plot shapley values -凯发k8网页登录

train a classification model and create a shapley object. then plot the shapley values by using the object function plot.

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');

display the first three rows of the table.

head(tbl,3)

     id      wc_ta    re_ta    ebit_ta    mve_bvtd    s_ta     industry    rating
    _____    _____    _____    _______    ________    _____    ________    ______
    62394    0.013    0.104     0.036      0.447      0.142       3        {'bb'}
    48608    0.232    0.335     0.062      1.969      0.281       8        {'a' }
    42444    0.311    0.367     0.074      1.935      0.366       1        {'a' }

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. a recommended practice is to specify the class names to set the order of the classes.

blackbox = fitcecoc(tbl,'rating', ...
    'predictornames',tbl.properties.variablenames(2:7), ...
    'categoricalpredictors','industry', ...
    'classnames',{'aaa' 'aa' 'a' 'bbb' 'bb' 'b' 'ccc'});

create a shapley object that explains the prediction for the last observation. for faster computation, subsample 25% of the observations from tbl with stratification and use the samples to compute the shapley values.

querypoint = tbl(end,:)

querypoint=1×8 table
     id      wc_ta    re_ta    ebit_ta    mve_bvtd    s_ta    industry    rating
    _____    _____    _____    _______    ________    ____    ________    ______
    73104    0.239    0.463     0.065      2.924      0.34       2        {'aa'}

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

for a classification model, shapley computes shapley values using the predicted class score for each class. display the values in the shapleyvalues property.

explainer.shapleyvalues

ans=6×8 table
    predictor        aaa           aa             a            bbb            bb             b            ccc    
    __________    _________    __________    ___________    __________    ___________    __________    __________
    "wc_ta"        0.051045      0.022644      0.0096138     0.0015954      -0.027857      -0.04134     -0.039476
    "re_ta"         0.16729       0.09479        0.05308     -0.011178      -0.087689      -0.20847      -0.29204
    "ebit_ta"     0.0012015    0.00053338     0.00043344    0.00012321    -0.00066994    -0.0013388    -0.0011793
    "mve_bvtd"       1.3377         1.338        0.67839     -0.027654       -0.55142      -0.75327      -0.59578
    "s_ta"        -0.012484     -0.009098    -0.00074119    -0.0035582    -7.3462e-05     0.0014495    -0.0020609
    "industry"    -0.099117     -0.046867      0.0031376      0.080071       0.089726      0.099699       0.15691

the shapleyvalues property contains the shapley values of all features for each class.

plot the shapley values for the predicted class 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 score for the query point from the average score of the predicted class, due to the corresponding variable.

plot the shapley values for all classes by specifying all class names in explainer.blackboxmodel.

plot(explainer,'classnames',explainer.blackboxmodel.classnames)

train a regression model and create a shapley object. use the object function fit to compute the shapley values for the specified query point. then plot the shapley values of the predictors by using the object function plot. specify the number of important predictors to plot when you call the plot function.

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. specify 'numimportantpredictors',5 to plot only the five most important predictors for the predicted response.

plot(explainer,'numimportantpredictors',5)

the horizontal bar graph shows the shapley values for the five most important predictors, 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.