compute shapley values for query point -凯发k8网页登录

train a regression model and create a shapley object. when you create a shapley object, if you do not specify a query point, then the software does not compute shapley values. use the object function fit to compute the shapley values for the specified query point. then create a bar graph of the shapley values by using the object function plot.

load the carbig data set, which contains measurements of cars made in the 1970s and early 1980s.

load carbig

create a table containing the predictor variables acceleration, cylinders, and so on, as well as the response variable mpg.

tbl = table(acceleration,cylinders,displacement,horsepower,model_year,weight,mpg);

removing missing values in a training set can help reduce memory consumption and speed up training for the fitrkernel function. remove missing values in tbl.

tbl = rmmissing(tbl);

train a blackbox model of mpg by using the function

rng('default') % for reproducibility
mdl = fitrkernel(tbl,'mpg','categoricalpredictors',[2 5]);

create a shapley object. specify the data set tbl, because mdl does not contain training data.

explainer = shapley(mdl,tbl)

explainer = 
  shapley with properties:
            blackboxmodel: [1x1 regressionkernel]
               querypoint: []
           blackboxfitted: []
            shapleyvalues: []
               numsubsets: 64
                        x: [392x7 table]
    categoricalpredictors: [2 5]
                   method: 'interventional-kernel'
                intercept: 22.6202

explainer stores the training data tbl in the x property.

compute the shapley values of all predictor variables for the first observation in tbl.

querypoint = tbl(1,:)

querypoint=1×7 table
    acceleration    cylinders    displacement    horsepower    model_year    weight    mpg
    ____________    _________    ____________    __________    __________    ______    ___
         12             8            307            130            70         3504     18 

explainer = fit(explainer,querypoint);

for a regression model, shapley computes shapley values using the predicted response, and stores them in the shapleyvalues property. display the values in the shapleyvalues property.

explainer.shapleyvalues

ans=6×2 table
      predictor       shapleyvalue
    ______________    ____________
    "acceleration"       -0.1561  
    "cylinders"         -0.18306  
    "displacement"      -0.34203  
    "horsepower"        -0.27291  
    "model_year"         -0.2926  
    "weight"            -0.32402  

plot the shapley values for the query point by using the plot function.

plot(explainer)

the horizontal bar graph shows the shapley values for all variables, sorted by their absolute values. each shapley value explains the deviation of the prediction for the query point from the average, due to the corresponding variable.

train a classification model and create a shapley object. then compute the shapley values for multiple query points.

load the creditrating_historical data set. the data set contains customer ids and their financial ratios, industry labels, and credit ratings.

tbl = readtable('creditrating_historical.dat');

train a blackbox model of credit ratings by using the fitcecoc function. use the variables from the second through seventh columns in tbl as the predictor variables.

blackbox = fitcecoc(tbl,'rating', ...
    'predictornames',tbl.properties.variablenames(2:7), ...
    'categoricalpredictors','industry');

create a shapley object with the blackbox model. for faster computation, subsample 25% of the observations from tbl with stratification and use the samples to compute the shapley values. specify to use the extension to the kernel shap algorithm.

rng('default') % for reproducibility
c = cvpartition(tbl.rating,'holdout',0.25);
tbl_s = tbl(test(c),:);
explainer = shapley(blackbox,tbl_s,'method','conditional');

find two query points whose true rating values are aaa and b, respectively.

querypoint(1,:) = tbl_s(find(strcmp(tbl_s.rating,'aaa'),1),:);
querypoint(2,:) = tbl_s(find(strcmp(tbl_s.rating,'b'),1),:)

querypoint=2×8 table
     id      wc_ta     re_ta     ebit_ta    mve_bvtd    s_ta     industry    rating 
    _____    ______    ______    _______    ________    _____    ________    _______
    58258     0.511     0.869     0.106      8.538      0.732       2        {'aaa'}
    82367    -0.078    -0.042     0.011      0.262      0.167       7        {'b'  }

compute and plot the shapley values for the first query point.

explainer1 = fit(explainer,querypoint(1,:));
plot(explainer1)

compute and plot the shapley values for the second query point.

explainer2 = fit(explainer,querypoint(2,:));
plot(explainer2)

the true rating for the second query point is b, but the predicted rating is bb. the plot shows the shapley values for the predicted rating.

explainer1 and explainer2 include the shapley values for the first query point and second query point, respectively.