alibi.utils.visualization

ImageVisualizationMethod

Inherits from: Enum

An enumeration.

VisualizeSign

Inherits from: Enum

An enumeration.

Functions

heatmap

heatmap(data: numpy.ndarray, xticklabels: List[str], yticklabels: List[str], vmin: Optional[float] = None, vmax: Optional[float] = None, cmap: Union[str, matplotlib.colors.Colormap] = 'magma', robust: Optional[bool] = False, annot: Optional[bool] = True, linewidths: float = 3, linecolor: str = 'w', cbar: bool = True, cbar_label: str = '', cbar_ax: Optional[matplotlib.axes._axes.Axes] = None, cbar_kws: Optional[dict] = None, fmt: Union[str, matplotlib.ticker.Formatter] = '{x:.2f}', textcolors: Tuple[str, str] = ('white', 'black'), threshold: Optional[float] = None, text_kws: Optional[dict] = None, ax: Optional[matplotlib.axes._axes.Axes] = None, kwargs) -> matplotlib.axes._axes.Axes

Constructs a heatmap with annotation.

Name	Type	Default	Description
data	numpy.ndarray		A 2D numpy array of shape M x N.
xticklabels	List[str]		A list or array of length N with the labels for the columns.
yticklabels	List[str]		A list or array of length M with the labels for the rows.
vmin	Optional[float]	None
vmax	Optional[float]	None
cmap	Union[str, matplotlib.colors.Colormap]	'magma'	The Colormap instance or registered colormap name used to map scalar data to colors. This parameter is ignored for RGB(A) data.
robust	Optional[bool]	False	If True and vmin or vmax are absent, the colormap range is computed with robust quantiles instead of the extreme values. Uses numpy.nanpercentile_ with q values set to 2 and 98, respectively. .. _numpy.nanpercentile: https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html
annot	Optional[bool]	True	Boolean flag whether to annotate the heatmap. Default True.
linewidths	float	3	Width of the lines that will divide each cell. Default 3.
linecolor	str	'w'	Color of the lines that will divide each cell. Default "w".
cbar	bool	True	Boolean flag whether to draw a colorbar.
cbar_label	str	''	Optional label for the colorbar.
cbar_ax	Optional[matplotlib.axes._axes.Axes]	None	Optional axes in which to draw the colorbar, otherwise take space from the main axes.
cbar_kws	Optional[dict]	None	An optional dictionary with arguments to matplotlib.figure.Figure.colorbar_. .. _matplotlib.figure.Figure.colorbar: https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.colorbar
fmt	Union[str, matplotlib.ticker.Formatter]	'{x:.2f}'	Format of the annotations inside the heatmap. This should either use the string format method, e.g. "{x:.2f}", or be a matplotlib.ticker.Formatter_. Default "{x:.2f}". .. _matplotlib.ticker.Formatter: https://matplotlib.org/stable/api/ticker_api.html#matplotlib.ticker.Formatter
textcolors	Tuple[str, str]	('white', 'black')	A tuple of matplotlib colors. The first is used for values below a threshold, the second for those above. Default ("black", "white").
threshold	Optional[float]	None	Optional value in data units according to which the colors from textcolors are applied. If None (the default) uses the middle of the colormap as separation.
text_kws	Optional[dict]	None	An optional dictionary with arguments to matplotlib.axes.Axes.text_. .. _matplotlib.axes.Axes.text: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html
ax	Optional[matplotlib.axes._axes.Axes]	None	Axes in which to draw the plot, otherwise use the currently-active axes.
kwargs			All other keyword arguments are passed to matplotlib.axes.Axes.imshow_. .. _matplotlib.axes.Axes.imshow: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html
vmin,	vmax		When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use vmin/vmax when norm is given. When using RGB(A) data, parameters vmin/vmax are ignored.

Returns

• Type: matplotlib.axes._axes.Axes

visualize_image_attr

visualize_image_attr(attr: numpy.ndarray, original_image: Optional[numpy.ndarray] = None, method: str = 'heat_map', sign: str = 'absolute_value', plt_fig_axis: Optional[Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]] = None, outlier_perc: Union[int, float] = 2, cmap: Optional[str] = None, alpha_overlay: float = 0.5, show_colorbar: bool = False, title: Optional[str] = None, fig_size: Tuple[int, int] = (6, 6), use_pyplot: bool = True) -> Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]

Visualizes attribution for a given image by normalizing attribution values of the desired sign ('positive' | 'negative' | 'absolute_value' | 'all') and displaying them using the desired mode in a matplotlib figure.

Name	Type	Default	Description
attr	numpy.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[numpy.ndarray]	None	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	'heat_map'	Chosen method for visualizing attribution. Supported options are: - 'heat_map' - Display heat map of chosen attributions - 'blended_heat_map' - Overlay heat map over greyscale version of original image. Parameter alpha_overlay corresponds to alpha of heat map. - 'original_image' - Only display original image. - 'masked_image' - Mask image (pixel-wise multiply) by normalized attribution values. - 'alpha_scaling' - Sets alpha channel of each pixel to be equal to normalized attribution value. Default: 'heat_map'.
sign	str	'absolute_value'	Chosen sign of attributions to visualize. Supported options are: - 'positive' - Displays only positive pixel attributions. - 'absolute_value' - Displays absolute value of attributions. - 'negative' - Displays only negative pixel attributions. - '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[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]]	None	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]	2	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 and scale value are computed using absolute value of attributions.
cmap	Optional[str]	None	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	0.5	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.
show_colorbar	bool	False	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]	None	The title for the plot. If None, no title is set.
fig_size	Tuple[int, int]	(6, 6)	Size of figure created.
use_pyplot	bool	True	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.

Returns

• Type: Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]