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func_Prepare_Data.m
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func_Prepare_Data.m
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function [A,B,C,Init_dim,SET] = func_Prepare_Data(Parm)
%Prepare_Data with DeepInsight
%
%Description of Datasets
% 1. dataset1.mat is RNA-seq (1.5GB)
% 2. dataset2.mat is ringnorm-DELVE (560KB)
% 3. dataset3.mat is vowels (3.6MB)
% 4. dataset4.mat is Madelon (2.1MB)
% 5. dataset5.mat is Relathe (696KB)
if Parm.SnowFall==1
Parm.MPS_Fix=0;
Parm.SnowFall_A = Parm.Max_Px_Size;
Parm.SnowFall_B = Parm.Max_Px_Size;
Parm.Max_Px_Size = 1e+5;%inf; % inf gives precision error if data values are very large eg 1e+17
end
if strcmp(Parm.PATH{3}(end),'/')==0
Parm.PATH{3} = [Parm.PATH{3},'/'];
end
current_dir=pwd;
cd(Parm.PATH{3})
%load(['dataset1.mat']);
load(Parm.Dataname);
cd(current_dir);
Init_dim = dset.dim;
SET=dset.Set;
%##### augment data ####
% for j=1:dset.class
% cnt=1;
% tr=[];
% for m=1+sum(dset.num_tr(1:j-1)):sum(dset.num_tr(1:j))-1
% for n=2+sum(dset.num_tr(1:j-1)):sum(dset.num_tr(1:j))
% if cnt<=1000
% tr(:,cnt,:) = [0.5*(dset.Xtrain(:,m,:)+dset.Xtrain(:,n,:))];
% cnt=cnt+1;
% end
% end
% end
% Num_TR(j)=size(tr,2)+dset.num_tr(j);
% TR{j}=cat(2,dset.Xtrain(:,1+sum(dset.num_tr(1:j-1)):sum(dset.num_tr(1:j)),:),tr);
% end
% dset.Xtrain=[TR{1},TR{2}];
% dset.num_tr=Num_TR;
% clear tr TR cnt Num_TR
%#######################
if Parm.ApplyFS==1
Sel = LogRegRed(dset);
dset.Xtrain = dset.Xtrain(Sel,:,:);
dset.Xtest = dset.Xtest(Sel,:,:);
dset.dim = length(Sel);
Init_dim = dset.dim;
end
% Prepare_Data(dset,Parm);
%
% dset is a data struct (dset.Xtrain, dset.XValidation,...)
%
% Out is the output in struct format
%fprintf(Parm.fid,'\nDataset: %s\n',dset.Set);
fprintf('\nDataset: %s\n',dset.Set);
Stages=Parm.Stage; % {1,2,3,4 or 5}
FileRun = Parm.FileRun; %Run1, Run2, Run3 or Run4
Directory = ['Models/',FileRun,'/Stage1'];
genesCompressed = 'no'; %'yes' or 'no'
if Stages > 1
%RUN1, Run2 etc.
cd(Directory)
if strcmp(lower(genesCompressed),'yes')==1
Stage1 = load('Genes_compressed.mat');
Genes_compressed = Stage1.Genes_compressed;
Init_dim = length(Genes_compressed);
else
Stage1 = load('Genes.mat');
Genes = Stage1.Genes;
Init_dim = length(Genes);
end
%Stage1 = load('Genes_compressed.mat');
%Genes_compressed = Stage1.Genes_compressed;
% This is the output of Stage 1, using this line will execute Stage 2
if Stages > 2
cd ../Stage2
if strcmp(lower(genesCompressed),'yes')==1
Stage2 = load('Genes_compressed.mat');
Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed);
else
Stage2 = load('Genes.mat');
Genes = Stage1.Genes(Stage2.Genes);
end
%Stage2 = load('Genes_compressed.mat');
%Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed);
% This is the output of Stage 2, using this line will execute Stage 3
end
if Stages > 3
cd ../Stage3
if strcmp(lower(genesCompressed),'yes')==1
Stage3 = load('Genes_compressed.mat');
Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed(Stage3.Genes_compressed));
else
Stage3 = load('Genes.mat');
Genes = Stage1.Genes(Stage2.Genes(Stage3.Genes));
end
%Stage3 = load('Genes_compressed.mat');
%Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed(Stage3.Genes_compressed));
%This is the output of Stage 3, using this line will execute Stage 4
end
if Stages > 4
cd ../Stage4
if strcmp(lower(genesCompressed),'yes')==1
Stage4 = load('Genes_compressed.mat');
Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed(Stage3.Genes_compressed(Stage4.Genes_compressed)));
else
Stage4 = load('Genes.mat');
Genes = Stage1.Genes(Stage2.Genes(Stage3.Genes(Stage4.Genes)));
end
%This is the output of Stage 4, using this line will execute Stage 5
end
if Stages > 5
cd ../Stage5
if strcmp(lower(genesCompressed),'yes')==1
