Review Multiobjective compatibility

README.md

@@ -67,6 +67,8 @@ If you want to contribute to Revolve, be sure to review the [contribution
 guidelines](CONTRIBUTING.md). By participating, you are expected to
 uphold this code.
 
+[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/ci-group/revolve)
+
 We use [GitHub issues](https://github.com/ci-group/revolve/issues) for
 tracking requests and bugs.
 

experiments/Environmental_influences_on_evolvable_robots/baseline2.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+import asyncio
+
+from pyrevolve import parser
+from pyrevolve.evolution import fitness
+from pyrevolve.evolution.selection import multiple_selection, tournament_selection
+from pyrevolve.evolution.population import Population, PopulationConfig
+from pyrevolve.evolution.pop_management.steady_state import steady_state_population_management
+from pyrevolve.experiment_management import ExperimentManagement
+from pyrevolve.genotype.plasticoding.crossover.crossover import CrossoverConfig
+from pyrevolve.genotype.plasticoding.crossover.standard_crossover import standard_crossover
+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.genotype.plasticoding.plasticoding import PlasticodingConfig
+from pyrevolve.tol.manage import measures
+from pyrevolve.util.supervisor.simulator_queue import SimulatorQueue
+from pyrevolve.util.supervisor.analyzer_queue import AnalyzerQueue
+from pyrevolve.custom_logging.logger import logger
+import sys
+
+async def run():
+    """
+    The main coroutine, which is started below.
+    """
+
+    # experiment params #
+    num_generations = 100
+    population_size = 100
+    offspring_size = 50
+    front = 'total_slaves'
+
+    # environment world and z-start
+    environments = {'plane': 0.03,
+                    'tilted5': 0.1
+                    }
+
+    genotype_conf = PlasticodingConfig(
+        max_structural_modules=15,
+        plastic=False,
+    )
+
+    mutation_conf = MutationConfig(
+        mutation_prob=0.8,
+        genotype_conf=genotype_conf,
+    )
+
+    crossover_conf = CrossoverConfig(
+        crossover_prob=0.8,
+    )
+    # experiment params #
+
+    # Parse command line / file input arguments
+    settings = parser.parse_args()
+    experiment_management = ExperimentManagement(settings, environments)
+    do_recovery = settings.recovery_enabled and not experiment_management.experiment_is_new()
+
+    logger.info('Activated run '+settings.run+' of experiment '+settings.experiment_name)
+
+    if do_recovery:
+        gen_num, has_offspring, next_robot_id = experiment_management.read_recovery_state(population_size,
+                                                                                          offspring_size)
+
+        if gen_num == num_generations-1:
+            logger.info('Experiment is already complete.')
+            return
+    else:
+        gen_num = 0
+        next_robot_id = 1
+
+    def fitness_function_plane(robot_manager, robot):
+        #contacts = measures.contacts(robot_manager, robot)
+        #assert(contacts != 0)
+        return fitness.displacement_velocity_hill(robot_manager, robot)
+
+
+    fitness_function = {'plane': fitness_function_plane,
+                        'tilted5': fitness_function_plane}
+
+    population_conf = PopulationConfig(
+        population_size=population_size,
+        genotype_constructor=random_initialization,
+        genotype_conf=genotype_conf,
+        fitness_function=fitness_function,
+        mutation_operator=standard_mutation,
+        mutation_conf=mutation_conf,
+        crossover_operator=standard_crossover,
+        crossover_conf=crossover_conf,
+        selection=lambda individuals: tournament_selection(individuals, environments, 2),
+        parent_selection=lambda individuals: multiple_selection(individuals, 2, tournament_selection, environments),
+        population_management=steady_state_population_management,
+        population_management_selector=tournament_selection,
+        evaluation_time=settings.evaluation_time,
+        offspring_size=offspring_size,
+        experiment_name=settings.experiment_name,
+        experiment_management=experiment_management,
+        environments=environments,
+        front=front
+    )
+
+    settings = parser.parse_args()
+
+    simulator_queue = {}
+    analyzer_queue = None
+
+    previous_port = None
+    for environment in environments:
+
+        settings.world = environment
+        settings.z_start = environments[environment]
+
+        if previous_port is None:
+            port = settings.port_start
+            previous_port = port
+        else:
+            port = previous_port+settings.n_cores
+            previous_port = port
+
+        simulator_queue[environment] = SimulatorQueue(settings.n_cores, settings, port)
+        await simulator_queue[environment].start()
+
+    analyzer_queue = AnalyzerQueue(1, settings, port+settings.n_cores)
+    await analyzer_queue.start()
+
+    population = Population(population_conf, simulator_queue, analyzer_queue, next_robot_id)
+
+    if do_recovery:
+
+        if gen_num >= 0:
+            # loading a previous state of the experiment
+            await population.load_snapshot(gen_num)
+            logger.info('Recovered snapshot '+str(gen_num)+', pop with ' + str(len(population.individuals))+' individuals')
+
+        if has_offspring:
+            individuals = await population.load_offspring(gen_num, population_size, offspring_size, next_robot_id)
+            gen_num += 1
+            logger.info('Recovered unfinished offspring '+str(gen_num))
+
+            if gen_num == 0:
+                await population.init_pop(individuals)
+            else:
+                population = await population.next_gen(gen_num, individuals)
+
+            experiment_management.export_snapshots(population.individuals, gen_num)
+    else:
+        # starting a new experiment
+        experiment_management.create_exp_folders()
+        await population.init_pop()
+        experiment_management.export_snapshots(population.individuals, gen_num)
+
+    while gen_num < num_generations-1:
+        gen_num += 1
+        population = await population.next_gen(gen_num)
+        experiment_management.export_snapshots(population.individuals, gen_num)
+
+    # output result after completing all generations...

