IForest feature contribution for a given outlier about pyod HOT 3 OPEN

# Fit PyOD model
clf = KNN()
clf.fit(data)
    
# Shap is slow so perhaps only explain highest likelihood values, e.g. 100
scores = clf.decision_scores_
idx = np.argsort(scores)
    
# Fit shap explainer and get values for top 100
explainer = shap.Explainer(clf.decision_function, data)
shap_values = explainer(data[idx][-100:])

# Or for just one point of interest
idx = 55
shap_values = explainer(data[55].reshape(1,-1))
    
# Example of some plots
shap.plots.waterfall(shap_values)
shap.plots.beeswarm(shap_values)
shap.plots.bar(shap_values)