MATLAB: Do I receive “Undefined function or variable” error when calling the functions “predictorImportance” and “oobPermutedPredictorImportance”

oobpermutedpredictorimportancepredictorimportanceStatistics and Machine Learning Toolbox

I have Statistics and Machine Learning Toolbox installed. Why do I receive "Undefined function or variable" error message when calling the functions "predictorImportance" and "oobPermutedPredictorImportance" after MATLAB starts?

Best Answer

The function "predictorImportance" is a method of the "RegressionTree" class. When MATLAB starts, the "RegressionTree" classes are not loaded so the methods of these classes are not available at this point. Hence, one may get an error message like "Undefined function or variable" if he calls that function right after MATLAB starts. If a "RegressionTree" object is created (for example, by calling the function "fitrtree") before "predictorImportance" is executed, then running "predictorImportance" will not result in this type of error.

Similarly, the function "oobPermutedPredictorImportance" is a method of the "RegressionBaggedEnsemble" class. One needs to create a "RegressionBaggedEnsemble" object first before calling this function. One way to create such an object is to call the function "fitrensemble". Calling "oobPermutedPredictorImportance" without first creating a "RegressionBaggedEnsemble" object may result in this type of error.

Related Solutions

MATLAB: How to choose the best kernel for a Gaussian process regression, possibly using “bayesopt” function? How to predict unseen data with the resulting model

There are two ways you could do this. One of them involves "bayesopt" function.

(1) The easiest way is to use the "fitrgp" function. There is an example of how to optimize a fitrgp hyperparameter (Sigma) on the "fitrgp" function Documentation page:

https://www.mathworks.com/help/stats/fitrgp.html?searchHighlight=fitrgp

To optimize ‘KernelFunction’, using default values for all other hyperparameters, you could do the following (using data from the documentation example):

load(fullfile(matlabroot,'examples','stats','gprdata2.mat'))
model1 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction'});

Because fitrgp tends to be sensitive to the Sigma parameter, you will probably get a better result if you simultaneously optimize KernelFunction and Sigma. Also, searching categorical variables inherently requires more function evaluations, so you should probably run it longer:

model2 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction','Sigma'},...
    'HyperparameterOptimizationOptions', struct('MaxObjectiveEvaluations',100));

The "fitrgp" function documentation page lists the hyperparameters that are eligible for optimization, and what values are searched:

https://www.mathworks.com/help/stats/fitrgp.html#input_argument_namevalue_OptimizeHyperparameters.

You can use this model to make predictions on unseen data directly with the "predict" function:

model2 = fitrgp(x,y,'OptimizeHyperparameters',{'KernelFunction','Sigma'},...
    'HyperparameterOptimizationOptions', struct('MaxObjectiveEvaluations',100));
% Predict on unseen data

xNew = 0.2
ypred = predict(model2, xNew)

(2) A second approach would be to use "bayesopt" function directly, defining your own objective function. Here, the objective function is the 5-fold crossvalidation loss of the model.

load(fullfile(matlabroot,'examples','stats','gprdata2.mat')) 
% Define kernel variable
kernel = optimizableVariable('KernelFunction',{'exponential','squaredexponential','matern32','matern52',...
'rationalquadratic','ardexponential','ardsquaredexponential','ardmatern32','ardmatern52','ardrationalquadratic'},...
'Type','categorical') 
% Call bayesopt, capturing x and y in the objective function

bo = bayesopt(@(T)objFcn(T,x,y), kernel)
% Define an objective function
function Loss = objFcn(Vars, x, y)
m = fitrgp(x, y, 'KernelFunction', char(Vars.KernelFunction), 'KFold', 5);Loss = kfoldLoss(m);
end

(2.1) To optimize both KernelFunction and Sigma using "bayesopt" function, you can do the following:

% Define 2 variables
kernel = optimizableVariable('KernelFunction',{'exponential','squaredexponential','matern32','matern52',...
'rationalquadratic','ardexponential','ardsquaredexponential','ardmatern32','ardmatern52','ardrationalquadratic'},...
'Type','categorical')
sigma = optimizableVariable('Sigma',[1e-4,10],'Transform','log')
 
% Call bayesopt, capturing x and y in the objective function
bo = bayesopt(@(T)objFcn(T,x,y), [sigma, kernel], 'MaxObjectiveEvaluations', 100)
 
function Loss = objFcn(Vars, x, y)
m = fitrgp(x, y, 'KernelFunction', char(Vars.KernelFunction), ...
                 'Sigma', Vars.Sigma, 'ConstantSigma', true,...
                 'KFold', 5);
Loss = kfoldLoss(m);
end

To get a model that can make predictions, you need to fit a model without passing the ‘kfold’ argument, but keeping the optimal kernel function obtained from "bayesopt" function.

kernel= bo.XAtMinObjective.KernelFunction
model = fitrgp(x, y, 'KernelFunction', char(kernel));
% Predict on unseen data
xNew = 0.2
ypred = predict(model2, xNew)

MATLAB: Do I receive a “web” error message when clicking certain links in MATLAB

There are several possible solutions for resolving these error messages:

1) Restore MATLAB search path:

Execute the following commands and check if the issue is resolved:

>> restoredefaultpath
>> rehash toolboxcache
>> sl_refresh_customizations

If resolved, save the new path with:

>> savepath

2) Check for shadowing of the "web" and "helpview" functions:

>> which -all web       % Should return only *1* file location
>> which -all helpview  % Should return only *2* file locations

If more file locations are outputted than expected, you can change the name of the user-defined function or remove it from the path.

3) Regenerating preferences:

Visit the link below for instructions to regenerate your MATLAB preferences:

https://www.mathworks.com/matlabcentral/answers/99625-how-do-i-regenerate-my-matlab-preferences

4) Debugging 'web.m':

a) In the MATLAB command window, execute the following line:

>> edit web

b) Next, in the 'web.m' file, place a break point on the line of code which says, "e.throwAsCaller;". This should be on line 213.

c) Reproduce the error

d) Execution will stop at the break point. When this occurs, execute the following line:

>> disp(getReport(e,'extended'))

In one case, this command outputted "Error using strip", which pointed to the underlying cause of the issue. The "strip" command was being shadowed by a user-defined function.

Best Answer

Related Solutions

MATLAB: How to choose the best kernel for a Gaussian process regression, possibly using “bayesopt” function? How to predict unseen data with the resulting model

MATLAB: Do I receive a “web” error message when clicking certain links in MATLAB

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