run classify ldaΒΆ

Matlab output: run_classify_lda

%% odd-even classification with LDA classifier
%
% #   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');

% Load the dataset with VT mask
ds = cosmo_fmri_dataset([data_path '/glm_T_stats_perrun.nii'], ...
                     'mask', [data_path '/vt_mask.nii']);

% remove constant features
ds=cosmo_remove_useless_data(ds);

%% set sample attributes

ds.sa.targets = repmat((1:6)',10,1);
ds.sa.chunks = floor(((1:60)-1)/6)'+1;

% Add labels as sample attributes
classes = {'monkey','lemur','mallard','warbler','ladybug','lunamoth'};
ds.sa.labels = repmat(classes,1,10)';

%% Part 1: bird classification; train on even runs, test on odd runs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% slice into odd and even runs using ds.sa.chunks attribute, and
% store in new dataset structs called 'ds_even' and 'ds_odd'.
% (hint: use the 'mod' function (remainder after division) to see which
% chunks are even or odd)
even_msk = mod(ds.sa.chunks,2)==0;
odd_msk = mod(ds.sa.chunks,2)==1;

ds_even = cosmo_slice(ds,even_msk);
ds_odd = cosmo_slice(ds,odd_msk);

%% discriminate between mallards and warblers
categories={'mallard','warbler'};

% select samples where .sa.labels match on of the categories
% for the even and odd runs seperately. Slice the dataset twice and store
% the result in 'ds_even_birds' and 'ds_odd_birds'
% (use cosmo_match with .sa.labels and categories to define a mask,
% then cosmo_slice to select the data)
msk_even_birds=cosmo_match(ds_even.sa.labels,categories);
ds_even_birds=cosmo_slice(ds_even,msk_even_birds);

msk_odd_birds=cosmo_match(ds_odd.sa.labels,categories);
ds_odd_birds=cosmo_slice(ds_odd,msk_odd_birds);

% show the data
fprintf('Even data:\n')
cosmo_disp(ds_even_birds);

fprintf('Odd data:\n')
cosmo_disp(ds_odd_birds);

% train on even, test on odd
%
% Use cosmo_classify_lda to get predicted targets for the odd runs when
% training on the even runs, and assign these predictions to
% a variable 'test_pred'.
% (hint: use .samples and .sa.targets from ds_even_birds, and
%        use .samples from ds_odd_birds)
train_samples=ds_even_birds.samples;
train_targets=ds_even_birds.sa.targets;
test_samples=ds_odd_birds.samples;

test_pred=cosmo_classify_lda(train_samples,train_targets,...
                                    test_samples);

% Assign the real tagets of the odd runs to a variable 'test_targets'
test_targets=ds_odd_birds.sa.targets;

% show real and predicted labels
fprintf('\ntarget predicted\n');
disp([test_targets test_pred])

% compare the predicted labels for the odd
% runs with the actual targets to compute the accuracy. Store the accuracy
% in a variable 'accuracy'.
accuracy = mean(test_pred==test_targets);
fprintf('\nLDA birds even-odd: accuracy %.3f\n', accuracy);

% compare with naive bayes classification
% (hint: do classification as above, but use cosmo_classify_naive_bayes)
test_pred_nb=cosmo_classify_naive_bayes(train_samples,train_targets,...
                                     test_samples);

test_targets=ds_odd_birds.sa.targets;
accuracy = mean(test_pred_nb==test_targets);
fprintf('\nNaive Bayes birds even-odd: accuracy %.3f\n', accuracy);


%% Part 2: all categories; train/test on even/odd runs and vice versa
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% This is as above, but without slicing to get the samples with bird
% species. In other words, just use 'ds_even' and 'ds_odd'
%
% First, train on even, test on odd
train_samples=ds_even.samples;
train_targets=ds_even.sa.targets;
test_samples=ds_odd.samples;

test_pred=cosmo_classify_lda(train_samples,train_targets,test_samples);

test_targets=ds_odd.sa.targets;
accuracy = mean(test_pred==test_targets);
fprintf('\nLDA all categories even-odd: accuracy %.3f\n', accuracy);

% Now train on odd, test on even
train_samples=ds_odd.samples;
train_targets=ds_odd.sa.targets;
test_samples=ds_even.samples;

test_pred=cosmo_classify_lda(train_samples,train_targets,test_samples);

test_targets=ds_even.sa.targets;
accuracy = mean(test_pred==test_targets);
fprintf('\nLDA all categories odd-even: accuracy %.3f\n', accuracy);


%% Part 3: build confusion matrix
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% manually build the confusion matrix for the six categories

% first, allocate space for the confusion matrix
nclasses=numel(classes); % should be 6
confusion_matrix=zeros(nclasses); % 6x6 matrix

% sanity check to ensure targets are in range 1..6
assert(isequal(unique(test_targets),(1:6)'));

% in confusion matrix, the i-th row and j-th column should contain
% the number of times that a sample with test_targets==i was predicted as
% test_pred==j. Use a nested for-loop (a for-loop in a for-loop) to count
% this for all combinations of i (1 to 6) and j (1 to 6)
for predicted=1:nclasses
    for target=1:nclasses
        match_mask=test_pred==predicted & test_targets==target;
        match_count=sum(match_mask);
        confusion_matrix(target,predicted)=match_count;
    end
end

% CoSMoMVPA can generate the confusion matrix using cosmo_confusion_matrix;
% the check below ensures that your solution matches the one produced by
% CoSMoMVPA
confusion_matrix_alt=cosmo_confusion_matrix(test_targets,test_pred);
if ~isequal(confusion_matrix,confusion_matrix_alt)
    error('your confusion matrix does not match the expected output');
end

figure
imagesc(confusion_matrix,[0 5])
title('confusion matrix');
set(gca,'XTick',1:nclasses,'XTickLabel',classes);
set(gca,'YTick',1:nclasses,'YTickLabel',classes);
ylabel('target');
xlabel('predicted');
colorbar