Classification Learner App - generate a more generic function for your model training that could take varying size of prediction tables? One step beyond the default function generation

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Andy 2018-5-27

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关闭： MATLAB Answer Bot 2021-8-20

This is a question with an answer, that could easily translate into a feature implementation. When one generates a function from a Classification Learner App on a found model, typically the Function content is really static:

1) It is tied to the original Prediction Table Column Names / Properties. 2) As a consequence, the function cannot be used on a different table that might have more or less columns [but where we still have y as the last column] (why would you do that is another topic all-together).

I found that modifying the original function file with the below function:

        function nameNewExt = aAdjustTrainClassifier(~,trainClassFile)
        % aAdjustTrainClassifier
        %
        %
            fidi=fopen(trainClassFile,'r');
            [~,name,~]=fileparts(trainClassFile);
            nameNew = strcat('aAdjusted_',name);
            nameNewExt = strcat(nameNew,'.m');
            if exist(nameNewExt, 'file') == 2
                % file already exists, no overwrite ... 
            else
                fido=fopen(nameNewExt,'w');
                while ~feof(fidi)
                  l=fgetl(fidi);   % read line
                  if     startsWith(l,'predictorNames = ')
                    % modify line here
                    l='predictorNames = inputTable.Properties.VariableNames(:,1:end-1);';
                  elseif startsWith(l,'isCategoricalPredictor = ')
                    l='isCategoricalPredictor = false(1,length(predictorNames));';
                  elseif contains(l,strcat('function [trainedClassifier, validationAccuracy] ='," ",name,'(trainingData)'))
                    l=strcat('function [trainedClassifier, validationAccuracy] ='," ",nameNew,'(trainingData)');
                  elseif startsWith(l,'trainedClassifier.RequiredVariables = ')
                      l=strcat('trainedClassifier.RequiredVariables ='," ",'inputTable.Properties.VariableNames(:,1:end-1)');
                  end
                  fprintf(fido,'%s\n',l);  % 'fgetl returns \n so it's embedded
                end
                fclose(fido);
            end
            fclose(fidi);
            fclose('all');
        end

does the trick. Some static assumptions made (as for my use-case), and this might not work in all instances, however - would be grateful if you could provide feedback / improvements. Another reason for posting is - this could easily translate into a GREAT feature to be implemented.

Thanks

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Answer 1

Bernhard Suhm 2018-6-12

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But if your table has fewer columns (predictors) typically you need to retrain the model, or at least have performance deteriorate significantly!?

Classification Learner App - generate a more generic function for your model training that could take varying size of prediction tables? One step beyond the default function generation

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