Heatmap¶

For getting an idea of the distribution of values, heatmaps can be used.

Let us visualize some test data:

[1]:

import numpy as np

np.random.seed(1234)
F = np.random.random((4, 6))

A simple heatmap can be created by:

[2]:

from pymoo.visualization.heatmap import Heatmap
Heatmap().add(F).show()

[2]:

<pymoo.visualization.heatmap.Heatmap at 0x118684860>

../_images/visualization_heatmap_5_1.png

By default, larger values are represented with wight and smaller values with the corresponding color:

[3]:

Heatmap(bounds=[0,1]).add(np.ones((1, 6))).show()

[3]:

<pymoo.visualization.heatmap.Heatmap at 0x12e13ff80>

../_images/visualization_heatmap_7_1.png

This behavior can be changed by setting reverse to False.

[4]:

Heatmap(bounds=[0,1],reverse=False).add(np.ones((1, 6))).show()

[4]:

<pymoo.visualization.heatmap.Heatmap at 0x12e1bc920>

../_images/visualization_heatmap_9_1.png

The plot can be further customized by supplying a title, labels, and by using the plotting directives from matplotlib. Also, colors can be changed:

[5]:

plot = Heatmap(title=("Optimization", {'pad': 15}),
               cmap="Oranges_r",
               y_labels=["Solution A", "Solution B", "Solution C", "Solution D"],
               labels=["profit", "cost", "sustainability", "environment", "satisfaction", "time"])
plot.add(F)
plot.show()

[5]:

<pymoo.visualization.heatmap.Heatmap at 0x12e3a1220>

../_images/visualization_heatmap_11_1.png

Moreover, the values can be sorted lexicographically by objective(s) - and by default, the selected objective is inserted in position 0 of the range of objectives. Also, boundaries can be changed. Otherwise, it is scaled according to the smallest and largest values supplied.

[6]:

F = np.random.random((30, 6))

plot = Heatmap(figsize=(10,30),
               bound=[0,1],
               order_by_objectives=0,
               y_labels=None,
               labels=["profit", "cost", "sustainability", "environment", "satisfaction", "time"],
               cmap="Greens_r")

plot.add(F, aspect=0.2)
plot.show()

[6]:

<pymoo.visualization.heatmap.Heatmap at 0x12e3a3920>

../_images/visualization_heatmap_13_1.png

API¶

class pymoo.visualization.heatmap.Heatmap(self, cmap='Blues', order_by_objectives=False, reverse=True, solution_labels=True, **kwargs)

Heatmap

Parameters

cmapstr: The color map to be used.
order_by_objectivesint or list: Whether the result should be ordered by an objective. If false no order. Otherwise, either supply just the objective or a list. (it is lexicographically sorted).
reversebool: If true large values are white and small values the corresponding color. Otherwise, the other way around.
solution_labelsbool or list: If False no labels are plotted in the y axis. If true just the corresponding index. Otherwise the label provided.
boundstuple: If plot requires normalization, it might be necessary to supply the boundaries. (Otherwise they might be approximate by the minimum and maximum of the provided data). The boundaries should be provided as a list/tuple or 2D numpy array, where the first element represents the minimum, second the second the maximum values. If only an integer or float is supplied, the boundaries apply for each variable.
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.