run crossvalidation measureΒΆ

Matlab output: run_crossvalidation_measure

%% Cross validation measure example
% This example runs cross validation with the
% cosmo_crossvalidation_measure function, using a classifier with n-fold
% crossvalidation.
% It shows the confusion matrices using multiple classifiers
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #

%% Define data
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'ak6','s01');

data_fn=fullfile(data_path,'glm_T_stats_perrun.nii');
mask_fn=fullfile(data_path,'vt_mask.nii');
ds=cosmo_fmri_dataset(data_fn,'mask',mask_fn,...
                        'targets',repmat(1:6,1,10),...
                        'chunks',floor(((1:60)-1)/6)+1);

% remove constant features (due to liberal masking)
ds=cosmo_remove_useless_data(ds);
%% Part 1: Use single classifier

% Assign a function handle to the cosmo_crossvalidation_measure
% function to the variable 'measure'
% Hint: a function handle is a reference to a function. A function
%       handle to the function named 'foo' is expressed by: @foo
%       For more information, run: help function_handle
measure=@cosmo_crossvalidation_measure;

% Make a struct containing the arguments for the measure:
% - classifier: a function handle to cosmo_classify_lda
% - partitions: the output of cosmo_nfold_partitioner applied to the
%               dataset
% Assign the struct to the variable 'args'
args=struct();
args.classifier=@cosmo_classify_lda;
args.partitions=cosmo_nfold_partitioner(ds);

fprintf('Using the following measure:\n');
cosmo_disp(measure,'strlen',Inf); % avoid string truncation

fprintf('\nUsing the following measure arguments:\n');
cosmo_disp(args);

% Apply the measure to ds, with args as second argument. Assign the result
% to the variable 'ds_accuracy'.
ds_accuracy=measure(ds,args);

% Show the result
fprintf('\nOutput dataset (with classification accuracy)\n');
% Show the contents of 'ds_accuracy' using 'cosmo_disp'
cosmo_disp(ds_accuracy);

%% %% Part 2: Compare multiple classifiers

% This exercise shows how multiple classifiers can be run on the same
% data.

% As a cell can contain data of any type, it also supports storage of
% function handles. The syntax is the same as for other types; to put
% handles to the functions named 'foo','bar' and 'baz' in a cell,
% use {@foo, @bar, @baz}
%
% For this exercise, put function handles to cosmo_classify_nn,
% cosmo_classify_naive_bayes and cosmo_classify_lda in a cell,
% and assign the result to a variable
% named 'classifiers'
% (if the SVM classifier is present, it can also be put in this cell)
classifiers={@cosmo_classify_nn,...
             @cosmo_classify_naive_bayes,...
             @cosmo_classify_lda};

% if svm classifier is present (either libsvm or matlab's svm), use that
% too. The solution presented here is system-independent because it is
% checked first that an svm classifier is present
if cosmo_check_external('svm',false)
    classifiers{end+1}=@cosmo_classify_svm;
end

% Print which classifiers are used
nclassifiers=numel(classifiers);
classifier_names=cellfun(@func2str,classifiers,'UniformOutput',false);
fprintf('\n\nUsing %d classifiers: %s\n', nclassifiers, ...
            cosmo_strjoin(classifier_names, ', '));

% Set the measure (again) to a function handle to
% cosmo_crossvalidation_measure, and assign the result to a variable named
% 'measure'
measure=@cosmo_crossvalidation_measure;

% Make a struct containing the arguments for the measure:
% - partitions: the output of cosmo_nfold_partitioner applied to the
% - output:     set to 'predictions' to get the predictions from the
%               classifier
%               (without the 'output' field the output defaults to
%               classification accuracy)
% (Below, in the for-loop, the field 'classifier' is set for each function
%  handle in the cell 'classifiers')

args=struct();
args.partitions=cosmo_nfold_partitioner(ds);
args.output='predictions';

%% Run classifications
% Compute the accuracy and predictions for each classifier, and plot the
% confusion matrix
for k=1:nclassifiers
    % Set the classifier function here:
    % assign args.classifier to the k-th classifier in the cell
    % 'classifiers'.
    args.classifier=classifiers{k};

    % compute predictions using the measure, and assign the result to a
    % variable 'predicted_ds'.
    predicted_ds=measure(ds,args);

    % compute confusion matrix using cosmo_confusion_matrix, and assign the
    % result to a variable 'confusion_matrix'.
    confusion_matrix=cosmo_confusion_matrix(predicted_ds);

    % compute accuracy, and store the result in a variable called
    % 'accuracy'
    sum_diag=sum(diag(confusion_matrix));
    sum_total=sum(confusion_matrix(:));
    accuracy=sum_diag/sum_total;

    % alternative:
    accuracy_alt=mean(predicted_ds.samples==predicted_ds.sa.targets);

    % visualize confusion matrix and show classification accuracy in the
    % title
    figure();
    imagesc(confusion_matrix,[0 10])
    classifier_name=strrep(classifier_names{k},'_',' '); % no underscores
    desc=sprintf('%s: accuracy %.1f%%', classifier_name, accuracy*100);
    title(desc)


    classes = {'monkey','lemur','mallard','warbler','ladybug','lunamoth'};
    nclasses=numel(classes);
    set(gca,'XTick',1:nclasses,'XTickLabel',classes);
    set(gca,'YTick',1:nclasses,'YTickLabel',classes);
    ylabel('target');
    xlabel('predicted');
    colorbar

    % print classificationa accuracy in terminal window
    fprintf('%s\n',desc);
end

% Note: poor performance by some classifiers does not mean that they are
% useless, just that they were unable to capture the distinctions between
% the patterns of different conditions because these distinctions were not
% captured by the classifier's model.