U f @sRddlmZddlZzddlZddlZWnek r<YnXGdddeZdS)) ExplainerNc@s$eZdZdZd ddZd ddZdS) LimeTabularExplainera Simply wrap of lime.lime_tabular.LimeTabularExplainer into the common shap interface. Parameters ---------- model : function or iml.Model User supplied function that takes a matrix of samples (# samples x # features) and computes a the output of the model for those samples. The output can be a vector (# samples) or a matrix (# samples x # model outputs). data : numpy.array The background dataset. mode : "classification" or "regression" Control the mode of LIME tabular. classificationcs|_|dkst|_tt|dr0|j}|_tj j ||d_ |dd}t |j dkrd_d_|dkrfdd }|_n |ddj d_d _dS) N)r regressionpandas.core.frame.DataFrame'>)moderTrcs(|dd}d|}t||fS)Nr )modelZreshapenpZhstack)Xpredsp0selfA/tmp/pip-target-lpfmz8o1/lib/python/shap/explainers/other/lime.pypred)sz+LimeTabularExplainer.__init__..predF)r AssertionErrorrstrtypeendswithvaluesdatalimeZ lime_tabularr explainerlenshapeout_dimflat_out)rr rroutrrrr__init__s   zLimeTabularExplainer.__init__Nc s|dkrjdn|}ttdr.jfddt|jD}tjdD]X}|jj||j t|j|d}t|jD](}|j |D]\}} | ||||f<qqqT|j dkrtt |D]}|| ||<q|j r|dS|S)Nr rcsg|]}tjqSr)r Zzerosr).0jr rr 8sz5LimeTabularExplainer.attributions..r)labels num_featuresr)rrrrrrangerrZexplain_instancer Z local_exprrr ) rr Znsamplesr)r!iexpr%kvrr&r attributions2s  z!LimeTabularExplainer.attributions)r)r#N)__name__ __module__ __qualname____doc__r"r/rrrrr s r)rrnumpyr rZlime.lime_tabular ImportErrorrrrrrs