evaluate_results_all.m

clear all
close all

% The current visualization script can only draw the heat map for the case
% when we iterate through the #training_data or #dimensions, assuming the
% other one as a constant value

load('results_all')

num_methods = size(Method_list,2);
num_iterations = size(decisions,2);
num_runs = size(decisions,3);


%display useful information about the simulation
disp(struct2table(sparse_params));
if MODE == 1
    disp('Feedback is on the weight value of features');
end
if MODE == 2
    disp('Feedback is on the relevance of features');
end
disp(['Number of "relevant" features: ', num2str(sum(z_star==1)),'.']);
disp(['Number of "do not know" features: ', num2str(sum(z_star==-1)),'.']);
if size(num_trainingdata,2) == 1
    disp(['The number of training data is fixed to ', num2str(num_trainingdata) ,'. (runs over dimensions).']);
end
if size(num_features,2) == 1
    disp(['The number of dimensions is fixed to ', num2str(num_features) ,'. (runs over training data.)']);
end
disp(['Averaged over ', num2str(num_runs), ' runs']);


for loss_function = 1:2     
    if loss_function == 1
        loss = Loss_1;
    end
    if loss_function == 2
        loss = Loss_2;
    end
    
    if size(num_trainingdata,2) == 1
        %% Assume that the #training_data is fixed and iterate through #dimensions
        t_index = 1;
        figure();
        heat_map = zeros(num_iterations,size(num_features,2),num_methods);       
        for f_index = 1:size(num_features,2)
            cutted_loss = loss(:,:,:,f_index,t_index);
            temp = mean(cutted_loss,3); %average over different runs
            for method = 1:num_methods
                heat_map(:,f_index,method) = temp(method,:)'; %create a heatmap for each method
            end
        end
        temp_num_features = num_features;
        min_val = min(heat_map(:));
        max_val = max(heat_map(:));
        for method = 1:num_methods
            subplot(2,1,method) 
%             figure
            imagesc(heat_map(:,:,method), [min_val,max_val]);
            axis xy
            title(Method_list(method),'FontSize',16)
            set(gca, 'XTick', 1:floor(length(temp_num_features)/10):length(temp_num_features)); % Change x-axis ticks
            set(gca, 'XTickLabel', temp_num_features(1:floor(length(temp_num_features)/10):length(temp_num_features))); % Change x-axis ticks labels.
            xlabel('number of dimensions','FontSize',16)
            ylabel('number of expert feedbacks','FontSize',16)
        %     pcolor(heat_map(:,:,method))
        %     colormap(gray)     
        %     colorbar();
        end       
    end
    
    if size(num_features,2) == 1
        %% Assume that the #dimensions is fixed and iterate through #training_data 
        f_index = 1;
        figure();
        heat_map = zeros(num_iterations,size(num_trainingdata,2),num_methods);
        for t_index = 1:size(num_trainingdata,2)
            cutted_loss = loss(:,:,:,f_index,t_index);
            temp = mean(cutted_loss,3); %average over different runs
            for method = 1:num_methods
                heat_map(:,t_index,method) = temp(method,:)'; %create a heatmap for each method
            end
        end
        temp_num_trainingdata = num_trainingdata;
        %(optional) remove some parts of the figure -  next three lines
%         remove_first_k_iteration=3;
%         heat_map(:,1:remove_first_k_iteration,:) = []; 
%         temp_num_trainingdata = num_trainingdata(remove_first_k_iteration+1:size(num_trainingdata,2));
        min_val = min(heat_map(:));
        max_val = max(heat_map(:));
        for method = 1:num_methods
            subplot(2,1,method) 
%             figure
            imagesc(heat_map(:,:,method), [min_val,max_val]);
            axis xy
            title(Method_list(method),'FontSize',16)
            xlabel('number of training data','FontSize',16)
            set(gca, 'XTick', 1:floor(length(temp_num_trainingdata)/20):length(temp_num_trainingdata)); % Change x-axis ticks
            set(gca, 'XTickLabel', temp_num_trainingdata(1:floor(length(temp_num_trainingdata)/20):length(temp_num_trainingdata))); % Change x-axis ticks labels.        
            ylabel('number of expert feedbacks','FontSize',16)    
        %     pcolor(heat_map(:,:,method))            
        %     colormap(gray)     
        %     colorbar();
        end       
    end   
end