plot
Plot results of local interpretable model-agnostic explanations (LIME)
Since R2020b
Description
plot( visualizes the LIME results in the
results)lime object
results. (since R2023b)
The horizontal bar graph shows the coefficient values of a linear simple model or predictor importance values of a decision tree simple model, depending on the simple model in
results(SimpleModelproperty ofresults).The plot displays two predictions for the query point computed using the machine learning model and the simple model, respectively. These values correspond to the
BlackboxFittedproperty and theSimpleModelFittedproperty ofresults.
Examples
Train a regression model and create a lime object that uses a linear simple model. When you create a lime object, if you do not specify a query point and the number of important predictors, then the software generates samples of a synthetic data set but does not fit a simple model. Use the object function fit to fit a simple model for a query point. Then display the coefficients of the fitted linear simple model by using the object function plot.
Load the carbig data set, which contains measurements of cars made in the 1970s and early 1980s.
load carbigCreate 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);
Create a table of predictor variables by removing the response variable from tbl.
tblX = removevars(tbl,'MPG');Train a blackbox model of MPG by using the fitrkernel function.
rng('default') % For reproducibility mdl = fitrkernel(tblX,tbl.MPG,'CategoricalPredictors',[2 5]);
Create a lime object. Specify a predictor data set because mdl does not contain predictor data.
results = lime(mdl,tblX)
results =
lime with properties:
BlackboxModel: [1×1 RegressionKernel]
DataLocality: 'global'
CategoricalPredictors: [2 5]
Type: 'regression'
X: [392×6 table]
QueryPoint: []
NumImportantPredictors: []
NumSyntheticData: 5000
SyntheticData: [5000×6 table]
Fitted: [5000×1 double]
SimpleModel: []
ImportantPredictors: []
BlackboxFitted: []
SimpleModelFitted: []
results contains the generated synthetic data set. The SimpleModel property is empty ([]).
Fit a linear simple model for the first observation in tblX. Specify the number of important predictors to find as 3.
queryPoint = tblX(1,:)
queryPoint=1×6 table
Acceleration Cylinders Displacement Horsepower Model_Year Weight
____________ _________ ____________ __________ __________ ______
12 8 307 130 70 3504
results = fit(results,queryPoint,3);
Plot the lime object results by using the object function plot.
plot(results)
The plot displays two predictions for the query point, which correspond to the BlackboxFitted property and the SimpleModelFitted property of results.
The horizontal bar graph shows the coefficient values of the simple model, sorted by their absolute values. LIME finds Horsepower, Model_Year, and Cylinders as important predictors for the query point.
Model_Year and Cylinders are categorical predictors that have multiple categories. For a linear simple model, the software creates one less dummy variable than the number of categories for each categorical predictor. The bar graph displays only the most important dummy variable. You can check the coefficients of the other dummy variables using the SimpleModel property of results. Display the sorted coefficient values, including all categorical dummy variables.
[~,I] = sort(abs(results.SimpleModel.Beta),'descend'); table(results.SimpleModel.ExpandedPredictorNames(I)',results.SimpleModel.Beta(I), ... 'VariableNames',{'Expanded Predictor Name','Coefficient'})
ans=17×2 table
Expanded Predictor Name Coefficient
__________________________ ___________
{'Horsepower' } -3.5035e-05
{'Model_Year (74 vs. 70)'} -6.1591e-07
{'Model_Year (80 vs. 70)'} -3.9803e-07
{'Model_Year (81 vs. 70)'} 3.4186e-07
{'Model_Year (82 vs. 70)'} -2.2331e-07
{'Cylinders (6 vs. 8)' } -1.9807e-07
{'Model_Year (76 vs. 70)'} 1.816e-07
{'Cylinders (5 vs. 8)' } 1.7318e-07
{'Model_Year (71 vs. 70)'} 1.5694e-07
{'Model_Year (75 vs. 70)'} 1.5486e-07
{'Model_Year (77 vs. 70)'} 1.5151e-07
{'Model_Year (78 vs. 70)'} 1.3864e-07
{'Model_Year (72 vs. 70)'} 6.8949e-08
{'Cylinders (4 vs. 8)' } 6.3098e-08
{'Model_Year (73 vs. 70)'} 4.9696e-08
{'Model_Year (79 vs. 70)'} -2.4822e-08
⋮
Train a classification model and create a lime object that uses a decision tree simple model. When you create a lime object, specify a query point and the number of important predictors so that the software generates samples of a synthetic data set and fits a simple model for the query point with important predictors. Then display the estimated predictor importance in the simple model 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' }
Create a table of predictor variables by removing the columns of customer IDs and ratings from tbl.
tblX = removevars(tbl,["ID","Rating"]);
Train a blackbox model of credit ratings by using the fitcecoc function.
blackbox = fitcecoc(tblX,tbl.Rating,'CategoricalPredictors','Industry');
Create a lime object that explains the prediction for the last observation using a decision tree simple model. Specify 'NumImportantPredictors' as six to find at most 6 important predictors. If you specify the 'QueryPoint' and 'NumImportantPredictors' values when you create a lime object, then the software generates samples of a synthetic data set and fits a simple interpretable model to the synthetic data set.
queryPoint = tblX(end,:)
queryPoint=1×6 table
WC_TA RE_TA EBIT_TA MVE_BVTD S_TA Industry
_____ _____ _______ ________ ____ ________
0.239 0.463 0.065 2.924 0.34 2
rng('default') % For reproducibility results = lime(blackbox,'QueryPoint',queryPoint,'NumImportantPredictors',6, ... 'SimpleModelType','tree')
results =
lime with properties:
BlackboxModel: [1×1 ClassificationECOC]
DataLocality: 'global'
CategoricalPredictors: 6
Type: 'classification'
X: [3932×6 table]
QueryPoint: [1×6 table]
NumImportantPredictors: 6
NumSyntheticData: 5000
SyntheticData: [5000×6 table]
Fitted: {5000×1 cell}
SimpleModel: [1×1 ClassificationTree]
ImportantPredictors: [2×1 double]
BlackboxFitted: {'AA'}
SimpleModelFitted: {'AA'}
Plot the lime object results by using the object function plot.
f = plot(results);
The plot displays two predictions for the query point, which correspond to the BlackboxFitted property and the SimpleModelFitted property of results.
The horizontal bar graph shows the sorted predictor importance values. lime finds the financial ratio variables MVE_BVTD and RE_TA as important predictors for the query point.
You can read the bar lengths by using data tips or Bar Properties. For example, you can find Bar objects by using the findobj function and add labels to the ends of the bars by using the text function.
b = findobj(f,'Type','bar'); text(b.YEndPoints+0.001,b.XEndPoints,string(b.YData))
Alternatively, you can display the coefficient values in a table with the predictor variable names.
imp = b.YData; flipud(array2table(imp', ... 'RowNames',f.CurrentAxes.YTickLabel,'VariableNames',{'Predictor Importance'}))
ans=2×1 table
Predictor Importance
____________________
MVE_BVTD 0.088412
RE_TA 0.0018061
