Integrated seeding. Modify fitness function of exp 5 for simplified i…

…ntro example.

default_experiments.py

@@ -177,7 +177,7 @@ def getExp5() -> Experiment:
                               "velocity_ego",
                               "orientation_ped",
                               "velocity_ped"],
-                          fitness_function=FitnessMinDistanceVelocityFrontOnly(),
+                          fitness_function=FitnessMinDistanceVelocity(),
                           critical_function=CriticalAdasDistanceVelocity(),
                           simulate_function=DummySimulator.simulate,
                           simulation_time=10,

opensbt/algorithm/nsga2_optimizer.py

@@ -42,7 +42,8 @@ def __init__(self,
             "Crossover probability" : str(config.prob_crossover),
             "Crossover eta" : str(config.eta_crossover),
             "Mutation probability" : str(config.prob_mutation),
-            "Mutation eta" : str(config.eta_mutation)
+            "Mutation eta" : str(config.eta_mutation),
+            "Seed" : str(config.seed)
         }
 
         self.algorithm = NSGA2(

opensbt/algorithm/nsga2dt_optimizer.py

@@ -1,3 +1,4 @@
+import random
 from opensbt.model_ga.result  import SimulationResult
 from opensbt.evaluation.critical import *
 from pymoo.termination import get_termination
@@ -64,6 +65,8 @@ def run(self) -> SimulationResult:
         '''Initial conditions (initial region)'''
         xl = problem.xl
         xu = problem.xu
+
+        random.seed(config.seed)
 
         # sampling = FloatRandomSampling()
         sampling = LHS()  # Latin Hypercube Sampling
@@ -137,7 +140,7 @@ def run(self) -> SimulationResult:
                 res = minimize(sub_problem,
                                algorithm,
                                termination,
-                               seed=1,
+                               seed=config.seed,
                                save_history=True,
                                verbose=True)
 
@@ -178,7 +181,9 @@ def run(self) -> SimulationResult:
             "Crossover probability": str(config.prob_crossover),
             "Crossover eta": str(config.eta_crossover),
             "Mutation probability": str(config.prob_mutation),
-            "Mutation eta": str(config.eta_mutation)}
+            "Mutation eta": str(config.eta_mutation),
+            "Seed" : str(config.seed)
+        }
 
         result = self._create_result(problem, hist_holder, inner_algorithm, execution_time)
         self.res = result

opensbt/algorithm/optimizer.py

@@ -10,6 +10,8 @@ class Optimizer(ABC):
     algorithm_name: str
 
     parameters: str
+
+    config: SearchConfiguration
 
     @abstractmethod
     def __init__(self, problem: SimulationProblem, config: SearchConfiguration):
@@ -21,4 +23,5 @@ def run(self) -> SimulationResult:
                 self.algorithm,
                 self.termination,
                 save_history=self.save_history,
-                verbose=True)
+                verbose=True,
+                seed = self.config.seed)

opensbt/algorithm/ps.py

@@ -22,6 +22,8 @@
 from pathlib import Path
 from typing import List
 
+import random
+
 from pymoo.optimize import minimize
 from pymoo.visualization.scatter import Scatter
 from pymoo.core.problem import Problem
@@ -63,7 +65,9 @@ def __init__(self,
         log.info(f"Initialized algorithm with config: {config.__dict__}")
 
     def run(self) -> SimulationResult:
-
+
+        random.seed(config.seed)
+
         problem = self.problem
         sample_size = self.sample_size
         sampled = self.sampling_type()(problem,sample_size)

opensbt/algorithm/ps_rand.py

@@ -28,6 +28,7 @@ def __init__(self,
                 problem: Problem,
                 config: SearchConfiguration,
                 sampling_type = FloatRandomSampling):
+
         super().__init__(
             problem = problem,
             config = config,

opensbt/experiment/search_configuration.py

@@ -18,6 +18,8 @@ class SearchConfiguration(object):
     nadir = None
     ideal = None
 
+    seed = None
+
 
 #TODO create a search configuration file specific for each algorithm
 class DefaultSearchConfiguration(SearchConfiguration):
@@ -38,3 +40,5 @@ class DefaultSearchConfiguration(SearchConfiguration):
     # metrics
     ref_point_hv = None
     nadir = ref_point_hv
+
+    seed = None

run.py

@@ -71,6 +71,8 @@
                     help='Whether to use the simuator\'s visualization. This feature is useful for debugging and demonstrations, however it reduces the search performance.')
 parser.add_argument('-info', dest='show_info', action='store_true',
                     help='Names of all defined experiments.')
+parser.add_argument('-s', dest='seed', action='store', default=None, type=int,
+                    help='Seed for randomized operations.')
 
 args = parser.parse_args()
 
@@ -149,6 +151,8 @@
     problem.design_names = args.design_names
 if not args.do_visualize is None:
     problem.do_visualize = args.do_visualize
+if not args.seed is None:
+    config.seed = args.seed
 
 ####### Run algorithm