> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-explain/api-reference/utils/visualization.md). # alibi.utils.visualization ## `ImageVisualizationMethod` *Inherits from:* `Enum` An enumeration. ## `VisualizeSign` *Inherits from:* `Enum` An enumeration. ## Functions ### `heatmap` ```python 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: | | `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: | | `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: | | `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: | | `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: | | `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` ```python 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]` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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