add model_base.m

Model_Base.m

@@ -0,0 +1,140 @@
+% use after selecting weekly events
+% use best features from lasso 
+% parallel run of model fitting
+% make box plot of AUC distribution
+
+% train base model
+
+load trainingData
+load best_features_lasso
+% name of features
+% disp(X_names)
+
+loopSize = 500;
+numFold = 8; 
+
+modelNames = {'Base'};
+% Base = LogReg with only mean
+
+%% Feature selection
+%usePeakflow=true;
+% using best_features according to FitModels_Dec022019B
+% selected_features = double(best_features_lasso(1:optNumFeatures,3))';
+
+% random_features = (best_features(:,3));
+random_features = contains(best_features_lasso(:,2),"random");
+gradAbs_features = contains(best_features_lasso(:,2),"gradAbs");
+grad_features = contains(best_features_lasso(:,2),"grad")&~gradAbs_features;
+peakflow_features = contains(best_features_lasso(:,2),"peakflow")&usePeakflow;
+middle_features = contains(best_features_lasso(:,2),"middle");
+
+selected_features = double(best_features_lasso(middle_features&~random_features,3));
+
+freq_features = contains(X_names,"Freq");
+
+% select points with freq >1
+
+selected_patients = X(:,freq_features)>=minFreq;
+X = X(selected_patients,:);
+Y = Y(selected_patients);
+
+peakflow_patients = any(X(:,1)'==IP_peakflow,1);
+X = X(peakflow_patients,:);
+Y = Y(peakflow_patients);
+
+
+if usePeakflow
+    top_peakflow = best_features_lasso(~random_features&peakflow_features,:);
+    top_peakflow = double(top_peakflow(1,3));
+    if ~any(selected_features == top_peakflow)
+        selected_features = [selected_features, top_peakflow];
+    end
+end
+X = X(:, selected_features);
+
+
+%% standardise features
+X = X-mean(X,1);
+X = X./std(X);
+
+%% Loop
+
+% ROC_loop = cell(loopSize,5); % 5 methods
+ROC_loop_base = cell(loopSize,1);
+
+tic
+for Nloop = 1:loopSize 
+
+    %% Fit models
+
+    indices = crossvalind('Kfold',size(X,1),numFold);
+    parfor kfold = 1:numFold
+%         disp([num2str(kfold),' fold of ',num2str(numFold)])
+        % make train and test set
+%         ind_train = find(indices~=kfold);
+%         ind_test = find(indices==kfold);
+        X_train = X(indices~=kfold,:);
+        X_test = X(indices==kfold,:);
+        Y_train = Y(indices~=kfold,:);
+        Y_test = Y(indices==kfold,:);
+
+        %% train the model
+        GLM_model = fitglm(X_train,Y_train,'Distribution','binomial','Link','logit');
+
+        % score is posterior probabilities
+        [~,score_glm] = predict(GLM_model,X_test);
+
+        % store result
+        Pred(kfold).score_glm = score_glm(:,2);
+        Pred(kfold).Y = Y_test;
+
+    end
+
+    %% unparallel
+    GLM_pred = [];
+    Y_reorder = [];
+    for kfold = 1:numFold
+        GLM_pred = [GLM_pred; Pred(kfold).score_glm];
+        Y_reorder = [Y_reorder; Pred(kfold).Y];
+    end
+
+    GLM_pred = Alg_standardise(GLM_pred);
+
+    [X_glm,Y_glm,T_glm,AUC_glm] = perfcurve(Y_reorder,GLM_pred,1);
+
+    % store AUC and ROC
+    ROC_loop_glm{Nloop} = {X_glm,Y_glm,T_glm,AUC_glm};
+
+    if mod(Nloop,10)==0
+        disp(['loop number ',num2str(Nloop),' of ',num2str(loopSize)])
+        toc
+    end
+end
+clc;
+disp([num2str(loopSize),' loops completed'])
+toc
+
+%% save
+ROC_loop_base = ROC_loop_glm;
+AUC_loop=zeros(size(ROC_loop_base));
+GMA_loop=zeros(size(ROC_loop_base));
+
+for loopIndex = 1:length(ROC_loop_base)
+    if isempty(ROC_loop_base{loopIndex})
+        AUC_loop(loopIndex) = 0;
+        disp('empty')
+    else
+        AUC_loop(loopIndex) = ROC_loop_base{loopIndex}{4};
+        GMA_loop(loopIndex) = max(Alg_GMA(ROC_loop_base{loopIndex}{1},ROC_loop_base{loopIndex}{2}));
+    end
+end
+
+AUC_loop_base = AUC_loop;
+GMA_loop_base = GMA_loop;
+
+loopSize_base = loopSize;
+modelNames_base = modelNames;
+
+save AUC_GMA_loop_base AUC_loop_base GMA_loop_base loopSize_base modelNames_base ROC_loop_base
+
+

Model_FitModels.m

@@ -25,7 +25,7 @@
 
 usePeakflow = TF(usePF);
 disp(['usePeakflow=', num2str(usePeakflow)])
-% using best_features according to FitModels_Dec022019B
+% using best_features according to Model_LASSO
 selected_features = double(best_features_lasso(1:optNumFeatures,3))';
 
 % random_features = (best_features(:,3));

README.md

@@ -43,7 +43,8 @@ Please run the files in the following order:
 4. `Create_QandA_Lookup.m` defines a dictionary for answers to daily questionnaire
 5. `Filter_WeeklyEvents.m` to create the training data
 6. `Model_LASSO.m` to rank and select the features
-7. `Model_FitModels.m` to benchmark algorithms
+7. `Model_Base.m` to fit base model
+8. `Model_FitModels.m` to benchmark algorithms
 
 ## Citation