def scatter_pymoo(data, name, label=None, **kwargs):
"""
Plot either a scatter, curve or surface plot.
Arguments:
data (np.array): Data to plot.
name (str): Visualization type.
label (str): Variable name for label.
Notes:
Surface plot not used.
"""
# Add data to plot
plot_args = get_plot_args(data, label)
angles = 4 if data.shape[1] == 3 else 1
for angle in range(angles):
plot_args["angle"] = (30, -135 + angle * 90)
plot = get_visualization("scatter", **plot_args)
plot.add(data, plot_type=None, color="k", **kwargs)
plot.do()
## plot.apply(lambda ax: ax.set_xlim([0,1]))
# Save figure
save_figure(name + f"_{angle+1}", plot)
def surface_pymoo(data, iteration):
"""
A surface plot using Pymoo.
Args:
data (np.array): Data to plot.
iteration (int): Iteration number.
"""
# Add data to plot
plot_args = get_plot_args(data, "x")
angles = 4 if data.shape[1] == 3 and iteration else 1
for angle in range(angles):
plot_args["angle"] = (30, -135 + angle * 90)
plot = get_visualization("scatter", **plot_args)
kwargs = {
"cmap": get_blackblue_cmap()
} if data.shape[1] == 3 else {
"color": "k"
}
plot.add(data, plot_type="surface", **kwargs)
plot.do()
# Save figure
if iteration:
save_figure(f"iteration_{iteration}_response_{angle}", plot)
else:
plot.show()
def heatmap(correlation):
"""
A heatmap plot.
Args:
correlation (np.array): Correlation matrix.
"""
newmap = get_blackblue_cmap()
plot = get_visualization("heatmap", labels="x", cmap=newmap, reverse=False)
plot.add(correlation, vmin=-1, vmax=1)
save_figure("heatmap", plot)
def pcp(data, name):
"""
A parallel component plot.
Args:
data (np.array): Data to plot.
name (str): Filename.
"""
plot = get_visualization("pcp", labels="f")
plot.set_axis_style(color="C0")
plot.add(data, color="k")
plot.do()
save_figure(name, plot)
from pymoo.algorithms.moead import MOEAD
from pymoo.factory import get_problem, get_visualization, get_reference_directions
from pymoo.optimize import minimize
problem = get_problem("dtlz2")
algorithm = MOEAD(get_reference_directions("das-dennis", 3, n_partitions=12),
n_neighbors=15,
decomposition="pbi",
prob_neighbor_mating=0.7)
res = minimize(problem, algorithm, termination=('n_gen', 200))
get_visualization("scatter").add(res.F).show()
plt.xlabel("Function Evaluations")
plt.ylabel("Hypervolume")
# fig.savefig(LATEX_DIR + 'convergence.eps', format='eps')
plt.show()
from pymoo.factory import get_visualization, get_decomposition
F = res.F
weights = np.array([0.01, 0.99])
decomp = get_decomposition("asf")
# We apply the decomposition and retrieve the best value (here minimum):
I = get_decomposition("asf").do(F, weights).argmin()
print("Best regarding decomposition: Point %s - %s" % (I, F[I]))
plot = get_visualization("scatter")
plot.add(F, color="blue", alpha=0.5, s=30)
plot.add(F[I], color="red", s=40)
plot.do()
# plot.apply(lambda ax: ax.arrow(0, 0, F[I][0], F[I][1], color='black',
# head_width=0.001, head_length=0.001, alpha=0.4))
plot.show()
## Parallel Coordinate Plots
from pymoo.visualization.pcp import PCP
plotPCP = PCP().add(res.F).add(res.F[I], color='r').show()
print(
f'A solução ideal é encontrada para a deformação {res.F[I][0]:.4f}m e massa {res.F[I][0]:.4f}kg com os parametros a {res.X[I][0]:.4f} b {res.X[I][1]:.4f} e h {res.X[I][2]:.4f}'
)
