problem.directions[:] = DIRECTIONS
# Experimenter with NSGAII, NSGAIII and SPEA2
algorithms = [(NSGAII, {"population_size":POPSIZE}),
(NSGAIII, {"population_size":POPSIZE, "divisions_outer":12}),
(SPEA2, {"population_size":POPSIZE})
]
# Multi-processing (4 parallel processes)
with ProcessPoolEvaluator(4) as evaluator:
results = experiment(algorithms, problem, nfe=FEs, seeds=n_reps , evaluator=evaluator, display_stats=True)
# As of Platypus v1.0.4, NSGA-III works only with minimization objectives
# Converting first objective (Apoptosis) back to positive
for alg in ["NSGAII", "NSGAIII", "SPEA2"]:
for run in range(len(results[alg]["Problem"])):
for ind in range(len(results[alg]["Problem"][0])):
results[alg]["Problem"][run][ind].objectives[0] = -results[alg]["Problem"][run][ind].objectives[0]
results[alg]["Problem"][run][ind].problem.directions[0] = 1
# Save results in pickle
import pickle
pickle.dump(results, open("res_3obj_"+str(n_reps)+"reps.p", "wb"))
# Calculate Hypervolume
hyp = Hypervolume(minimum=[-1.0, -1.0, 0], maximum=[1.0, 1.0, 16])
hyp_result = calculate(results, hyp, evaluator=evaluator)
display(hyp_result, ndigits=3)
with open("hypervolume_3obj_"+str(n_reps)+"reps.txt", 'w') as f:
print(hyp_result, file=f)
plt.legend(numpoints=1)
# plt.xlim(0, 50);
plt.xlabel("Compilation Time(in ms)")
plt.ylabel("Execution Time(in ms)")
plt.title(
"Comparision of Compilation and Execution time \n of various compiler optimization approaches"
)
# # Hypervolume
# In[ ]:
from platypus import Hypervolume, experiment, calculate, display
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(results, hyp)
display(hyp_result, ndigits=5)
print(hyp_result)
# # Compilation and Execution time of Optimization levels
# In[1012]:
import matplotlib.pyplot as plt
import numpy as np
import collections
fig = plt.figure()
X = np.arange(4)
fig = plt.figure()
from platypus import NSGAII, Problem, Real, nondominated, Hypervolume, calculate
def belegundu(vars):
x = vars[0]
y = vars[1]
return [-2 * x + y, 2 * x + y], [-x + y - 1, x + y - 7]
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0, 5), Real(0, 3)]
problem.constraints[:] = "<=0"
problem.function = belegundu
algorithm = NSGAII(problem)
algorithm.run(10000)
feasible_solutions = [s for s in algorithm.result if s.feasible]
nondominated_solutions = nondominated(algorithm.result)
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(algorithm.result, hyp)
print(hyp_result)
pass
}
for i in range(num_iter):
algorithm = NSGAII(SVM(), population_size=pop)
for j in tqdm(range(generations_amount),
desc="Iteration " + str(i + 1)):
algorithm.step()
# Defining structure to pass as parameter to class Hypervolume
nsga_results = OrderedDict()
nsga_results["NSGAII"] = {}
nsga_results["NSGAII"]["SVM"] = [algorithm.result]
# calculate the hypervolume indicator
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(nsga_results, hyp)
hypervolume = np.mean(
list(hyp_result["NSGAII"]["SVM"]["Hypervolume"]))
hypervolumes[i].append(hypervolume)
nondominated_results = nondominated(algorithm.result)
# display(hipervolume, ndigits=3)
solution = nondominated_results[0]
features = solution.variables[0]
for s in nondominated_results:
if abs(s.objectives[0] -
s.objectives[1]) < abs(solution.objectives[0] -
solution.objectives[1]):
solution = s
features = s.variables[0]
from platypus import NSGAII, NSGAIII, DTLZ2, Hypervolume, experiment, calculate, display
if __name__ == "__main__":
algorithms = [NSGAII, (NSGAIII, {"divisions_outer":12})]
problems = [DTLZ2(3)]
# run the experiment
results = experiment(algorithms, problems, nfe=10000, seeds=10)
# calculate the hypervolume indicator
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(results, hyp)
display(hyp_result, ndigits=3)