Fix some pep8 issues

golem/core/optimisers/genetic/gp_optimizer.py

@@ -53,7 +53,8 @@ def __init__(self,
             self.elitism = Elitism(graph_optimizer_params)
             self.operators = [self.regularization, self.selection, self.crossover,
                               self.mutation, self.inheritance, self.elitism]
-            self.reproducer = ReproductionController(graph_optimizer_params, self.selection, self.mutation, self.crossover)
+            self.reproducer = ReproductionController(graph_optimizer_params, self.selection, self.mutation,
+                                                     self.crossover)
 
             # Define adaptive parameters
             self._pop_size: PopulationSize = init_adaptive_pop_size(graph_optimizer_params, self.generations)
@@ -70,7 +71,6 @@ def __init__(self,
             self.initial_individuals = [Individual(graph, metadata=requirements.static_individual_metadata)
                                         for graph in self.initial_graphs]
 
-
     def _initial_population(self, evaluator: EvaluationOperator):
         """ Initializes the initial population """
         # Adding of initial assumptions to history as zero generation

golem/core/optimisers/optimizer.py

@@ -136,6 +136,7 @@ def __init__(self,
 
         self._saved_state_path = saved_state_path
         self._run_id = str(uuid.uuid1())
+
     @property
     def objective(self) -> Objective:
         """Returns Objective of this optimizer with information about metrics used."""
@@ -180,7 +181,6 @@ def _progressbar(self):
             bar = EmptyProgressBar()
         return bar
 
-
     def save(self, saved_state_path):
         """
         Method for serializing and saving a class object to a file using the dill library
@@ -201,7 +201,6 @@ def load(self, saved_state_path):
         with open(saved_state_path, 'rb') as f:
             self.__dict__.update(pickle.load(f))
 
-
     def _find_latest_dir(self, directory: str) -> str:
         return max([os.path.join(directory, d) for d in os.listdir(directory) if os.path.isdir(
             os.path.join(directory, d))], key=os.path.getmtime)

golem/core/optimisers/populational_optimizer.py

@@ -97,8 +97,8 @@ def __init__(self,
             self.generations = GenerationKeeper(self.objective, keep_n_best=requirements.keep_n_best)
             self.timer = OptimisationTimer(timeout=self.requirements.timeout)
 
-            dispatcher_type = MultiprocessingDispatcher if self.requirements.parallelization_mode == 'populational' else \
-                SequentialDispatcher
+            dispatcher_type = MultiprocessingDispatcher if self.requirements.parallelization_mode == 'populational' \
+                else SequentialDispatcher
 
             self.eval_dispatcher = dispatcher_type(adapter=graph_generation_params.adapter,
                                                    n_jobs=requirements.n_jobs,

test/integration/test_saved_state.py

@@ -18,6 +18,7 @@
 def find_latest_file_in_dir(directory: str) -> str:
     return max(glob.glob(os.path.join(directory, '*')), key=os.path.getmtime)
 
+
 def test_saved_state():
     # Set params
     size = 16
@@ -34,7 +35,7 @@ def test_saved_state():
 
     # Setup optimization parameters
     requirements_run_1 = GraphRequirements(timeout=timedelta(minutes=timeout),
-                                     num_of_generations=num_of_generations_run_1)
+                                           num_of_generations=num_of_generations_run_1)
     requirements_run_2 = GraphRequirements(timeout=timedelta(minutes=timeout),
                                            num_of_generations=num_of_generations_run_2)
 
@@ -43,7 +44,7 @@ def test_saved_state():
 
     # Build and run the optimizer to create a saved state file
     optimiser1 = EvoGraphOptimizer(objective, initial_population, requirements_run_1, gen_params, algo_params,
-                                  saved_state_path=saved_state_path)
+                                   saved_state_path=saved_state_path)
     st = time.time()
     optimiser1.optimise(objective, save_state_delta=1)
     et = time.time()
@@ -56,16 +57,16 @@ def test_saved_state():
 
     # Create the optimizer to check that the saved state was used
     optimiser2 = EvoGraphOptimizer(objective, initial_population, requirements_run_2, gen_params, algo_params,
-                                  use_saved_state=True, saved_state_path=saved_state_path)
+                                   use_saved_state=True, saved_state_path=saved_state_path)
 
     # Check that the restored object has the same main parameters as the original or at least the params are not empty
     assert optimiser1.current_generation_num == optimiser2.current_generation_num + 1, \
         f'ERROR: Restored object field \'current_generation_num\' has wrong value: {optimiser2.current_generation_num}'
     assert optimiser1.generations.stagnation_iter_count == optimiser2.generations.stagnation_iter_count + 1, \
         f'ERROR: Restored object field \'generations.stagnation_iter_count\' has wrong value: ' \
         f'{optimiser2.generations.stagnation_iter_count}'
-    assert optimiser1.best_individuals != [], f'ERROR: Restored object field \'best_individuals\' is empty'
-    assert optimiser1.population is not None, f'ERROR: Restored object field \'population\' is empty'
+    assert optimiser1.best_individuals != [], 'ERROR: Restored object field \'best_individuals\' is empty'
+    assert optimiser1.population is not None, 'ERROR: Restored object field \'population\' is empty'
     assert optimiser1.timer.timeout > optimiser2.timer.timeout, 'ERROR: timeout was not adjusted correctly'
 
     st = time.time()