diff --git a/High-Dimensional Feature Selection of Medical Data/Code/FeatureSelect1.m b/High-Dimensional Feature Selection of Medical Data/Code/FeatureSelect1.m new file mode 100644 index 0000000..f1e57ad --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/FeatureSelect1.m @@ -0,0 +1,32 @@ +%% Read dataset and preprocessing +x=csvread('train_data1.csv',0,0,[0 0 699 128]); +y=csvread('train_data1.csv',0,129,[0 129 699 140]); +y(y(:,:)==-1) = 0; + +%% T test +pSum = zeros(1,129); +for i=1:12 + nowY = y(:,i); + X0 = x(nowY==0,:); + X1 = x(nowY==1,:); + [~,p,~,~] = ttest2(X0,X1,'Vartype','unequal'); + pSum = pSum+p; +end + +[~,featureIdxSortbyP]= sort(pSum); +x=x(:,featureIdxSortbyP(1:50)); + +%% LASSO regression feature selection +opts = statset('UseParallel',true); +featureSum = zeros(12,1); +featureWeight = zeros(50,1); +for i=1:12 + [B,S] = lassoglm(x,y(:,i),'binomial','DFmax',30,'CV',10,'Alpha',0.5,'Options',opts); + featureSum(i,1) = sum(B(:,S.IndexMinDeviance)~=0); + featureWeight = featureWeight+B(:,S.IndexMinDeviance); +end + +%% Filter +sum=floor(sum(featureSum)/12); +[~,featureIdxSortbyLasso]= sort(featureWeight); +x=x(:,featureIdxSortbyLasso(1:sum)); diff --git a/High-Dimensional Feature Selection of Medical Data/Code/Question1.m b/High-Dimensional Feature Selection of Medical Data/Code/Question1.m new file mode 100644 index 0000000..3c5cfe9 --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/Question1.m @@ -0,0 +1,59 @@ +%% Read dataset +x=csvread('train_data1.csv',0,0,[0 0 699 128]); +y=csvread('train_data1.csv',0,129,[0 129 699 140]); +testX = csvread('test_feature_data1.csv',0,1); +number = csvread('test_feature_data1.csv',0,0,[0 0 203 0]); + +%% Preprocessing +y(y(:,:)==-1) = 0; +net = patternnet(15); + +net.trainFcn = 'trainrp'; +net.trainParam.max_fail = 20; +net.trainParam.epochs = 1000; + +net.divideParam.trainRatio = 70/100; +net.divideParam.valRatio = 15/100; +net.divideParam.testRatio = 15/100; + +results = zeros(204,13); +results(:,1)=number; +errors = zeros(1,700); + +%% Divide into 12 sub problems +for i=1:12 + + % T Test feature selection + nowY = y(:,i); + X0 = x(nowY==0,:); + X1 = x(nowY==1,:); + [~,p,~,~] = ttest2(X0,X1,'Vartype','unequal'); + [~,featureIdxSortbyP]= sort(p); + nowX = x(:,featureIdxSortbyP(1:70)); + + % LASSO regression feature selection + [B,S] = lassoglm(nowX,nowY,'binomial','DFmax',30,'CV',10,'Alpha',0.5); + model = B(:,S.IndexMinDeviance)~=0; + nowX = nowX(:,model); + + nowTestX = testX(:,featureIdxSortbyP(1:70)); + nowTestX = nowTestX(:,model); + + % Train and use NN array + outputs = zeros(1,204); + for j=1:5 + rand = randperm(700); + nowX = nowX(rand,:); + nowY = nowY(rand,:); + nowNet = train(net,nowX',nowY'); + errors = errors+gsubtract(nowY',nowNet(nowX')); + outputs = outputs+nowNet(nowTestX'); + end + + % Generate result + results(:,i+1) = outputs'/5; +end + +%% Output result +disp(sum(abs(errors))/(700*60)); +csvwrite('result1.csv',results); \ No newline at end of file diff --git a/High-Dimensional Feature Selection of Medical Data/Code/Question2.m b/High-Dimensional Feature Selection of Medical Data/Code/Question2.m new file mode 100644 index 0000000..7ed954a --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/Question2.m @@ -0,0 +1,86 @@ +%% Read dataset and preprocessing +x=csvread('train_data2.csv',0,0,[0 0 3999 409]); +y=csvread('train_data2.csv',0,410,[0 410 3999 410]); +X1 = x(y==0,:); +X2 = x(y==1,:); +X3 = x(y==2,:); + +%% T Test +[~,p12,~,~] = ttest2(X1,X2,'Vartype','unequal'); +[~,p13,~,~] = ttest2(X2,X3,'Vartype','unequal'); +[~,p23,~,~] = ttest2(X1,X3,'Vartype','unequal'); +p=p12+p13+p23; +[~,featureIdxSortbyP]= sort(p); + +%% LASSO regression feature selection + +x=x(:,featureIdxSortbyP(1:100)); +opts = statset('UseParallel',true); +% Linear Regression +[B,S] = lasso(x,y,'DFmax',50,'CV',10,'Alpha',0.5,'Options',opts); +% Poisson Regression +% [B,S] = lassoglm(x,y,'poisson','DFmax',50,'CV',10,'Alpha',0.5,'Options',opts); +model = B(:,S.Index1SE)~=0; +x=x(:,model); + +clear X1 X2 X3 p p12 p13 p23 B S + +%% Process class label +newY=zeros(4000,3); +newY(y==0,1)=1; +newY(y==1,2)=1; +newY(y==2,3)=1; +y=newY; +clear newY; + +%% Build NN parameters +neurons = sum(model~=0); +net = patternnet(floor(neurons/2)); +net.divideParam.trainRatio = 70/100; +net.divideParam.valRatio = 15/100; +net.divideParam.testRatio = 15/100; + +net.trainFcn = 'trainrp'; +net.trainParam.max_fail = 20; +net.trainParam.epochs = 1000; + +%% Train NN array +errorsArray=zeros(1,20); +nets = cell(1,20); + +for i=1:20 + % Randomly disorganize sample order + rand = randperm(4000); + x = x(rand,:); + y = y(rand,:); + + nets{i} = train(net,x',y'); + neti=nets{i}; + outputs = neti(x'); + errors = gsubtract(y',outputs); + errorsArray(i) = sum(sum(abs(errors)))/12000; +end + +%% Select 10 best NN by error information + +[~,IdxSortbyerrors]= sort(errorsArray,'ascend'); +nets = nets(IdxSortbyerrors(1:10)); +disp(mean(errorsArray(IdxSortbyerrors(1:10)))); +clear errors errorsArray i net neti neurons opts outputs rand IdxSortbyerrors + +%% Read test data +testX = csvread('test_feature_data2.csv',0,1); +number = csvread('test_feature_data2.csv',0,0,[0 0 1030 0]); +testX = testX(:,featureIdxSortbyP(1:100)); +testX = testX(:,model); + +%% Classify by NN +testY = zeros(3,1031); +for i=1:10 + neti = nets{i}; + testY = testY+neti(testX'); +end +testY = testY/10; +testY = (vec2ind(testY)-1)'; +testY = [number,testY]; +csvwrite('result2.csv',testY); diff --git a/High-Dimensional Feature Selection of Medical Data/Code/lasso.m b/High-Dimensional Feature Selection of Medical Data/Code/lasso.m new file mode 100644 index 0000000..95b23dd --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/lasso.m @@ -0,0 +1,12 @@ +%% Read dataset +x=csvread('train_data2.csv',0,0,[0 0 3999 409]); +y=csvread('train_data2.csv',0,410,[0 410 3999 410]); + +%% LASSO regression feature selection + +opts = statset('UseParallel',true); +% Linear Regression +[B,S] = lasso(x,y,'DFmax',100,'CV',10,'Alpha',0.5,'Options',opts); +% Poisson Regression +[B,S] = lassoglm(x,y,'poisson','DFmax',100,'CV',10,'Alpha',0.5,'Options',opts); +model = B(:,S.Index1SE)~=0; diff --git a/High-Dimensional Feature Selection of Medical Data/Code/lassoSelect.m b/High-Dimensional Feature Selection of Medical Data/Code/lassoSelect.m new file mode 100644 index 0000000..e8f4d7e --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/lassoSelect.m @@ -0,0 +1,26 @@ +%% Read dataset and preprocessing +x=csvread('train_data2.csv',0,0,[0 0 3999 409]); +y=csvread('train_data2.csv',0,410,[0 410 3999 410]); +X1 = x(y==0,:); +X2 = x(y==1,:); +X3 = x(y==2,:); + +%% T test +[~,p12,~,~] = ttest2(X1,X2,'Vartype','unequal'); +[~,p13,~,~] = ttest2(X2,X3,'Vartype','unequal'); +[~,p23,~,~] = ttest2(X1,X3,'Vartype','unequal'); +p=p12+p13+p23; +[~,featureIdxSortbyP]= sort(p); + +%% LASSO regression feature selection + +x=x(:,featureIdxSortbyP(1:100)); +opts = statset('UseParallel',true); +% Linear Regression +[B,S] = lasso(x,y,'DFmax',100,'CV',10,'Alpha',0.5,'Options',opts); +% Poisson Regression +% [B,S] = lassoglm(x,y,'poisson','DFmax',100,'CV',10,'Alpha',0.5,'Options',opts); +model = B(:,S.Index1SE)~=0; +x=x(:,model); + +%% SVM diff --git a/High-Dimensional Feature Selection of Medical Data/Code/useNN.m b/High-Dimensional Feature Selection of Medical Data/Code/useNN.m new file mode 100644 index 0000000..3056aeb --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Code/useNN.m @@ -0,0 +1,14 @@ +%% Read dataset + +testX = csvread('test_feature_data2.csv',0,1); +number = csvread('test_feature_data2.csv',0,0,[0 0 1030 0]); +testX = testX(:,featureIdxSortbyP(1:100)); +testX = testX(:,model); + +%% Neural Network +testY = net(testX'); +testY = (vec2ind(testY)-1)'; +testY = [number,testY]; + +%% Outpur data +csvwrite('result2.csv',testY); \ No newline at end of file diff --git a/High-Dimensional Feature Selection of Medical Data/Data/example_task/example_task1.csv b/High-Dimensional Feature Selection of Medical Data/Data/example_task/example_task1.csv new file mode 100644 index 0000000..cc6c1e3 --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Data/example_task/example_task1.csv @@ -0,0 +1,50 @@ +1,0.82,0.53,0.81,0.77,0.12,0.53,0.81,0.47,0.12,0.53,0.83,0.11 +2,0.72,0.53,0.81,0.71,0.1,0.51,0.74,0.77,0.72,0.93,0.83,0.11 +3,0.68,0.53,0.81,0.77,0.12,0.52,0.81,0.37,0.12,0.53,0.83,0.11 +4,0.62,0.53,0.83,0.77,0.12,0.53,0.63,0.27,0.72,0.63,0.83,0.11 +5,0.21,0.53,0.81,0.77,0.12,0.56,0.81,0.77,0.12,0.53,0.83,0.11 +6,0.12,0.53,0.81,0.77,0.11,0.53,0.22,0.17,0.92,0.43,0.83,0.11 +7,0.22,0.53,0.82,0.76,0.12,0.53,0.29,0.67,0.12,0.53,0.83,0.11 +8,0.12,0.53,0.81,0.77,0.14,0.58,0.71,0.37,0.12,0.73,0.83,0.11 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of Medical Data/Data/task1/Average_precision.txt @@ -0,0 +1,52 @@ +function Average_Precision=Average_precision(Outputs,test_target) +%Computing the average precision +%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i) +%test_target: the actual labels of the test instances, if the ith instance belong to the jth class, test_target(j,i)=1, otherwise test_target(j,i)=-1 + + [num_class,num_instance]=size(Outputs); + temp_Outputs=[]; + temp_test_target=[]; + for i=1:num_instance + temp=test_target(:,i); + if((sum(temp)~=num_class)&(sum(temp)~=-num_class)) + temp_Outputs=[temp_Outputs,Outputs(:,i)]; + temp_test_target=[temp_test_target,temp]; + end + end + Outputs=temp_Outputs; + test_target=temp_test_target; + [num_class,num_instance]=size(Outputs); + + Label=cell(num_instance,1); + not_Label=cell(num_instance,1); + Label_size=zeros(1,num_instance); + for i=1:num_instance + temp=test_target(:,i); + Label_size(1,i)=sum(temp==ones(num_class,1)); + for j=1:num_class + if(temp(j)==1) + Label{i,1}=[Label{i,1},j]; + else + not_Label{i,1}=[not_Label{i,1},j]; + end + end + end + + aveprec=0; + for i=1:num_instance + temp=Outputs(:,i); + [tempvalue,index]=sort(temp); + indicator=zeros(1,num_class); + for m=1:Label_size(i) + [tempvalue,loc]=ismember(Label{i,1}(m),index); + indicator(1,loc)=1; + end + summary=0; + for m=1:Label_size(i) + [tempvalue,loc]=ismember(Label{i,1}(m),index); + summary=summary+sum(indicator(loc:num_class))/(num_class-loc+1); + end + ap_binary(i)=summary/Label_size(i); + aveprec=aveprec+summary/Label_size(i); + end + Average_Precision=aveprec/num_instance; \ No newline at end of file diff --git a/High-Dimensional Feature Selection of Medical Data/Data/task1/test_feature_data.csv b/High-Dimensional Feature Selection of Medical Data/Data/task1/test_feature_data.csv new file mode 100644 index 0000000..4408d66 --- /dev/null +++ b/High-Dimensional Feature Selection of 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goes here +% Detailed explanation goes here + +Y(Y ~= 1) = 0; +Y_hat(Y_hat ~= 1) = 0; + +num_samples = size(Y, 1); + +result = 0; +for i = 1:num_samples + Y_i = Y(i, :); + Y_hat_i = Y_hat(i, :); + + result_i = 2*nnz(Y_i & Y_hat_i)/(nnz(Y_i) + nnz(Y_hat_i)); + + if isnan(result_i) + result_i = 0; + end + + result = result + result_i; +end + +result = result/num_samples; + +end + diff --git a/High-Dimensional Feature Selection of Medical Data/Data/task2/Precision.txt b/High-Dimensional Feature Selection of Medical Data/Data/task2/Precision.txt new file mode 100644 index 0000000..f7838c0 --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Data/task2/Precision.txt @@ -0,0 +1,27 @@ +function [ result ] = Precision( Y_hat, Y) +%CALCPRECISION Summary of this function goes here +% Detailed explanation goes here + +Y(Y ~= 1) = 0; +Y_hat(Y_hat ~= 1) = 0; + +num_samples = size(Y, 1); + +result = 0; +for i = 1:num_samples + Y_i = Y(i, :); + Y_hat_i = Y_hat(i, :); + + result_i = nnz(Y_i & Y_hat_i)/nnz(Y_hat_i); + + if isnan(result_i) + result_i = 0; + end + + result = result + result_i; +end + +result = result/num_samples; + +end + diff --git a/High-Dimensional Feature Selection of Medical Data/Data/task2/Recall.txt b/High-Dimensional Feature Selection of Medical Data/Data/task2/Recall.txt new file mode 100644 index 0000000..1901a0b --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Data/task2/Recall.txt @@ -0,0 +1,26 @@ +function [ result ] = Recall( Y_hat, Y) +%CALCRECALL Summary of this function goes here +% Detailed explanation goes here + +Y(Y ~= 1) = 0; +Y_hat(Y_hat ~= 1) = 0; + +num_samples = size(Y, 1); + +result = 0; +for i = 1:num_samples + Y_i = Y(i, :); + Y_hat_i = Y_hat(i, :); + + result_i = nnz(Y_i & Y_hat_i)/nnz(Y_i); + if isnan(result_i) + result_i = 0; + end + + result = result + result_i; +end + +result = result/num_samples; + +end + diff --git a/High-Dimensional Feature Selection of Medical Data/Data/task2/test_feature_data.csv b/High-Dimensional Feature Selection of Medical Data/Data/task2/test_feature_data.csv new file mode 100644 index 0000000..bfad94b --- /dev/null +++ b/High-Dimensional Feature Selection of Medical Data/Data/task2/test_feature_data.csv @@ -0,0 +1,1031 @@ 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