from autofit.non_linear.plot.nest_plotters import NestPlotter
 from autofit.non_linear.plot.output import Output

docs/api/plot.rst

@@ -24,4 +24,4 @@ Plotters
 
    NestPlotter
    MCMCPlotter
-   OptimizePlotter
+   MLEPlotter

docs/api/searches.rst

@@ -4,8 +4,8 @@ Non-Linear Searches
 
 A non-linear search is an algorithm which fits a model to data.
 
-**PyAutoFit** currently supports three types of non-linear search algorithms: nested samplers,
-Markov Chain Monte Carlo (MCMC) and optimizers.
+**PyAutoFit** currently supports three types of non-linear search algorithms: nested samplers (nest),
+Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimators (MLE).
 
 **Examples / Tutorials:**
 
@@ -41,8 +41,8 @@ MCMC
    Emcee
    Zeus
 
-Optimizers
-----------
+Maximum Likelihood Estimators
+-----------------------------
 
 .. currentmodule:: autofit
 
@@ -51,6 +51,8 @@ Optimizers
    :template: custom-class-template.rst
    :recursive:
 
+   BFGS
+   LBFGS
    PySwarmsLocal
    PySwarmsGlobal
 

test_autofit/analysis/test_free_parameter.py

@@ -2,7 +2,7 @@
 
 import autofit as af
 from autofit.non_linear.analysis import FreeParameterAnalysis
-from autofit.non_linear.mock.mock_search import MockOptimizer
+from autofit.non_linear.mock.mock_search import MockMLE
 
 
 def test_copy():
@@ -77,8 +77,8 @@ def make_result(
     combined_analysis,
     model,
 ):
-    optimizer = MockOptimizer()
-    return optimizer.fit(model, combined_analysis)
+    search = MockMLE()
+    return search.fit(model, combined_analysis)
 
 
 @pytest.fixture(autouse=True)

test_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).
-- ``optimizer.yaml``: Settings default behaviour of optimizer non-linear searches (e.g. PySwarms).
+- ``mle.yaml``: Settings default behaviour of maximum likelihood estimator (mle) searches (e.g. PySwarms).

test_autofit/config/non_linear/optimize.yaml → test_autofit/config/non_linear/mle.yaml

@@ -1,22 +1,3 @@
-DownhillSimplex:
-  initialize:
-    method: prior
-  printing:
-    silence: false
-  search:
-    disp: 1
-    ftol: 0.0001
-    full_output: 0
-    maxfun: null
-    maxiter: null
-    retall: 0
-    xtol: 0.0001
-  updates:
-    iterations_per_update: 11
-    log_every_update: 1
-    model_results_every_update: 1
-    remove_state_files_at_end: true
-    visualize_every_update: 1
 Drawer:
   initialize:
     ball_lower_limit: 0.49

test_autofit/config/non_linear/mock.yaml

@@ -1,4 +1,4 @@
-MockOptimizer:
+MockMLE:
   initialize:
     method: prior
   printing: