upd tutorials

docs/source/tutorials/index.rst

@@ -5,5 +5,5 @@ Tutorials
    :glob:
    :maxdepth: 1
 
-   optimizers
-   tuners
+   optimisation
+   tuning

docs/source/tutorials/optimizers.rst → docs/source/tutorials/optimisation.rst

@@ -1,73 +1,74 @@
 .. role:: raw-html-m2r(raw)
    :format: html
 
-Optimizers
-==========
+Optimisation
+============
 
-To solve the optimization problem, 4 optimizers are available in GEFEST - 2 native and 2 based on GOLEM.
+Optimisers summary
+------------------
+
+To solve the optimisation problem, 4 optimisers are available in GEFEST - 1 native and 2 based on GOLEM.
 All of them have a single interface and can be imported from ``gefest.tools.optimizers``.
 
 .. list-table:: Optimizers comparation
    :header-rows: 1
 
    * - 
      - ``BaseGA``
-     - ``SPEA2``
      - ``StandardOptimizer``
      - ``SurrogateOptimizer``
    * - **Backend**
-     - GEFEST
      - GEFEST
      - GOLEM
      - GOLEM
    * - **Muti objective**
-     - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
    * - **Evolutionary schemes**
-     - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
    * - **Adaptive mutation strategies**
-     - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
-   * - **Surrogate optimization**
-     - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
+   * - **Surrogate optimisation**
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:green;color:white;font-weight:bold">Yes</code>`
 
-Some details
-------------
 
 ``BaseGA`` implements the base genetic algorithm, that performs generation of the initial population,
 crossover and mutation operations, fitness estimation and selection.
 Each of the steps is encapsulated in a separate executor, which allows you to change the logic of individual steps.
-Thus, BaseGA essentially only implements the sequence of their call. 
-
-``SPEA2`` implements Strength Pareto Evolutionary Algorithm 2 for multiobjective optimization.
+Thus, BaseGA essentially only implements the sequence of their call.
 
-``StandardOptimizer`` is a wrapper for GOLEM`s ``EvoGraphOptimizer`` optimizer.
+``StandardOptimizer`` is a wrapper for GOLEM`s ``EvoGraphOptimizer`` optimiser.
 It allows to select different evolutionary schemes, adaptive mutation strategies and some other features.
-To use multiobjective optimization set `golem_selection_type` in ``OptimizationParams`` config to 'spea2'.
+To use multiobjective optimisation set `golem_selection_type` in ``OptimizationParams`` config to 'spea2'.
 
 ``SurrogateOptimizer`` is the extension of ``StandardOptimizer`` with the ability 
 to use a surrogate model to evaluate fitness along with the main estimator.
 
-How to run
-----------
+Selectors summary
+-----------------
+
+``OptimizationParams`` have 3 parameters to configure selection strategy:
+ * ``golem_selection_type`` defines which selection function will be used by GOLEM optimisers. Available values: 'spea2' for multi objective and 'tournament' for single objective problems.
+ * ``selector`` defines which selection function will be used by GEFEST for single objective problems and also for multi objective fitnesses if it possible. Available values: 'tournament_selection' and 'roulette_selection'.
+ * ``multiobjective_selector`` defines which selection function will be used by GEFEST for multiobjective problems. Available values: 'spea2' and 'moead'.
 
-Easiest way to run optimizer described in :ref:`quickstart`.
+How to optimise
+---------------
+
+Easiest way to run optimiser described in :ref:`quickstart`.
 
 If you want to get some more control you can do it in code by import corresponding classes:
 
 .. code-block:: python
 
-   from gefest.tools.optimizers BaseGA, SPEA2, StandardOptimizer, SurrogateOptimizer
+   from gefest.tools.optimizers BaseGA, StandardOptimizer, SurrogateOptimizer
 
    from gefest.core.configs.optimization_params import OptimizationParams
    from gefest.core.geometry.datastructs.structure import Structure
@@ -76,3 +77,6 @@ If you want to get some more control you can do it in code by import correspondi
    
    optimizer = BaseGA(opt_params)
    optimized_population = optimizer.optimize(n_steps=42)
+
+By default initial population generates automatically with sampler from `opt_params`.
+It also can be provided as optional argument for optimiser constructor.

