evaExercise-python/plotting.py at master · goramartin/evaExercise-python · GitHub

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# use and edit this file to make all the plots you need - it is generally easier
# than plotting directly after the run of the algorithm

import utils

import matplotlib.pyplot as plt
import sys
import functools


def add_suffix(str, suff):
    return str + '.' + suff

fit_names = ['ZDT1', 'ZDT2', 'ZDT3', 'ZDT4', 'ZDT6']
datasets = [
    "default",
    "Hypervolume+default",
    "default+MS0.5",
    "Hypervolume+default+MS0.5",
    #"CXUniform+DefMut+CXP0.2+MP0.8+MS0.5",
    "CXUniform+DefMut+CXP0.8+MP0.2+MS0.5",
    "CXArithmetics+DefMut+CXP0.8+MP0.2+MS0.5",
    #"CXArithmetics+DefMut+CXP0.2+MP0.8+MS0.5",
    #"DefCX+AdaptiveMut+CXP0.8+MP0.2+MS0.5",
    #"DefCX+AdaptiveMut+CXP0.8+MP0.2+MS0.05",
    "CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.5+sum",
    "HyperVolume+CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.5",
    #"CXUniform+DiffMut+CXP0.8+MP0.2+MS0.5",
    "CXUniform+AdaptiveDiffMut+CXP0.8+MP0.2+MS0.55",
    "HyperVolume+CXUniform+DiffAdaptiveMut+CXP0.8+MP0.2+MS0.55"
]
for name in fit_names:
    f = functools.partial(add_suffix, suff=name)
    plt.figure(figsize=(12,8))
    utils.plot_experiments('multi', list(map(f, datasets)), { 'DefCX+AdaptiveMut+CXP0.8+MP0.2+MS0.5': 'CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.5',  'DefCX+AdaptiveMut+CXP0.8+MP0.2+MS0.05': 'CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.05', 'CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.5+sum': 'CXUniform+AdaptiveMut+CXP0.8+MP0.2+MS0.5' })
    #plt.yscale('log')
    plt.show()