WIP direct tree in manager_pop. Also check previous commit

experiments/examples/manager_pop.py

@@ -11,14 +11,16 @@
 )
 from pyrevolve.evolution.selection import multiple_selection, tournament_selection
 from pyrevolve.experiment_management import ExperimentManagement
-from pyrevolve.genotype.plasticoding import PlasticodingConfig
-from pyrevolve.genotype.plasticoding.crossover.crossover import CrossoverConfig
-from pyrevolve.genotype.plasticoding.crossover.standard_crossover import (
-    standard_crossover,
+from pyrevolve.genotype.direct_tree.direct_tree_config import DirectTreeGenotypeConfig
+from pyrevolve.genotype.direct_tree.direct_tree_crossover import (
+    crossover_list as direct_tree_crossover_list,
+)
+from pyrevolve.genotype.direct_tree.direct_tree_genotype import (
+    Genotype as DirectTreeGenotype,
+)
+from pyrevolve.genotype.direct_tree.direct_tree_mutation import (
+    mutate as direct_tree_mutate,
 )
-from pyrevolve.genotype.plasticoding.initialization import random_initialization
-from pyrevolve.genotype.plasticoding.mutation.mutation import MutationConfig
-from pyrevolve.genotype.plasticoding.mutation.standard_mutation import standard_mutation
 from pyrevolve.util.supervisor.analyzer_queue import AnalyzerQueue
 from pyrevolve.util.supervisor.simulator_queue import SimulatorQueue
 
@@ -33,23 +35,27 @@ async def run():
     population_size = 100
     offspring_size = 50
 
-    genotype_conf = PlasticodingConfig(
-        max_structural_modules=20,
-        allow_vertical_brick=True,
-        use_movement_commands=True,
-        use_rotation_commands=False,
-        use_movement_stack=True,
-    )
-
-    mutation_conf = MutationConfig(
-        mutation_prob=0.8,
-        genotype_conf=genotype_conf,
+    genotype_conf: DirectTreeGenotypeConfig = DirectTreeGenotypeConfig(
+        max_parts=50,
+        min_parts=10,
+        max_oscillation=5,
+        init_n_parts_mu=10,
+        init_n_parts_sigma=4,
+        init_prob_no_child=0.1,
+        init_prob_child_block=0.4,
+        init_prob_child_active_joint=0.5,
+        mutation_p_duplicate_subtree=0.2,
+        mutation_p_delete_subtree=0.2,
+        mutation_p_generate_subtree=0.2,
+        mutation_p_swap_subtree=0.2,
+        mutation_p_mutate_oscillators=0.5,
+        mutation_p_mutate_oscillator=0.5,
+        mutate_oscillator_amplitude_sigma=0.3,
+        mutate_oscillator_period_sigma=0.3,
+        mutate_oscillator_phase_sigma=0.3,
     )
-
-    crossover_conf = CrossoverConfig(
-        crossover_prob=0.8,
-    )
-    # experiment params #
+    mutation_conf = genotype_conf
+    crossover_conf = genotype_conf
 
     # Parse command line / file input arguments
     settings = parser.parse_args()
@@ -79,20 +85,27 @@ async def run():
 
     population_conf = PopulationConfig(
         population_size=population_size,
-        genotype_constructor=random_initialization,
+        genotype_constructor=lambda conf, _id: DirectTreeGenotype(
+            conf, _id, random_init=True
+        ),
         genotype_conf=genotype_conf,
         fitness_function=fitness.displacement_velocity,
-        mutation_operator=standard_mutation,
+        mutation_operator=lambda genotype, gen_conf: direct_tree_mutate(
+            genotype, gen_conf, in_place=False
+        ),
         mutation_conf=mutation_conf,
-        crossover_operator=standard_crossover,
+        crossover_operator=lambda parents, gen_conf, _cross_conf: direct_tree_crossover_list(
+            [p.genotype for p in parents], gen_conf
+        ),
         crossover_conf=crossover_conf,
         selection=lambda individuals: tournament_selection(individuals, 2),
         parent_selection=lambda individuals: multiple_selection(
             individuals, 2, tournament_selection
         ),
         population_management=steady_state_population_management,
-        population_management_selector=tournament_selection,
+        population_management_selector=None,
         evaluation_time=settings.evaluation_time,
+        grace_time=settings.grace_time,
         offspring_size=offspring_size,
         experiment_name=settings.experiment_name,
         experiment_management=experiment_management,