function area = calc_roc(fileName,varargin)
%
% RECEIVER-OPERATING CHARACTERISTICS (ROC) CURVE
%
%	Receiver-Operating Characteristics (ROC) curve and the area
%	under the curve is returned for one or more experiments on the
%	same dataset.
%
%	area = calc_roc(fileName,exp)
%
%		fileName: The name of the output file. It is an image
%		exp: a structure representing an experiment with three fields
%			- exp.name: a string specifying the experiment name
%			- exp.pos: positive class predictions
%			- exp.neg: negative class predictions
%		area: a 1x1-dimensional cell array of the area under the roc curve.
%
%	The function works for unlimited number of experiments. Therefore, the followings
%	are all possible.
%
%	area = roc(fileName,exp1,exp2)
%		fileName: the same as above.
%		exp1: the same as exp given above.
%		exp2: another experiment result on the same dataset.
%		area: a 1x2-dimensional cell array of the area under the roc curves.
%
%	Similarly,
%	area = roc(fileName,exp1,exp2,...,expN) is also possible.
%
%	Anshul Kundaje
%	Jan 12, 2009

clc;
close all;
colors='rbmkgcye';
types='.ox+*sw';
exp_n = nargin-1;	% number of experiments

handle=figure; hold on; title('ROC CURVE (Boosting with alternating decision tree)'); 
xlabel('FALSE POSITIVE RATIO'); ylabel('TRUE POSITIVE RATIO'); axis([-0.01 1.00001 -0.0001 1.00001]);
leg_names = cell(1,exp_n); %leg_names{nargin} = '';

area = cell(1,exp_n);

for i=1:1:exp_n		% for all experiments
	scolor=colors(mod(i,size(colors,2))); stype=types(mod(i,size(types,2)));
	leg_names{i} = varargin{i}.name;
    
    preds = [varargin{i}.pos; varargin{i}.neg]; % all predicted values
    nb_pos = length(varargin{i}.pos); % number of positives
    nb_neg = length(varargin{i}.neg); % number of negatives    
    nb_total = nb_pos + nb_neg; % total number of values
    
    labels = [ones(nb_pos,1);zeros(nb_neg,1)]; % label all positives with 1 and negatives with 0
    
	[preds,idx] = sort(preds,1,'descend'); % sort the predictions in descending order
    labels = labels(idx); % sort labels accordingly
    
    truepos = cumsum(labels); % TRUE POSITIVE RATE
    falsepos = ((1:nb_total)' - truepos); % FALSE POSITIVE RATE
    flags = ([diff(preds);1]~=0); % Select unique thresholds
    truepos = truepos(flags)/nb_pos;
    falsepos = falsepos(flags)/nb_neg;
    
    % 	form = sprintf('-%c%c', scolor, stype);
    form = sprintf('-%c.', scolor);
	plot(falsepos, truepos, form);
    area{1,i} = sum((falsepos(2:end)-falsepos(1:end-1)).* ...
        (truepos(2:end)+truepos(1:end-1))/2);
    leg_names{i} = [leg_names{i},sprintf(' AUC=%.4f',area{1,i})];
end
legend(leg_names,'Location','SouthEast');

fprintf('SAVING FILE %s\n',fileName);

print('-depsc','-r300',fileName);
hold off;