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Fix plotting in nsga2 example #555

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Fix plotting in nsga2 example #555

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33 changes: 18 additions & 15 deletions examples/ga/nsga2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,8 @@
from deap import creator
from deap import tools

from pathlib import Path

creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)

Expand Down Expand Up @@ -121,24 +123,25 @@ def main(seed=None):
return pop, logbook

if __name__ == "__main__":
# with open("pareto_front/zdt1_front.json") as optimal_front_data:
# optimal_front = json.load(optimal_front_data)
path = Path(__file__).parent / "pareto_front/zdt1_front.json"
with path.open() as optimal_front_data:
optimal_front = json.load(optimal_front_data)
# Use 500 of the 1000 points in the json file
# optimal_front = sorted(optimal_front[i] for i in range(0, len(optimal_front), 2))
optimal_front = sorted(optimal_front[i] for i in range(0, len(optimal_front), 2))

pop, stats = main()
# pop.sort(key=lambda x: x.fitness.values)
pop.sort(key=lambda x: x.fitness.values)

# print(stats)
# print("Convergence: ", convergence(pop, optimal_front))
# print("Diversity: ", diversity(pop, optimal_front[0], optimal_front[-1]))
print(stats)
print("Convergence: ", convergence(pop, optimal_front))
print("Diversity: ", diversity(pop, optimal_front[0], optimal_front[-1]))

# import matplotlib.pyplot as plt
# import numpy
import matplotlib.pyplot as plt
import numpy

# front = numpy.array([ind.fitness.values for ind in pop])
# optimal_front = numpy.array(optimal_front)
# plt.scatter(optimal_front[:,0], optimal_front[:,1], c="r")
# plt.scatter(front[:,0], front[:,1], c="b")
# plt.axis("tight")
# plt.show()
front = numpy.array([ind.fitness.values for ind in pop])
optimal_front = numpy.array(optimal_front)
plt.scatter(optimal_front[:,0], optimal_front[:,1], c="r")
plt.scatter(front[:,0], front[:,1], c="b")
plt.axis("tight")
plt.show()