wehub-resource-sync
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from . import colors
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def group_difference(
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shap_values,
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group_mask,
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feature_names=None,
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xlabel=None,
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xmin=None,
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xmax=None,
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max_display=None,
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sort=True,
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show=True,
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ax=None,
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):
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"""This plots the difference in mean SHAP values between two groups.
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It is useful to decompose many group level metrics about the model output among the
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input features. Quantitative fairness metrics for machine learning models are
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a common example of such group level metrics.
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Parameters
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----------
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shap_values : numpy.array
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Matrix of SHAP values (# samples x # features) or a vector of model outputs (# samples).
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group_mask : numpy.array
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A boolean mask where True represents the first group of samples and False the second.
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feature_names : list
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A list of feature names.
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"""
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# Compute confidence bounds for the group difference value
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vs = []
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gmean = group_mask.mean()
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for _ in range(200):
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r = np.random.rand(shap_values.shape[0]) > gmean
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vs.append(shap_values[r].mean(0) - shap_values[~r].mean(0))
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vs_ = np.array(vs)
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xerr = np.vstack([np.percentile(vs_, 95, axis=0), np.percentile(vs_, 5, axis=0)])
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# See if we were passed a single model output vector and not a matrix of SHAP values
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if len(shap_values.shape) == 1:
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shap_values = shap_values.reshape(1, -1).T
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if feature_names is None:
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feature_names = [""]
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# Fill in any missing feature names
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if feature_names is None:
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feature_names = [f"Feature {i}" for i in range(shap_values.shape[1])]
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diff = shap_values[group_mask].mean(0) - shap_values[~group_mask].mean(0)
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if sort is True:
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inds = np.argsort(-np.abs(diff)).astype(int)
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else:
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inds = np.arange(len(diff))
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if max_display is not None:
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inds = inds[:max_display]
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if ax:
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# Disable plotting out if an ax has been provided
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show = False
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else:
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# Draw the figure if no ax has been provided
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figsize = (6.4, 0.2 + 0.9 * len(inds))
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_, ax = plt.subplots(figsize=figsize)
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ticks = range(len(inds) - 1, -1, -1)
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ax.axvline(0, color="#999999", linewidth=0.5)
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ax.barh(ticks, diff[inds], color=colors.blue_rgb, capsize=3, xerr=np.abs(xerr[:, inds]))
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for i in range(len(inds)):
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ax.axhline(y=i, color="#cccccc", lw=0.5, dashes=(1, 5), zorder=-1)
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ax.xaxis.set_ticks_position("bottom")
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ax.yaxis.set_ticks_position("none")
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ax.set_yticks(ticks)
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ax.set_yticklabels([feature_names[i] for i in inds], fontsize=13)
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.spines["left"].set_visible(False)
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ax.tick_params(labelsize=11)
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if xlabel is None:
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xlabel = "Group SHAP value difference"
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ax.set_xlabel(xlabel, fontsize=13)
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ax.set_xlim(xmin, xmax)
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if show:
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plt.show()
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