Method, apparatus and device for explaining model and computer storage medium

An apparatus for explaining a model, comprising:

• a processor;
• and
• a memory configured to store one or more instructions executable by the processor;
• wherein the processor, when loading and executing the one or more instructions, is caused to perform:
• acquiring a target input sample of a target data analysis model, the target input sample comprising a plurality of features;
• acquiring an embedding vector by transforming the target input sample from an original feature space to an embedding space, the original feature space being a feature space where the target input sample is disposed;
• generating a neighborhood perturbation data set based on the embedding vector, the neighborhood perturbation data set comprising a plurality of first neighborhood vectors in a neighborhood of the embedding vector;
• determining a weight of each of the first neighborhood vectors based on a distance between the first neighborhood vector and the embedding vector, the distance being negatively correlated with the weight of the first neighborhood vector;
• acquiring an original perturbation data set by transforming the neighborhood perturbation data set into the original feature space, the original perturbation data set comprising a plurality of second neighborhood vectors;
• acquiring a plurality of output vectors by inputting the plurality of second neighborhood vectors into the target data analysis model;
• acquiring an explainable model by training a to-be-trained explainable model, by taking the plurality of second neighborhood vectors, the plurality of output vectors, and the weight of each of the first neighborhood vectors as training samples;
• and
• acquiring, based on the explainable model, weights of the plurality of features in the target input sample.

A non-transient computer storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform a method for explaining the model comprising:

• acquiring a target input sample of a target data analysis model, the target input sample comprising a plurality of features;
• acquiring an embedding vector by transforming the target input sample from an original feature space to an embedding space, the original feature space being a feature space where the target input sample is disposed;
• generating a neighborhood perturbation data set based on the embedding vector, the neighborhood perturbation data set comprising a plurality of first neighborhood vectors in a neighborhood of the embedding vector;
• determining a weight of each of the first neighborhood vectors based on a distance between the first neighborhood vector and the embedding vector, the distance being negatively correlated with the weight of the first neighborhood vector;
• acquiring an original perturbation data set by transforming the neighborhood perturbation data set into the original feature space, the original perturbation data set comprising a plurality of second neighborhood vectors;
• acquiring a plurality of output vectors by inputting the plurality of second neighborhood vectors into the target data analysis model;
• acquiring an explainable model by training a to-be-trained explainable model, by taking the plurality of second neighborhood vectors, the plurality of output vectors, and the weight of each of the first neighborhood vectors as training samples;
• and
• acquiring, based on the explainable model, weights of the plurality of features in the target input sample of the target data analysis model.