fix some errors

golem/core/optimisers/common_optimizer/common_optimizer.py

@@ -52,7 +52,7 @@ class CommonOptimizer(PopulationalOptimizer):
                           'graph_optimizer_params', 'history', 'stages', '_run',
                           'generations', 'population', 'evaluator')
     __parameters_allowed_to_change = ('requirements', 'graph_generation_params',
-                                      'graph_optimizer_params', 'stages', '_run')
+                                      'graph_optimizer_params', 'stages', '_run', 'new_population')
 
     def __init__(self,
                  objective: Objective,
@@ -85,7 +85,8 @@ def parameters(self, parameters: CommonOptimizerParameters):
         if not isinstance(parameters, CommonOptimizerParameters):
             raise TypeError(f"parameters should be `CommonOptimizerParameters`, got {type(parameters)} instead")
         for attr in self.__parameters_allowed_to_change:
-            setattr(self, attr, getattr(parameters, attr))
+            if hasattr(parameters, attr):
+                setattr(self, attr, getattr(parameters, attr))
 
     # def optimise(self, objective: ObjectiveFunction):
     #     while self._run:
@@ -98,9 +99,10 @@ def _initial_population(self, evaluator: EvaluationOperator):
 
     def _evolve_population(self, evaluator: EvaluationOperator) -> PopulationT:
         """ Method realizing full evolution cycle """
+        # TODO rebuild population
 
         self.evaluator = evaluator
 
         for i_stage in range(len(self.stages)):
             self.parameters = self.stages[i_stage].run(self.parameters)
-        print(1)
+        return self.new_population

golem/core/optimisers/common_optimizer/nodes/evaluator.py

@@ -7,6 +7,7 @@ class EvaluatorTask(TaskMixin):
     def __init__(self, parameters: 'CommonOptimizerParameters'):
         super().__init__(parameters)
         self.evaluator = parameters.evaluator
+        self.generation = parameters.population
 
     def update_parameters(self, parameters: 'CommonOptimizerParameters'):
         return super().update_parameters(parameters)

golem/core/optimisers/common_optimizer/old_config.py

@@ -2,6 +2,7 @@
     that reproduces behavior of default GOLEM
     genetic optimization """
 from collections import defaultdict
+from itertools import chain
 
 from golem.core.optimisers.common_optimizer.nodes.evaluator import Evaluator
 from golem.core.optimisers.common_optimizer.nodes.old_crossover import Crossover, CrossoverTask
@@ -43,10 +44,10 @@ def update_parameters(self, parameters: 'CommonOptimizerParameters'):
 stop_fun = lambda f, a: a and len(f) >= a[0].graph_optimizer_params.pop_size
 
 def parameter_updater(finished_tasks, parameters):
-    parameters.new_population = [task.generation for task in finished_tasks]
+    parameters.new_population = list(chain(*[task.generation for task in finished_tasks]))
     return parameters
 
 runner = OneThreadRunner()
-# runner = ParallelRunner()
+runner = ParallelRunner()
 default_stages.append(Stage(runner=runner, nodes=nodes, task_builder=EvolvePopulationTask,
                             scheme=scheme, stop_fun=stop_fun, parameter_updater=parameter_updater))

golem/core/optimisers/common_optimizer/runner.py

@@ -1,6 +1,7 @@
 import time
 from abc import abstractmethod
 from collections import deque
+from copy import copy
 from dataclasses import dataclass
 from multiprocessing import Manager
 from queue import Empty
@@ -82,7 +83,7 @@ def __init__(self,
                  worker_cycle_sleep_seconds: float = 0.02,
                  **kwargs):
         super().__init__(*args, **kwargs)
-        self.n_jobs = n_jobs
+        self.n_jobs = determine_n_jobs(n_jobs)
         self.main_cycle_sleep_seconds = main_cycle_sleep_seconds
         self.worker_cycle_sleep_seconds = worker_cycle_sleep_seconds
 
@@ -93,7 +94,7 @@ def run(self, scheme: Scheme, task: Task, nodes: List[Node], stop_fun: Callable)
                             queued_tasks=queued_tasks, processed_tasks=processed_tasks,
                             sleep_time=self.worker_cycle_sleep_seconds)
             with Parallel(n_jobs=self.n_jobs, prefer='processes', return_as='generator') as parallel:
-                _ = parallel(delayed(worker)(randint(0, int(2 ** 32 - 1))) for _ in range(self.n_jobs))
+                gen = parallel(delayed(worker)(randint(0, int(2 ** 32 - 1))) for _ in range(self.n_jobs))
                 finished_tasks, all_tasks = list(), list()
                 while not stop_fun(finished_tasks, all_tasks):
                     time.sleep(self.main_cycle_sleep_seconds)