ribs.visualize.sliding_boundaries_archive_heatmap(archive, ax=None, *, df=None, transpose_measures=False, cmap='magma', aspect='auto', ms=None, boundary_lw=0, vmin=None, vmax=None, cbar='auto', cbar_kwargs=None, rasterized=False)[source]

Plots heatmap of a SlidingBoundariesArchive with 2D measure space.

Since the boundaries of ribs.archives.SlidingBoundariesArchive are dynamic, we plot the heatmap as a scatter plot, in which each marker is an elite and its color represents the objective value. Boundaries can optionally be drawn by setting boundary_lw to a positive value.


>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ribs.archives import SlidingBoundariesArchive
>>> from ribs.visualize import sliding_boundaries_archive_heatmap
>>> archive = SlidingBoundariesArchive(solution_dim=2,
...                                    dims=[10, 20],
...                                    ranges=[(-1, 1), (-1, 1)],
...                                    seed=42)
>>> # Populate the archive with the negative sphere function.
>>> xy = np.clip(np.random.standard_normal((1000, 2)), -1.5, 1.5)
>>> archive.add(solution=xy,
...             objective=-np.sum(xy**2, axis=1),
...             measures=xy)
>>> # Plot heatmaps of the archive.
>>> fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,6))
>>> fig.suptitle("Negative sphere function")
>>> sliding_boundaries_archive_heatmap(archive, ax=ax1,
...                                    boundary_lw=0.5)
>>> sliding_boundaries_archive_heatmap(archive, ax=ax2)
>>> ax1.set_title("With boundaries")
>>> ax2.set_title("Without boundaries")
>>> ax1.set(xlabel='x coords', ylabel='y coords')
>>> ax2.set(xlabel='x coords', ylabel='y coords')
>>> plt.show()
  • archive (SlidingBoundariesArchive) – A 2D SlidingBoundariesArchive.

  • ax (matplotlib.axes.Axes) – Axes on which to plot the heatmap. If None, the current axis will be used.

  • df (ribs.archives.ArchiveDataFrame) – If provided, we will plot data from this argument instead of the data currently in the archive. This data can be obtained by, for instance, calling ribs.archives.ArchiveBase.data() with return_type="pandas" and modifying the resulting ArchiveDataFrame. Note that, at a minimum, the data must contain columns for index, objective, and measures. To display a custom metric, replace the “objective” column.

  • transpose_measures (bool) – By default, the first measure in the archive will appear along the x-axis, and the second will be along the y-axis. To switch this behavior (i.e. to transpose the axes), set this to True.

  • cmap (str, list, matplotlib.colors.Colormap) – Colormap to use when plotting intensity. Either the name of a Colormap, a list of RGB or RGBA colors (i.e. an \(N \times 3\) or \(N \times 4\) array), or a Colormap object.

  • aspect ('auto', 'equal', float) – The aspect ratio of the heatmap (i.e. height/width). Defaults to 'auto'. 'equal' is the same as aspect=1. See matplotlib.axes.Axes.set_aspect() for more info.

  • ms (float) – Marker size for the solutions.

  • boundary_lw (float) – Line width when plotting the boundaries. Set to 0 to have no boundaries.

  • vmin (float) – Minimum objective value to use in the plot. If None, the minimum objective value in the archive is used.

  • vmax (float) – Maximum objective value to use in the plot. If None, the maximum objective value in the archive is used.

  • cbar ('auto', None, matplotlib.axes.Axes) – By default, this is set to 'auto' which displays the colorbar on the archive’s current Axes. If None, then colorbar is not displayed. If this is an Axes, displays the colorbar on the specified Axes.

  • cbar_kwargs (dict) – Additional kwargs to pass to colorbar().

  • rasterized (bool) – Whether to rasterize the heatmap. This can be useful for saving to a vector format like PDF. Essentially, only the heatmap will be converted to a raster graphic so that the archive cells will not have to be individually rendered. Meanwhile, the surrounding axes, particularly text labels, will remain in vector format.


ValueError – The archive is not 2D.