shap.plots.beeswarm
- shap.plots.beeswarm(shap_values: Explanation, max_display: int | None = 10, order=shap.Explanation.abs.mean(0), clustering=None, cluster_threshold=0.5, color=None, axis_color='#333333', alpha: float = 1.0, ax: Axes | None = None, show: bool = True, log_scale: bool = False, color_bar: bool = True, s: float = 16, plot_size: Literal['auto'] | float | tuple[float, float] | None = 'auto', color_bar_label: str = 'Feature value', group_remaining_features: bool = True)
Create a SHAP beeswarm plot, colored by feature values when they are provided.
- Parameters:
- shap_valuesExplanation
This is an
Explanationobject containing a matrix of SHAP values (# samples x # features).- max_displayint
How many top features to include in the plot (default is 10, or 7 for interaction plots).
- ax: matplotlib Axes
Axes object to draw the plot onto, otherwise uses the current Axes.
- showbool
Whether
matplotlib.pyplot.show()is called before returning. Setting this toFalseallows the plot to be customized further after it has been created, returning the current axis viamatplotlib.pyplot.gca().- color_barbool
Whether to draw the color bar (legend).
- sfloat
What size to make the markers. For further information, see
sinmatplotlib.pyplot.scatter().- plot_size“auto” (default), float, (float, float), or None
What size to make the plot. By default, the size is auto-scaled based on the number of features that are being displayed. Passing a single float will cause each row to be that many inches high. Passing a pair of floats will scale the plot by that number of inches. If
Noneis passed, then the size of the current figure will be left unchanged. Ifaxis notNone, then passingplot_sizewill raise aValueError.- group_remaining_features: bool
If there are more features than
max_display, then plot a row representing the sum of SHAP values of all remaining features. Default True.
- Returns:
- ax: matplotlib Axes
Returns the
Axesobject with the plot drawn onto it. Only returned ifshow=False.
Examples