fix and add adaptive parameters to common_optimizer

golem/core/optimisers/common_optimizer/common_optimizer.py

@@ -100,6 +100,7 @@ def _initial_population(self, evaluator: EvaluationOperator):
     def _evolve_population(self, evaluator: EvaluationOperator) -> PopulationT:
         """ Method realizing full evolution cycle """
         # TODO rebuild population
+        # TODO add iterations limit
 
         self.evaluator = evaluator
 

golem/core/optimisers/common_optimizer/old_config.py

@@ -12,14 +12,24 @@
 from golem.core.optimisers.common_optimizer.nodes.old_regularization import Regularization, RegularizationTask
 from golem.core.optimisers.common_optimizer.nodes.old_selection import Selection, SelectionTask
 from golem.core.optimisers.common_optimizer.runner import ParallelRunner, OneThreadRunner
-from golem.core.optimisers.common_optimizer.scheme import Scheme
+from golem.core.optimisers.common_optimizer.scheme import Scheme, SequentialScheme
 from golem.core.optimisers.common_optimizer.stage import Stage
 from golem.core.optimisers.common_optimizer.task import Task, TaskStatusEnum
+from golem.core.optimisers.common_optimizer.temp.adaptive import AdaptivePopulationSize, AdaptiveParametersTask
 
 default_stages = list()
 
 
 # adaptive parameters
+nodes = [AdaptivePopulationSize()]
+scheme = SequentialScheme(nodes=nodes)
+def adaptive_parameter_updater(finished_tasks, parameters):
+    parameters = finished_tasks[0].update_parameters(parameters)
+    return parameters
+
+default_stages.append(Stage(runner=OneThreadRunner(), nodes=nodes, task_builder=AdaptiveParametersTask,
+                            scheme=scheme, stop_fun=lambda f, a: bool(f),
+                            parameter_updater=adaptive_parameter_updater))
 
 # main evolution process
 class EvolvePopulationTask(ElitismTask, MutationTask,
@@ -41,7 +51,14 @@ def update_parameters(self, parameters: 'CommonOptimizerParameters'):
 nodes = [Elitism(), Mutation(), Crossover(), Regularization(),
          Selection(), Inheritance(), Evaluator()]
 
-stop_fun = lambda f, a: a and len(f) >= a[0].graph_optimizer_params.pop_size
+def stop_fun(finished_tasks, all_tasks):
+    if all_tasks:
+        pop_size = all_tasks[0].graph_generation_params.pop_size
+        if len(finished_tasks) >= pop_size:
+            return True
+    return False
+
+
 
 def parameter_updater(finished_tasks, parameters):
     parameters.new_population = list(chain(*[task.generation for task in finished_tasks]))

golem/core/optimisers/common_optimizer/temp/adaptive.py

@@ -16,11 +16,9 @@ class AdaptiveParametersTask(Task):
 
     def __init__(self, parameters: 'CommonOptimizerParameters'):
         super().__init__()
-        self.parameters = {}
-        for attribute, values in parameters.__dict__.items():
-            if isinstance(values, (OptimizationParameters, GraphGenerationParams, AlgorithmParameters)):
-                self.parameters[attribute] = dict(values.__dict__.items())
-        self.parameters['population'] = parameters.population
+        self.graph_optimizer_params = parameters.graph_optimizer_params
+        self.graph_generation_params = parameters.graph_generation_params
+        self.population = parameters.population
 
     def update_parameters(self, parameters: 'CommonOptimizerParameters') -> 'CommonOptimizerParameters':
         """
@@ -29,11 +27,7 @@ def update_parameters(self, parameters: 'CommonOptimizerParameters') -> 'CommonO
         :param parameters: instance of CommonOptimizerParameters to update
         :return: updated parameters object
         """
-        for attribute, values in self.parameters.items():
-            parameters_obj = getattr(parameters, attribute, None)
-            if parameters_obj:
-                for subattribute, subvalues in values.items():
-                    setattr(parameters_obj, subattribute, subvalues)
+        parameters.graph_generation_params = self.graph_generation_params
         return parameters
 
 
@@ -75,10 +69,10 @@ def __call__(self, task: AdaptiveParametersTask) -> List[AdaptiveParametersTask]
             raise TypeError(f"task should be `AdaptiveParametersTask`, got {type(task)} instead")
         if self._pop_size is None:
             self._pop_size: OldAdaptivePopulationSize = init_adaptive_pop_size(
-                    task.parameters['graph_optimizer_params'],
-                    task.parameters['population']
+                    task.graph_optimizer_params,
+                    task.population
                 )
-        pop_size = self._pop_size.next(task.parameters['population'])
+        pop_size = self._pop_size.next(task.population)
 
-        task.parameters['graph_generation_params']['pop_size'] = pop_size
+        task.graph_generation_params.pop_size = pop_size
         return [task]