experiments/Environmental_influences_on_evolvable_robots/bestrobots_summary_renders_journal1_tilted.r

@@ -0,0 +1,86 @@
+
+library(sqldf)
+require('magick')
+
+##### change paths/labels/params here #####
+
+
+paths = c( 'flat', 'tilted', 'baseline2')
+
+environments = list(c( 'plane'),
+                    c( 'tilted5'),
+                    c( 'plane')
+)
+
+base_directory <- paste('data/', sep='')
+
+experiments = c(1:20)
+gens = 100
+pop = 100
+num_top = 1
+
+analysis = 'analysis_journal1_tilted'
+
+##### change paths/labels/params here #####
+
+output_directory = paste(base_directory,analysis, sep='')
+
+
+file <-file(paste(output_directory,'/best.txt',sep=''), open="w")
+
+# for each method
+for(m in 1:length(paths))
+{
+    # for each repetition
+    for (exp in experiments) 
+      {
+
+        input_directory1  <-    paste(base_directory, paths[m],'_',exp, '/data_fullevolution/',environments[[m]][1],sep='')
+        input_directory2  <-    paste(base_directory, paths[m],'_',exp, '/selectedpop_', sep='')
+
+        ids_gens = data.frame()
+        list = strsplit(list.files(paste(input_directory2, environments[[m]][1],'/selectedpop_',gens-1, sep='')), ' ')
+        for(geno in 1:pop)
+        {
+          genome =  data.frame(cbind(c(gens), c(strsplit(strsplit(list [[geno]],'_')[[1]][3],'.png')[[1]][1] )))
+          names(genome)<-c('generation','robot_id')
+          ids_gens = rbind(ids_gens,genome)
+        }
+
+        measures   =  read.table(paste(input_directory1,"/all_measures.tsv", sep=''), header = TRUE)
+        bests =  sqldf(paste("select robot_id, cons_fitness from measures inner join ids_gens using (robot_id) order by cons_fitness desc limit",num_top))
+
+        for(b in 1:nrow(bests))
+        {
+
+           writeLines( paste(paths[m],'exp',exp,bests[b,'robot_id'] ,bests[b,'cons_fitness'] ), file )
+          print( paste(paths[m],'exp',exp,bests[b,'robot_id'] ,bests[b,'cons_fitness'] ))
+
+           phenotype= bests[b,'robot_id'] 
+
+           for (env in 1:length(environments[[m]]))
+           {
+               patha = paste(input_directory2, environments[[m]][env], "/selectedpop_",gens-1,sep="")
+
+               body <- list.files(patha, paste("body_robot_",phenotype,".png$",sep=""), full.names = TRUE)
+               body = image_read(body)
+               body = image_scale(body, "100x100")
+               body = image_border(image_background(body, "white"), "white", "5x5")
+
+              if(b == 1 && env == 1)
+              {
+                bodies = body
+              }else{
+                bodies = c(bodies, body)
+              }
+          }
+        }
+
+        side_by_side = image_append(bodies, stack=F)
+        image_write(side_by_side, path = paste(output_directory,"/",paths[m],'_', environments[[m]][env], "_bodies_best_",exp,".png",sep=''), format = "png")
+
+    }
+}
+
+
+close(file)

experiments/Environmental_influences_on_evolvable_robots/consolidate_experiments.py

