How to get shapley value for Neural Network trained on matlab? it keeps error...

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한용 이
한용 이 2024-8-26
回答： Angelo Yeo 2024-8-26
Hi there,
I wanted to get shapley value of my pre-trained ANN.
it is regression model.
it's input's shape is 7*5120 double
and output is 1*5120 double.
I'm confused with idea of shapley.. sorry
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Angelo Yeo
Angelo Yeo 2024-8-26
Can you be more specific about your model and the error message? It's the best if you can share your model (code and data) and the reproduction steps for the error.
한용 이
한용 이 2024-8-26
编辑：Angelo Yeo 2024-8-26
thanks!
I've tried and managed to make it work.
but I want to change predictor's classes name.
I'd copied table and manually changed and manually plotted. but i want to change originals.
here's my code and want some help. I appreciate your help very much. thank you.
clc;
clear;
% 모델과 데이터 로드
net_temp = load('net_model_F1.mat');
net = net_temp.net;
%% Model F1
train_in_total_temp = load('Train_in.mat');
train_out_total_temp = load('Train_out.mat');
train_in_total = train_in_total_temp.Train_in;
train_out_total = train_out_total_temp.Train_out;
input = train_in_total(:,1:7); % model_S
output = train_out_total(:,1);
veri_in_total_temp = load('Veri_in.mat');
veri_out_total_temp = load('Veri_out.mat');
veri_in_total = veri_in_total_temp.Veri_in;
veri_out_total = veri_out_total_temp.Veri_out;
input_veri = veri_in_total(:,1:7); % model_S
output_veri = veri_out_total(:,1);
%% 입력 및 출력 데이터 정규화
input_max = zeros(1, size(input, 2));
input_min = zeros(1, size(input, 2));
output_max = zeros(1, size(output, 2));
output_min = zeros(1, size(output, 2));
for i = 1:size(input, 2)
input_max(i) = max(input(:,i));
input_min(i) = min(input(:,i));
end
for i = 1:size(output, 2)
output_max(i) = max(output(:,i));
output_min(i) = min(output(:,i));
end
x_train_re = zeros(size(input));
y_train_re = zeros(size(output));
for i = 1:size(input, 2)
x_train_re(:,i) = (input(:,i) - input_min(i)) / (input_max(i) - input_min(i));
end
for i = 1:size(output, 2)
y_train_re(:,i) = (output(:,i) - output_min(i)) / (output_max(i) - output_min(i));
end
x_train = x_train_re';
t_train = y_train_re';
y_train = net(x_train);
%% 입력 및 출력 데이터 정규화
x_veri_re = zeros(size(input_veri));
y_veri_re = zeros(size(output_veri));
for i = 1:size(input_veri, 2)
x_veri_re(:,i) = (input_veri(:,i) - input_min(i)) / (input_max(i) - input_min(i));
end
for i = 1:size(output_veri, 2)
y_veri_re(:,i) = (output_veri(:,i) - output_min(i)) / (output_max(i) - output_min(i));
end
x_veri = x_veri_re';
t_veri = y_veri_re';
y_veri = net(x_veri);
test_y = t_veri;
test_y_val = y_veri;
%% Shapley 값 계산
f = @(x) net(x')'; % 인공신경망 모델 함수를 정의
x_veri_shapley = x_veri'; % 각 행이 하나의 샘플이 되도록 전치
x_train_shapley = x_train'; % 각 행이 하나의 샘플이 되도록 전치
% 샘플링 예시 (500개 데이터 포인트 샘플링)
num_samples = 500; % 샘플링할 데이터 수
idx = randperm(size(x_veri_shapley, 1), num_samples);
x_veri_shapley_sampled = x_veri_shapley(idx, :);
% % 병렬 처리 활성화
explainer = shapley(f, x_train_shapley, 'QueryPoints', x_veri_shapley_sampled,'UseParallel', true);
%%
plot(explainer)
%%
% MeanAbsoluteShapley table을 복사
shapley_table = explainer.MeanAbsoluteShapley;
% 변수 이름 변경
desired_variable_names = {'PGA', 'Dur_{sig}', 'Sa_{max}', 'Tm', 'CAV_{max}', 'Arias_{max}', 'f_{1}'};
shapley_table.Predictor = desired_variable_names(:); % 새 변수 이름으로 교체
% Shapley 값과 변수 이름을 Shapley 값의 내림차순으로 정렬
[sorted_values, sort_index] = sort(shapley_table.ShapleyValue, 'ascend');
sorted_names = shapley_table.Predictor(sort_index);
% 막대 그래프 그리기 (큰 값부터 작은 값 순서로)
figure;
barh(sorted_values);
set(gca, 'YTickLabel', sorted_names);
xlabel('Shapley 절댓값의 평균');
ylabel('예측 변수');
title('Shapley 중요도 플롯');
%%
figure(10);
plot(explainer,QueryPointIndices=30);
figure(11);
plot(explainer);
figure(12);
swarmchart(explainer);

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回答（1 个）

Angelo Yeo
Angelo Yeo 2024-8-26
Ran in:
I do not have the model and dataset, so I used a random samples. The key is to use yticklabels. Would this work for you?
clc;
clear;
%% Shapley 값 계산
% demo neural network
x = randn(7, 150);
t = randn(1, 150);
net = fitnet(10);
net = configure(net,x,t);
% view(net)
f = @(x) net(x')'; % 인공신경망 모델 함수를 정의
x_veri_shapley = x(:,101:end)'; % 각 행이 하나의 샘플이 되도록 전치
x_train_shapley = x(:, 1:100)'; % 각 행이 하나의 샘플이 되도록 전치
% 샘플링 예시
num_samples = size(x_veri_shapley,1); % 샘플링할 데이터 수
idx = randperm(size(x_veri_shapley, 1), num_samples);
x_veri_shapley_sampled = x_veri_shapley(idx, :);
% % 병렬 처리 활성화
explainer = shapley(f, x_train_shapley, 'QueryPoints', x_veri_shapley_sampled,'UseParallel', false);
%%
% plot(explainer)
%%
% MeanAbsoluteShapley table을 복사
shapley_table = explainer.MeanAbsoluteShapley;
% 변수 이름 변경
desired_variable_names = ["PGA", "Dur_{sig}", "Sa_{max}", "Tm", "CAV_{max}", "Arias_{max}", "f_{1}"];
shapley_table.Predictor = desired_variable_names(:); % 새 변수 이름으로 교체
% Shapley 값과 변수 이름을 Shapley 값의 내림차순으로 정렬
[sorted_values, sort_index] = sort(shapley_table.ShapleyValue, 'ascend');
sorted_names = shapley_table.Predictor(sort_index);
% 막대 그래프 그리기 (큰 값부터 작은 값 순서로)
% figure;
% barh(sorted_values);
% set(gca, 'YTickLabel', sorted_names);
% xlabel('Shapley 절댓값의 평균');
% ylabel('예측 변수');
% title('Shapley 중요도 플롯');
%%
close all;
figure(10);
plot(explainer,QueryPointIndices=30);
hAxes = gca;
hAxes.TickLabelInterpreter = "tex";
yticklabels(hAxes, sorted_names) % use "yticklabels" to change the YTickLabels
figure(11);
plot(explainer);
hAxes = gca;
hAxes.TickLabelInterpreter = "tex";
yticklabels(hAxes, sorted_names) % use "yticklabels" to change the YTickLabels
figure(12);
swarmchart(explainer);
hAxes = gca;
hAxes.TickLabelInterpreter = "tex";
yticklabels(hAxes, sorted_names) % use "yticklabels" to change the YTickLabels

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