def get_data_from_archive(problems, algorithms, Configurations, function):
from PerformanceMeasures.DataFrame import ProblemFrame
problem_dict = {}
for problem in problems:
data = ProblemFrame(problem, algorithms)
reference_point = data.get_reference_point(
Configurations["Universal"]["No_of_Generations"])
generation_dict = {}
for generation in xrange(
Configurations["Universal"]["No_of_Generations"]):
population = data.get_frontier_values(generation)
evaluations = data.get_evaluation_values(generation)
algorithm_dict = {}
for algorithm in algorithms:
repeat_dict = {}
for repeat in xrange(Configurations["Universal"]["Repeats"]):
candidates = [
pop.objectives
for pop in population[algorithm.name][repeat]
]
repeat_dict[str(repeat)] = {}
if len(candidates) > 0:
repeat_dict[str(repeat)]["HyperVolume"] = function(
reference_point, candidates)
repeat_dict[str(repeat)]["Evaluations"] = evaluations[
algorithm.name][repeat]
else:
repeat_dict[str(repeat)]["HyperVolume"] = None
repeat_dict[str(repeat)]["Evaluations"] = None
algorithm_dict[algorithm.name] = repeat_dict
generation_dict[str(generation)] = algorithm_dict
problem_dict[problem.name] = generation_dict
return problem_dict
def generate_summary(problems,
algorithms,
baseline,
Configurations,
tag="Comparisions"):
for problem in problems:
print problem.name, " - " * 50
from PerformanceMeasures.DataFrame import ProblemFrame
data = ProblemFrame(problem, algorithms)
population = data.get_frontier_values()
fast_algorithm_population = []
for repeat in xrange(Configurations["Universal"]["Repeats"]):
fast_algorithm_population.append(
[pop.objectives for pop in population[baseline][repeat]])
for algorithm in algorithms:
if algorithm.name != baseline:
baseline_population = []
print algorithm.name + " | ",
for repeat in xrange(Configurations["Universal"]["Repeats"]):
baseline_population.append([
pop.objectives
for pop in population[algorithm.name][repeat]
])
for objective_number in xrange(len(problem.objectives)):
fast_algorithm_objective_list = [
flat[objective_number]
for fap in fast_algorithm_population for flat in fap
]
baseline_objective_list = [
flat[objective_number] for bp in baseline_population
for flat in bp
]
from numpy import std, mean
s = std(baseline_objective_list)
small_effect = s * 0.4
n1 = mean(baseline_objective_list)
n2 = mean(fast_algorithm_objective_list)
if abs(n1 - n2) <= small_effect:
print "Yes", round(
abs(n1 - n2) / (s + 0.000000001), 3),
else:
print "No", round(abs(n1 - n2) / (s + 0.000000001), 3),
print "|",
print
def get_data_from_archive(problems, algorithms, Configurations):
from PerformanceMeasures.DataFrame import ProblemFrame
problem_dict = {}
for problem in problems:
data = ProblemFrame(problem, algorithms)
generation_dict = {}
for generation in xrange(
Configurations["Universal"]["No_of_Generations"]):
population = data.get_frontier_values(generation)
evaluations = data.get_evaluation_values(generation)
repeat_dict = {}
for repeat in xrange(Configurations["Universal"]["Repeats"]):
algorithm_dict = {}
for algorithm in algorithms:
algorithm_dict[algorithm.name] = {}
try:
candidates = [
jmoo_individual(problem, pop.decisions,
pop.objectives)
for pop in population[algorithm.name][repeat]
]
except:
import pdb
pdb.set_trace()
repeat_dict[str(repeat)] = {}
if len(candidates) > 0:
algorithm_dict[
algorithm.name]["Solutions"] = candidates
algorithm_dict[
algorithm.name]["Evaluations"] = evaluations[
algorithm.name][repeat]
else:
algorithm_dict[algorithm.name]["Solutions"] = None
algorithm_dict[
algorithm.name]["Evaluations"] = None
repeat_dict[str(repeat)] = algorithm_dict
generation_dict[str(generation)] = repeat_dict
problem_dict[problem.name] = generation_dict
return problem_dict
def get_data_from_archive(problems, algorithms, Configurations, function):
from PerformanceMeasures.DataFrame import ProblemFrame
problem_dict = {}
for problem in problems:
data = ProblemFrame(problem, algorithms)
# # finding the final frontier
final_frontiers = data.get_frontier_values()
# unpacking the final frontiers
unpacked_frontier = []
for key in final_frontiers.keys():
for repeat in final_frontiers[key]:
unpacked_frontier.extend(repeat)
# Vivek: I have noticed that some of the algorithms (specifically VALE8) produces duplicate points
# which would then show up in nondominated sort and tip the scale in its favour. So I would like to remove
# all the duplicate points from the population and then perform a non dominated sort
old = len(unpacked_frontier)
unpacked_frontier = list(set(unpacked_frontier))
if len(unpacked_frontier) - old == 0:
print "There are no duplicates!! check"
# Find the non dominated solutions
# change into jmoo_individual
from jmoo_individual import jmoo_individual
population = [
jmoo_individual(problem, i.decisions, i.objectives)
for i in unpacked_frontier
]
# Vivek: I first tried to choose only the non dominated solutions. But then there are only few solutions
# (in order of 1-2) so I am just doing a non dominated sorting with crowd distance
actual_frontier = [
sol.fitness.fitness for sol in get_non_dominated_solutions(
problem, population, Configurations)
]
assert (len(actual_frontier) == Configurations["Universal"]
["Population_Size"])
generation_dict = {}
for generation in xrange(
Configurations["Universal"]["No_of_Generations"]):
#
population = data.get_frontier_values(generation)
evaluations = data.get_evaluation_values(generation)
algorithm_dict = {}
for algorithm in algorithms:
repeat_dict = {}
for repeat in xrange(
Configurations["Universal"]["Repeats"]):
candidates = [
pop.objectives
for pop in population[algorithm.name][repeat]
]
repeat_dict[str(repeat)] = {}
from PerformanceMetrics.IGD.IGD_Calculation import IGD
if len(candidates) > 0:
repeat_dict[str(repeat)]["IGD"] = IGD(
actual_frontier, candidates)
repeat_dict[str(
repeat)]["Evaluations"] = evaluations[
algorithm.name][repeat]
else:
repeat_dict[str(repeat)]["IGD"] = None
repeat_dict[str(repeat)]["Evaluations"] = None
algorithm_dict[algorithm.name] = repeat_dict
generation_dict[str(generation)] = algorithm_dict
problem_dict[problem.name] = generation_dict
return problem_dict, actual_frontier