@@ -0,0 +1,138 @@
+import os
+import math
+
+# set these variables according to your experiments #
+dirpath = 'data/'
+experiments_type = [
+       'baseline2',
+       'flat',
+       'tilted'
+]
+environments = {
+                 'baseline2': ['plane','tilted5'],
+                  'flat': ['plane'],
+                  'tilted': ['tilted5']                 }
+
+runs = range(1, 21)
+
+
+# set these variables according to your experiments #
+
+def build_headers(path1, path2):
+
+    file_summary = open(path1 + "/all_measures.tsv", "w+")
+    file_summary.write('robot_id\t')
+
+    behavior_headers = []
+    behavior_headers.append('velocity')
+    file_summary.write(behavior_headers[-1]+'\t')
+    behavior_headers.append('displacement_velocity')
+    file_summary.write(behavior_headers[-1]+'\t')
+    behavior_headers.append('displacement_velocity_hill')
+    file_summary.write(behavior_headers[-1]+'\t')
+    behavior_headers.append('head_balance')
+    file_summary.write(behavior_headers[-1]+'\t')
+    behavior_headers.append('contacts')
+    file_summary.write(behavior_headers[-1]+'\t')
+    # use this instead? but what if the guy is none?
+    # with open(path + '/data_fullevolution/descriptors/behavior_desc_robot_1.txt') as file:
+    #     for line in file:
+    #         measure, value = line.strip().split(' ')
+    #         behavior_headers.append(measure)
+    #         file_summary.write(measure+'\t')
+
+    phenotype_headers = []
+    with open(path1 + '/descriptors/phenotype_desc_robot_1.txt') as file:
+        for line in file:
+            measure, value = line.strip().split(' ')
+            phenotype_headers.append(measure)
+            file_summary.write(measure+'\t')
+    file_summary.write('fitness\t cons_fitness\n')
+    file_summary.close()
+
+    file_summary = open(path2 + "/snapshots_ids.tsv", "w+")
+    file_summary.write('generation\trobot_id\n')
+    file_summary.close()
+
+    return behavior_headers, phenotype_headers
+
+for exp in experiments_type:
+
+    for env in environments[exp]:
+
+        for run in runs:
+
+            path0 = dirpath + str(exp) + '_' + str(run) + '/data_fullevolution'
+            path1 = dirpath + str(exp) + '_' + str(run) + '/data_fullevolution/' + env
+            path2 = dirpath + str(exp) + '_' + str(run) + '/selectedpop_' + env
+
+            behavior_headers, phenotype_headers = build_headers(path1, path2)
+
+            file_summary = open(path1 + "/all_measures.tsv", "a")
+            for r, d, f in os.walk(path0+'/consolidated_fitness'):
+                for file in f:
+
+                    robot_id = file.split('.')[0].split('_')[-1]
+                    file_summary.write(robot_id+'\t')
+
+                    bh_file = path1+'/descriptors/behavior_desc_robot_'+robot_id+'.txt'
+                    if os.path.isfile(bh_file):
+                        with open(bh_file) as file:
+                            for line in file:
+                                if line != 'None':
+                                    measure, value = line.strip().split(' ')
+                                    file_summary.write(value+'\t')
+                                else:
+                                    for h in behavior_headers:
+                                        file_summary.write('None'+'\t')
+                    else:
+                        for h in behavior_headers:
+                            file_summary.write('None'+'\t')
+
+                    pt_file = path1+'/descriptors/phenotype_desc_robot_'+robot_id+'.txt'
+                    if os.path.isfile(pt_file):
+                        with open(pt_file) as file:
+                            for line in file:
+                                measure, value = line.strip().split(' ')
+                                file_summary.write(value+'\t')
+                    else:
+                        for h in phenotype_headers:
+                            file_summary.write('None'+'\t')
+
+                    f_file = open(path1+'/fitness/fitness_robot_'+robot_id+'.txt', 'r')
+                    fitness = f_file.read()
+                    file_summary.write(fitness + '\t')
+
+                    cf_file = open(path0+'/consolidated_fitness/consolidated_fitness_robot_'+robot_id+'.txt', 'r')
+                    cons_fitness = cf_file.read()
+                    file_summary.write(cons_fitness + '\n')
+
+            num_files = len(f)
+            list_gens = []
+            for r, d, f in os.walk(path2):
+                for dir in d:
+                    if 'selectedpop' in dir:
+                        gen = dir.split('_')[1]
+                        list_gens.append(int(gen))
+            list_gens.sort()
+            if len(list_gens)>0:
+                gen = list_gens[-1]
+            else:
+                gen = -1
+            print(exp, run, num_files, gen, num_files-(gen*50+100))
+
+            file_summary.close()
+
+            file_summary = open(path2 + "/snapshots_ids.tsv", "a")
+            for r, d, f in os.walk(path2):
+                for dir in d:
+                    if 'selectedpop' in dir:
+                        gen = dir.split('_')[1]
+                        for r2, d2, f2 in os.walk(path2 + '/selectedpop_' + str(gen)):
+                            for file in f2:
+                                if 'body' in file:
+                                    id = file.split('.')[0].split('_')[-1]
+                                    file_summary.write(gen+'\t'+id+'\n')
+
+            file_summary.close()
+