feat(optimizer): add argument --evolutionary

discopop_library/discopop_optimizer/OptimizerArguments.py

@@ -7,6 +7,7 @@
 # directory for details.
 import os.path
 from dataclasses import dataclass
+from typing import List, Optional
 
 
 @dataclass
@@ -16,6 +17,7 @@ class OptimizerArguments(object):
     verbose: bool
     interactive: bool
     exhaustive: bool
+    evolutionary: Optional[List[str]]
     doall_microbench_file: str
     reduction_microbench_file: str
     allow_nested_parallelism: bool
@@ -24,6 +26,13 @@ class OptimizerArguments(object):
     check_called_function_for_nested_parallelism: bool
 
     def __post_init__(self):
+        # fix correct optimization method
+        if not self.exhaustive:
+            if self.evolutionary == None:
+                self.evolutionary = [str(50), str(5)]
+        elif self.evolutionary != None:
+            self.evolutionary = None
+
         self.__validate()
 
     def __validate(self):

discopop_library/discopop_optimizer/__main__.py

@@ -24,7 +24,9 @@ def parse_args() -> OptimizerArguments:
     parser.add_argument("-v", "--verbose", action="store_true",
         help="Enable verbose output.")
     parser.add_argument("-x", "--exhaustive", action="store_true",
-        help="Enable exhaustive search. By default, an evolutionary search is performed.")
+        help="Enable exhaustive search. By default, an evolutionary search with a population size of 50 and 5 generations is performed.")
+    parser.add_argument("-e", "--evolutionary", type=str, default=None, nargs=2, metavar=("population_size", "generations"),
+        help="Enable evolutionary search. By default, an evolutionary search with a population size of 50 and 5 generations is performed.")
     parser.add_argument(
         "--doall-microbench-file", type=str, default="None",
         help="Do-All microbenchmark results"
@@ -56,6 +58,7 @@ def parse_args() -> OptimizerArguments:
         verbose=arguments.verbose,
         interactive=arguments.interactive,
         exhaustive=arguments.exhaustive,
+        evolutionary=arguments.evolutionary,
         doall_microbench_file=arguments.doall_microbench_file,
         reduction_microbench_file=arguments.reduction_microbench_file,
         allow_nested_parallelism=arguments.allow_nested_parallelism,

discopop_library/discopop_optimizer/optimization/evolutionary_algorithm.py

@@ -43,8 +43,8 @@ def perform_evolutionary_search(
     optimizer_dir: str,
 ) -> Optional[OptimizerOutputPattern]:
     ### SETTINGS
-    population_size = 50
-    generations = 5
+    population_size = int(arguments.evolutionary[0])
+    generations = int(arguments.evolutionary[1])
     selection_strength = 0.85  # 0.8 --> 80% of the population will be selected for the next generation
     crossovers = int(population_size / 10)
     mutations = int(population_size / 10)

discopop_library/discopop_optimizer/optimizer.py

@@ -195,14 +195,16 @@ def run(arguments: OptimizerArguments):
         best_configuration = evaluate_all_decision_combinations(
             experiment, available_decisions, arguments, optimizer_dir
         )
-    else:
+    elif arguments.evolutionary != None:
         # perform evolutionary search
         best_configuration = perform_evolutionary_search(
             experiment,
             available_decisions,
             arguments,
             optimizer_dir,
         )
+    else:
+        raise ValueError("No optimization method specified!")
 
     if best_configuration is not None:
         best_configuration = optimize_updates(experiment, best_configuration, arguments)