docs/source/tutorials/tuners.rst → docs/source/tutorials/tuning.rst

@@ -1,9 +1,12 @@
 .. role:: raw-html-m2r(raw)
    :format: html
 
-Tuners
+Tuning
 ======
 
+Tuners
+------
+
 GEFEST provides api for 4 tuners from `GOLEM <https://thegolem.readthedocs.io/en/latest/api/tuning.html>`_\ :
 
 .. list-table:: Tuners comparation
@@ -19,7 +22,7 @@ GEFEST provides api for 4 tuners from `GOLEM <https://thegolem.readthedocs.io/en
      - simultaneous
      - sequential
      - simultaneous
-   * - ****Backend**
+   * - **Backend**
      - `iOpt <https://github.com/aimclub/iOpt>`_
      - `Optuna <https://github.com/optuna/optuna>`_
      - `Hyperopt <https://github.com/hyperopt/hyperopt>`_
@@ -30,11 +33,10 @@ GEFEST provides api for 4 tuners from `GOLEM <https://thegolem.readthedocs.io/en
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
      - :raw-html-m2r:`<code style="background:red;color:white;font-weight:bold">No</code>`
 
-
 How to tune
 -----------
 
-To initialize tuner and run tuning, similarly to the optimizers, you need an ``OptimizationParams`` 
+To initialize tuner and run tuning, similarly to the optimisers, you need an ``OptimizationParams`` 
 with defined ``TunerParams`` and also one or more ``Structure`` objects.
 
 More details about ``OptimizationParams`` you can find in `cases` and `API referece` sections of this documentation.

gefest/tools/optimizers/golem_optimizer/standard.py

@@ -2,6 +2,8 @@
 
 from typing import TYPE_CHECKING
 
+from gefest.core.geometry import Structure
+
 if TYPE_CHECKING:
     from gefest.core.configs.optimization_params import OptimizationParams
 
@@ -23,7 +25,7 @@ class StandardOptimizer(Optimizer):
 
     """
 
-    def __init__(self, opt_params: OptimizationParams, **kwargs) -> None:
+    def __init__(self, opt_params: OptimizationParams, initial_population=None, **kwargs) -> None:
         super().__init__(opt_params.log_dispatcher, **kwargs)
         self.opt_params = opt_params
         self.objective = Objective(
@@ -33,9 +35,14 @@ def __init__(self, opt_params: OptimizationParams, **kwargs) -> None:
         self.requirements = map_into_graph_requirements(opt_params)
         self.ggp = map_into_graph_generation_params(opt_params)
         self.gpa = map_into_gpa(opt_params)
-        self.initial_pop = list(
-            map(opt_params.golem_adapter.adapt, opt_params.sampler(opt_params.pop_size)),
-        )
+
+        if initial_population:
+            self.initial_pop = initial_population
+        else:
+            self.initial_pop: list[Structure] = list(
+                map(opt_params.golem_adapter.adapt, opt_params.sampler(opt_params.pop_size)),
+            )
+
         self.__standard_opt = EvoGraphOptimizer(
             objective=self.objective,
             initial_graphs=self.initial_pop,

gefest/tools/optimizers/golem_optimizer/surrogate.py

@@ -25,7 +25,7 @@ class SurrogateOptimizer(Optimizer):
 
     """
 
-    def __init__(self, opt_params: OptimizationParams, **kwargs) -> None:
+    def __init__(self, opt_params: OptimizationParams, initial_population=None, **kwargs) -> None:
         super().__init__(opt_params.log_dispatcher, **kwargs)
         self.opt_params = opt_params
         self.objective = Objective(
@@ -35,9 +35,14 @@ def __init__(self, opt_params: OptimizationParams, **kwargs) -> None:
         self.requirements = map_into_graph_requirements(opt_params)
         self.ggp = map_into_graph_generation_params(opt_params)
         self.gpa = map_into_gpa(opt_params)
-        self.initial_pop = list(
-            map(opt_params.golem_adapter.adapt, opt_params.sampler(opt_params.pop_size)),
-        )
+
+        if initial_population:
+            self.initial_pop = initial_population
+        else:
+            self.initial_pop: list[Structure] = list(
+                map(opt_params.golem_adapter.adapt, opt_params.sampler(opt_params.pop_size)),
+            )
+
         self.__surrogate_opt = SurrogateEachNgenOptimizer(
             objective=self.objective,
             initial_graphs=self.initial_pop,