Stage5 = load('Genes_compressed.mat');
Genes_compressed = Stage1.Genes_compressed(Stage2.Genes_compressed(Stage3.Genes_compressed(Stage4.Genes_compressed(Stage5.Genes_compressed))));
else
Stage5 = load('Genes.mat');
Genes = Stage1.Genes(Stage2.Genes(Stage3.Genes(Stage4.Genes(Stage5.Genes))));
end
%This is the output of Stage 5, using this line will execute Stage 6
end
cd ../../../
if strcmp(lower(genesCompressed),'yes')==1
dset.Xtrain = dset.Xtrain(Genes_compressed,:,:);
dset.Xtest = dset.Xtest(Genes_compressed,:,:);
dset.dim = length(Genes_compressed);
else
dset.Xtrain = dset.Xtrain(Genes,:,:);
dset.Xtest = dset.Xtest(Genes,:,:);
dset.dim = length(Genes);
end
end
% for tsne exact algorithm, it is important to remove all zero rows
% if dset.dim < 5000
% for j=1:size(dset.Xtrain,1)
% q(j)=sum(dset.Xtrain(j,:));
% end
% r=(q~=0);
% dset.Xtrain = dset.Xtrain(r,:);
% dset.Xtest = dset.Xtest(r,:);
% end
if Parm.Norm==0
Out1 = Prepare_Data_norm(dset,1,Parm);
Out1.Method = Parm.Method;
Out1.Max_Px_Size = Parm.Max_Px_Size;
Out1.SnowFall = Parm.SnowFall;
if Parm.Augment==1
Out1.orgYTrain = Out1.YTrain;
Out1.orgYValidation = Out1.YValidation;
if Parm.AugMeth==1
[Out1.XTrain,Out1.YTrain] = augmentDeepInsight(Out1.XTrain,Out1.YTrain);
[Out1.XValidation,Out1.YValidation] = augmentDeepInsight(Out1.XValidation,Out1.YValidation);
else
[Out1.XTrain,Out1.YTrain] = augmentDeepInsight2(Out1.XTrain,Out1.YTrain,Parm.aug_tr);
[Out1.XValidation,Out1.YValidation] = augmentDeepInsight2(Out1.XValidation,Out1.YValidation,Parm.aug_val);
end
end
A = Out1.A;
B = Out1.B;
C = Out1.C;
save('Out1.mat','-struct','Out1','-v7.3');
Out2 = Prepare_Data_norm(dset,2,Parm);
Out2.Method = Parm.Method;
Out2.Max_Px_Size = Parm.Max_Px_Size;
Out2.SnowFall = Parm.SnowFall;
if Parm.Augment==1
Out2.orgYTrain = Out2.YTrain;
Out2.orgYValidation = Out2.YValidation;
if Parm.AugMeth==1
[Out2.XTrain,Out2.YTrain] = augmentDeepInsight(Out2.XTrain,Out2.YTrain);
[Out2.XValidation,Out2.YValidation] = augmentDeepInsight(Out2.XValidation,Out2.YValidation);
else
[Out2.XTrain,Out2.YTrain] = augmentDeepInsight2(Out2.XTrain,Out2.YTrain,Parm.aug_tr);
[Out2.XValidation,Out2.YValidation] = augmentDeepInsight2(Out2.XValidation,Out2.YValidation,Parm.aug_val);
end
end
A = Out2.A;
B = Out2.B;
C = Out2.C;
save('Out2.mat','-struct','Out2','-v7.3');
elseif Parm.Norm==1
Out1 = Prepare_Data_norm(dset,1,Parm);
Out1.Method = Parm.Method;
Out1.Max_Px_Size = Parm.Max_Px_Size;
Out1.SnowFall = Parm.SnowFall;
if Parm.Augment==1
Out1.orgYTrain = Out1.YTrain;
Out1.orgYValidation = Out1.YValidation;
if Parm.AugMeth==1
[Out1.XTrain,Out1.YTrain] = augmentDeepInsight(Out1.XTrain,Out1.YTrain);
[Out1.XValidation,Out1.YValidation] = augmentDeepInsight(Out1.XValidation,Out1.YValidation);
else
[Out1.XTrain,Out1.YTrain] = augmentDeepInsight2(Out1.XTrain,Out1.YTrain,Parm.aug_tr);
[Out1.XValidation,Out1.YValidation] = augmentDeepInsight2(Out1.XValidation,Out1.YValidation,Parm.aug_val);
end
end
A = Out1.A;
B = Out1.B;
C = Out1.C;
save('Out1.mat','-struct','Out1','-v7.3');
elseif Parm.Norm==2
Out2 = Prepare_Data_norm(dset,2,Parm);
Out2.Method = Parm.Method;
Out2.Max_Px_Size = Parm.Max_Px_Size;
Out2.SnowFall = Parm.SnowFall;
if Parm.Augment==1
Out2.orgYTrain = Out2.YTrain;
Out2.orgYValidation = Out2.YValidation;
if Parm.AugMeth==1
[Out2.XTrain,Out2.YTrain] = augmentDeepInsight(Out2.XTrain,Out2.YTrain);
[Out2.XValidation,Out2.YValidation] = augmentDeepInsight(Out2.XValidation,Out2.YValidation);
else
[Out2.XTrain,Out2.YTrain] = augmentDeepInsight2(Out2.XTrain,Out2.YTrain,Parm.aug_tr);
[Out2.XValidation,Out2.YValidation] = augmentDeepInsight2(Out2.XValidation,Out2.YValidation,Parm.aug_val);
end
end
A = Out2.A;
B = Out2.B;
C = Out2.C;
save('Out2.mat','-struct','Out2','-v7.3');
end