alibi.utils.visualization module

class alibi.utils.visualization.ImageVisualizationMethod(value)[source]

Bases: enum.Enum

An enumeration.

alpha_scaling = 5
blended_heat_map = 2
heat_map = 1
masked_image = 4
original_image = 3
class alibi.utils.visualization.VisualizeSign(value)[source]

Bases: enum.Enum

An enumeration.

absolute_value = 2
all = 4
negative = 3
positive = 1
alibi.utils.visualization.visualize_image_attr(attr, original_image=None, method='heat_map', sign='absolute_value', plt_fig_axis=None, outlier_perc=2, cmap=None, alpha_overlay=0.5, show_colorbar=False, title=None, fig_size=(6, 6), use_pyplot=True)[source]

Visualizes attribution for a given image by normalizing attribution values of the desired sign (positive, negative, absolute value, or all) and displaying them using the desired mode in a matplotlib figure.

  • attr (ndarray) – Numpy array corresponding to attributions to be visualized. Shape must be in the form (H, W, C), with channels as last dimension. Shape must also match that of the original image if provided.

  • original_image (Optional[ndarray]) – Numpy array corresponding to original image. Shape must be in the form (H, W, C), with channels as the last dimension. Image can be provided either with float values in range 0-1 or int values between 0-255. This is a necessary argument for any visualization method which utilizes the original image.

  • method (str) – Chosen method for visualizing attribution. Supported options are: 1. heat_map - Display heat map of chosen attributions 2. blended_heat_map - Overlay heat map over greyscale version of original image. Parameter alpha_overlay corresponds to alpha of heat map. 3. original_image - Only display original image. 4. masked_image - Mask image (pixel-wise multiply) by normalized attribution values. 5. alpha_scaling - Sets alpha channel of each pixel to be equal to normalized attribution value. Default: heat_map

  • sign (str) – Chosen sign of attributions to visualize. Supported options are: 1. positive - Displays only positive pixel attributions. 2. absolute_value - Displays absolute value of attributions. 3. negative - Displays only negative pixel attributions. 4. all - Displays both positive and negative attribution values. This is not supported for masked_image or alpha_scaling modes, since signed information cannot be represented in these modes.

  • plt_fig_axis (Optional[Tuple[figure, axis]]) – Tuple of matplotlib.pyplot.figure and axis on which to visualize. If None is provided, then a new figure and axis are created.

  • outlier_perc (Union[int, float]) – Top attribution values which correspond to a total of outlier_perc percentage of the total attribution are set to 1 and scaling is performed using the minimum of these values. For sign=`all`, outliers a nd scale value are computed using absolute value of attributions.

  • cmap (Optional[str]) – String corresponding to desired colormap for heatmap visualization. This defaults to “Reds” for negative sign, “Blues” for absolute value, “Greens” for positive sign, and a spectrum from red to green for all. Note that this argument is only used for visualizations displaying heatmaps.

  • alpha_overlay (float) – Alpha to set for heatmap when using blended_heat_map visualization mode, which overlays the heat map over the greyscaled original image.

  • show_colorbar (bool) – Displays colorbar for heatmap below the visualization. If given method does not use a heatmap, then a colormap axis is created and hidden. This is necessary for appropriate alignment when visualizing multiple plots, some with colorbars and some without.

  • title (Optional[str]) – Title string for plot. If None, no title is set.

  • fig_size (Tuple[int, int]) – Size of figure created.

  • use_pyplot (bool) – If true, uses pyplot to create and show figure and displays the figure after creating. If False, uses Matplotlib object oriented API and simply returns a figure object without showing.


  • 2-element tuple of **figure*, **axis***

  • - **figure* (matplotlib.pyplot.figure)* – Figure object on which visualization is created. If plt_fig_axis argument is given, this is the same figure provided.

  • - **axis* (matplotlib.pyplot.axis)* – Axis object on which visualization is created. If plt_fig_axis argument is given, this is the same axis provided.