shap.plots.heatmap
- shap.plots.heatmap(shap_values: ~shap._explanation.Explanation, instance_order=shap.Explanation.hclust, feature_values=shap.Explanation.abs.mean(0), feature_order=None, max_display=10, cmap=<matplotlib.colors.LinearSegmentedColormap object>, show=True, plot_width=8, ax=None)
Create a heatmap plot of a set of SHAP values.
This plot is designed to show the population substructure of a dataset using supervised clustering and a heatmap. Supervised clustering involves clustering data points not by their original feature values but by their explanations. By default, we cluster using
shap.utils.hclust_ordering(), but any clustering can be used to order the samples.- Parameters:
- shap_valuesshap.Explanation
A multi-row
Explanationobject that we want to visualize in a cluster ordering.- instance_orderOpChain or numpy.ndarray
A function that returns a sort ordering given a matrix of SHAP values and an axis, or a direct sample ordering given as an
numpy.ndarray.- feature_valuesOpChain or numpy.ndarray
A function that returns a global summary value for each input feature, or an array of such values.
- feature_orderNone, OpChain, or numpy.ndarray
A function that returns a sort ordering given a matrix of SHAP values and an axis, or a direct input feature ordering given as an
numpy.ndarray. IfNone, then we usefeature_values.argsort.- max_displayint
The maximum number of features to display (default is 10).
- showbool
Whether
matplotlib.pyplot.show()is called before returning. Setting this toFalseallows the plot to be customized further after it has been created.- plot_widthint, default 8
The width of the heatmap plot.
- axmatplotlib Axes
Axes object to draw the plot onto, otherwise uses the current Axes.
- Returns:
- ax: matplotlib Axes
Returns the
Axesobject with the plot drawn onto it.
Examples