pymoo
Latest Version: pymoo==0.3.1

Radar Plot

Let us produce some data first:

[1]:
import numpy as np

np.random.seed(3)

ideal_point = np.array([0.15, 0.1, 0.2, 0.1, 0.1])
nadir_point = np.array([0.85, 0.9, 0.95, 0.9, 0.85])

F = np.random.random((1, 5)) * (nadir_point - ideal_point) + ideal_point
print(F)
[[0.53555853 0.66651826 0.41817855 0.50866208 0.76971022]]

If the values should not be normalized, then we can plot the ideal and nadir point in addition. This keeps the absolute values of each objective. The outer shape represents the nadir point, the inner area the ideal point. All points will lie in the area spanned by those two points.

[2]:
from pymoo.visualization.radar import Radar

plot = Radar(bounds=[ideal_point, nadir_point], normalize_each_objective=False)
plot.add(F)
plot.show()
../_images/visualization_radar_5_0.png

But if the scale of the objective is too different, then normalization is recommended. Then, the ideal point is just the point in the middle and the nadir point is now symmetric.

[3]:
plot = Radar(bounds=[ideal_point, nadir_point])
plot.add(F)
plot.show()
../_images/visualization_radar_7_0.png
[4]:
F = np.random.random((6, 5)) * (nadir_point - ideal_point) + ideal_point

plot = Radar(bounds=[ideal_point, nadir_point],
             axis_style={"color": 'blue'},
             point_style={"color": 'red', 's': 30})
plot.add(F[:3], color="red", alpha=0.8)
plot.add(F[3:], color="green", alpha=0.8)
plot.show()
../_images/visualization_radar_8_0.png

API

class pymoo.visualization.radar.Radar(self, normalize_each_objective=True, n_partitions=3, point_style={}, **kwargs)

Radar Plot

Parameters
normalize_each_objectivebool

Whether each objective is normalized. Otherwise the inner and outer bound is plotted.

point_styledict

The style being used to visualize the points

n_partitionsint

Number of partitions to show in the radar.

reversebool

If plot requires normalization, then the reverse values can be plotted (1 - Input). For some plots it can be useful to interpret a larger area as better regarding a value. If minimization applies, a smaller area means better, which can be misleading.

Reverses the plotting. Larger area is better. Works only when normalize_each_objective is set to true.

axis_styledict

Most of the plots consists of an axis. The style of the axis, e.g. color, alpha, …, can be changed to further modify the plot appealing.

labelsstr or list

The labels to be used for each variable provided in the plot. If a string is used, then they will be enumerated. Otherwise, a list equal to the number of variables can be provided directly.

Other Parameters
figsizetuple

The figure size. Default (figsize=(8, 6)). For some plots changing the size might have side-effects for position.

titlestr or tuple

The title of the figure. If some additional kwargs should be provided this can be achieved by providing a tuple (“name”, {“key” : val}).

legendstr

Whether a legend should be shown or not.

tight_layoutbool

Whether tight layout should be used.

cmapcolormap

For some plots different kind of colors are used. The colormap can be changed to modify the color sequence for the plots.