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log_test.m
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%Program for CSC522 course project: Precipitation prediction of
%Northwestern United States
%run after day_data_preprocessing
%10-fold cross validation model selection
feature_matrix = correlation_matrix;
edges = [0, 0.1, 2.5, 10, 25, 50, 100, 400];
%mod_sel_dis_data = discretize(mod_sel_data, edges);
test_loss = zeros(length(lon), length(lat));
AUC = zeros(length(lon), length(lat));
BS = zeros(length(lon), length(lat));
[~, ~, d, ~] = size(feature_matrix);
[lo, la, dat] = size(mod_sel_data);
%mypool = parpool(4);
paroptions = statset('UseParallel',true);
logt = templateLinear('Learner', 'logistic');
%train_data = [];
%for i=1:dats
% tp = mod_sel_data(:, :, i);
% train_data = [train_data, tp(~no_detect)];
%end
%train_data = train_data';
total_predict_label = [];
total_test_label = [];
for m=1:lo
for n=1:la
if (~no_detect(m, n))
%prepare training data (including cv data)
cur_cost_matrix = cost_matrix;
train_labels = reshape(mod_sel_labels(m, n, :), [length(mod_sel_labels), 1]);
lb = [sum(train_labels==1), sum(train_labels==2), sum(train_labels==3), sum(train_labels==4), sum(train_labels==5)];
for i=0:(length(lb)-1)
tar = length(lb)-i;
if (lb(tar)==0)
cur_cost_matrix(tar, :) = [];
cur_cost_matrix(:, tar) = [];
end
end
train_data = [];
for j=1:(d-1)
cm = feature_matrix(m, n, j, 2);
cn = feature_matrix(m, n, j, 3);
train_data = [train_data; mod_sel_data(cm, cn, :)];
end
train_data = reshape(train_data, [(d-1), length(mod_sel_data)]);
%tmp = train_data;
%train_data = [mean(train_data, 1); max(train_data, [], 1); min(train_data, [], 1); std(train_data, 0, 1)];
train_data = train_data';
%prepare test data
testl = reshape(test_labels(m, n, :), [length(test_labels), 1]);
testd = [];
for j=1:(d-1)
cm = feature_matrix(m, n, j, 2);
cn = feature_matrix(m, n, j, 3);
testd = [testd; test_data(cm, cn, :)];
end
testd = reshape(testd, [(d-1), length(test_data)]);
testd = testd';
%nbmd = fitcecoc(train_data, train_labels, 'Learners', nbt,'Options', paroptions, 'Coding', 'ordinal', 'CrossVal', 'on', 'KFold', 5);
logmd = fitcecoc(train_data, train_labels, 'Learners', logt, 'Coding', 'ordinal','FitPosterior', 1,'Options', paroptions, 'CrossVal', 'off');
%nbmd = fitcnb(train_data, train_labels, 'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expected-improvement-plus'));
[predictl,~,~,postp] = predict(logmd, testd, 'Options', paroptions);
total_predict_label = [total_predict_label; predictl];
total_test_label = [total_test_label; testl];
testloss = 0;
for i=1:length(testl)
testloss = testloss+(testl(i)==predictl(i)); %+ cost_matrix(testl(i), predictl(i));
end
testloss = testloss/length(testl);
disp(testloss);
test_loss(m, n) = test_loss(m, n)+testloss;
frq = [sum(testl==1), sum(testl==2), sum(testl==3), sum(testl==4), sum(testl==5)]/length(testl);
bs = 0;
[~, csize] = size(postp);
for i=1:length(postp)
for j=1:5
if (j<=csize)
bs = bs + (postp(i, j)-frq(j))^2;
else
bs = bs + (frq(j))^2;
end
end
end
BS(m, n) = bs/(length(postp));
class_num = min(csize, length(unique(testl)));
for i=1:class_num
for j=(i+1):class_num
if ((sum(predictl==i)~=0) && (sum(testl==i)~=0) && (sum(predictl==j)~=0) && (sum(testl==j)~=0))
[~,~,~,auc] = perfcurve(predictl, testl, i, 'NegClass', j);
AUC(m, n) = AUC(m, n) + auc ;
end
end
%[~,~,~,auc] = perfcurve(predictl, testl, [1:i], 'NegClass', [(i+1):min(csize, length(testl))]);
end
AUC(m, n) = 2*AUC(m, n)/(class_num*(class_num-1));
end
end
end
confusion_matrix = confusionmat(total_test_label, total_predict_label);
%delete mypool