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Diffstat (limited to 'sourcecodes/parameter_learning/testSetPredictions.m')
| -rw-r--r-- | sourcecodes/parameter_learning/testSetPredictions.m | 361 |
1 files changed, 361 insertions, 0 deletions
diff --git a/sourcecodes/parameter_learning/testSetPredictions.m b/sourcecodes/parameter_learning/testSetPredictions.m new file mode 100644 index 00000000..0fff197d --- /dev/null +++ b/sourcecodes/parameter_learning/testSetPredictions.m @@ -0,0 +1,361 @@ +function [ ] = testSetPredictions( pre ) + % + % This function will make predictions for the cases included + % in the uploaded data file. + % + % + % Input: ???ts_input.txt + % This is the input file that is uploaded to BNW. + % It is directly written out by the BNW php code with no modification. + % The file format is a header line containing the variable that you want to predict. + % Then, there is a second header line with the variable names + % Finally, the file contains the data, with each case in a row. + % If there is missing data, an "NA" should be entered. + % + % Output: ???ts_output.txt + % + % It is called by the run_test_set script in the 'sourcecodes' directory. + + +% open file for input, include error handling +dfile=strcat(pre,'ts_upload.txt'); + +fin = fopen(dfile,'r'); +if fin < 0 + error(['Could not open ',dfile,' for input']); +end + +% Get the number of cases (the number of rows in the file excluding the header) +ntestcases = fskipl(fin,Inf) - 2; + +frewind(fin); + + + +% Read in first line to get the node label of the variable that should be predicted. +buffer = fgetl(fin); +[predict_label,buffer] = strtok(buffer); + +% Read in second line to get the number of nodes and the node labels. +buffer = fgetl(fin); %get header line as a string +nnodes = numel(strfind(buffer,"\t")) + 1; +labels_test = cell(1,nnodes); +for j=1:nnodes + [next,buffer] = strtok(buffer); + labels_test{j} = next; +end + +% Read in the test_data +data_test_temp = cell(ntestcases,nnodes); +for i = 1:ntestcases + buffer = fgetl(fin); + for j = 1:nnodes + [next,buffer] = strtok(buffer); + data_test_temp{i,j} = next; + end +end + + +% Read in the training (original) data and network structure. +sfile=strcat(pre,'structure_input.txt'); +dfile=strcat(pre,'continuous_input.txt'); +nnodefile=strcat(pre,'nnode.txt'); +fnnode = fopen(nnodefile,'r'); +nnodes = fscanf(fnnode,'%d'); + +Std_flag=true; +[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag); +[bnet] = parameterLearning(bnet,cases); + +%Get node id in actual network for variable to be predicted +for i=1:nnodes + if strcmp(labels(i),predict_label) + predict_node = i; + end +end + +%Reformat test data so the columns match bnet structure +label_map = cell(2,nnodes); +for i=1:nnodes + label_map{1,i} = labels_test{i}; + for j=1:nnodes + if strcmp(labels_test(i),labels(j)) + label_map{2,i} = j; + break + end + end +end + + +data_test = cell(ntestcases,nnodes); +for i=1:nnodes + data_test(:,label_map{2,i}) = data_test_temp(:,i); +end + +%%Read in training data means and standard deviations +means_orig=cell(1,nnodes); +stdevs_orig=cell(1,nnodes); +labels_orig=cell(1,nnodes); +%Read in original means and standard deviations +mapfile = strcat(pre,'map.txt'); +fmap = fopen(mapfile,'r'); +for i=1:nnodes + buffer = fgetl(mapfile); + temp = cell(1,3); + for j=1:3 + [next,buffer] = strtok(buffer); + temp{j} = next; + end + labels_orig{i} = temp{1}; + means_orig{i} = str2num(temp{3}); + stdevs_orig{i} = str2num(temp{2}); +end +fclose(fmap); +%Need to map the means and stdevs to the correct labels +means = cell(1,nnodes); +stdevs = cell(1,nnodes); +for i = 1:nnodes + for j = 1:nnodes + if strcmp(labels{i},labels_orig{j}) + means{i} = means_orig{j}; + stdevs{i} = stdevs_orig{j}; + break + end + end +end + +%Get mapping of discrete levels. +max_states = max(bnet.node_sizes) + 1; +disc_nodes = size(bnet.dnodes,2); +levelfile = strcat(pre,'nlevels.txt'); +flevels = fopen(levelfile,'r'); +levels = cell(disc_nodes,max_states); +ndisc_nodes = 0; +for i=1:disc_nodes + ndisc_nodes = ndisc_nodes + 1; + buffer = fgetl(flevels); + for j = 1:max_states + [next,buffer] = strtok(buffer); + if j == 1 + levels{i,j} = next; + else + levels{i,j} = next; + end + if length(buffer) < 1 + break + end + end +end + +%Standardize continuous data and map data to levels. +for i=1:nnodes + if bnet.node_sizes(i) == 1 + for j=1:ntestcases + if !strcmp(data_test{j,i},"NA") + data_test{j,i} = (str2num(data_test{j,i}) - means{i})/stdevs{i}; + end + end + else + for j=1:ntestcases + labels{i} + if !strcmp(data_test{j,i},"NA") + for jj = 1:size(levels,1) + if strcmp(labels{i},levels{jj,1}) + break + endif + end + for k = 1:bnet.node_sizes(i) + if strcmp(data_test{j,i},levels{jj,k+1}) + data_test{j,i} = k; + end + end + end + end +end +end + + +%Now make predictions +predict_cases = bnet.node_sizes(predict_node); + +if predict_cases == 1 + ts_continuous(pre,bnet,nnodes,predict_label,predict_node,data_test,means,stdevs) +else + pred_levels = cell(1,predict_cases); + for i = 1:disc_nodes + if strcmp(levels{i,1},predict_label); + for j = 1:predict_cases + pred_levels{j} = levels{i,j+1}; + end + break + end + end + ts_discrete(pre,bnet,nnodes,predict_label,predict_node,data_test,pred_levels,predict_cases) +end + +%delete(dfile) + +end + +function ts_continuous(pre,bnet,nnodes,predict_label,predict_node,data_test,means,stdevs) + +ntestcases = size(data_test,1); + +predictions = zeros(ntestcases,2); + +for i = 1:ntestcases + evidence = data_test(i,:); + evidence{predict_node} = {}; + for j=1:nnodes + if strcmp(evidence{j},"NA") + evidence{j} = {}; + end + end + engine = jtree_inf_engine(bnet); + [engine,loglik] = enter_evidence(engine,evidence); + predict = marginal_nodes(engine,predict_node); + adj_mu = predict.mu*stdevs{predict_node}+means{predict_node}; + adj_sigma = stdevs{predict_node}*predict.Sigma; + predictions(i,1) = adj_mu; + predictions(i,2) = adj_sigma; +end + +%Open output file. +filename = strcat(pre,'ts_output.txt'); +fileID = fopen(filename,'w'); + +fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label); + + +%Calculate RMSEP (root mean square error of prediction) and q^2 +%First, calculate TSS (total sum of squares) and +% PRESS (sum of squares of prediction errors) +%Get rid of 'NA' data for predicted data. +actual_values = []; +predictions_removeNA = []; +for i=1:ntestcases + if !strcmp(data_test(i,predict_node),'NA') + actual_values = [actual_values, cell2num(data_test(i,predict_node))] + predictions_removeNA = [predictions_removeNA,predictions(i,1)] + end +end + +size(actual_values) +size(predictions_removeNA) + +average = mean(actual_values); +average = average*stdevs{predict_node}+means{predict_node}; +tss = 0; +press = 0; +for i=1:length(actual_values) + actual_values(i) = actual_values(i)*stdevs{predict_node}+means{predict_node}; + tss = (actual_values(i)-average)^2 + tss; + press = (predictions_removeNA(i)-actual_values(i))^2 + press; +end +rmsep = sqrt(press/length(actual_values)); +q_squared = 1 - press/tss; + +%% Print rmseq and q^2 +fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep); +fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared); + + +%%Print the predictions +fprintf(fileID,'Predicted mean and standard deviation for each case:\n'); +fprintf(fileID,'CaseRow\tActualValue\tPredictionMean\tPredictionStDev\n'); +for i = 1:ntestcases + if strcmp(data_test{i,predict_node},"NA") + fprintf(fileID,'%i\t%s\t',i,data_test{i,predict_node}); + else + temp = data_test{i,predict_node}*stdevs{predict_node}+means{predict_node}; + fprintf(fileID,'%i\t%6.4f\t',i,temp); + end + fprintf(fileID,'%6.4f\t%6.4f\n',predictions(i,:)); +end + + +end + +function ts_discrete(pre,bnet,nnodes,predict_label,predict_node,data_test,pred_levels,predict_cases) + +ntestcases = size(data_test,1); + +predictions=zeros(ntestcases,predict_cases); + +for i = 1:ntestcases + evidence = data_test(i,:); + evidence{predict_node} = {}; + for j=1:nnodes + if strcmp(evidence{j},"NA") + evidence{j} = {}; + end + end + engine = jtree_inf_engine(bnet); + [engine,loglik] = enter_evidence(engine,evidence); + predict = marginal_nodes(engine,predict_node); + for j = 1:predict_cases + predictions(i,j) = predict.T(j); + end +end + + +pred_states = []; +for i=1:ntestcases + if !strcmp(data_test(i,predict_node),'NA') + max_state = 1; + for j = 2:predict_cases + if predictions(i,j) > predictions(i,max_state) + max_state = j; + end + end + pred_states = [pred_states,max_state]; + end +end + + +correct = 0; +j = 0; +for i=1:ntestcases + if !strcmp(data_test(i,predict_node),'NA') + j = j + 1; + if pred_states(j) == cell2mat(data_test(i,predict_node)) + correct = correct + 1; + end + end +end + +accuracy = correct/length(pred_states); + + +%Open output file. +filename = strcat(pre,'ts_output.txt'); +fileID = fopen(filename,'w'); + +fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label); + +%%Print the accuracy +fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy); + +%%Print the predictions +fprintf(fileID,'Predicted likelihood of each state for each case:\n'); +fprintf(fileID,'%s\t%s\t','CaseRow','ActualState'); +fprintf(fileID,'%s\t',pred_levels{1:end-1}); +fprintf(fileID,'%s\n',pred_levels{end}); +for i = 1:ntestcases + fprintf(fileID,'%i\t',i); + if strcmp(data_test{i,predict_node},"NA") + fprintf(fileID,'%s\t',data_test{i,predict_node}); + else + case_level = pred_levels{data_test{i,predict_node}}; + fprintf(fileID,'%s\t',case_level); + end + fprintf(fileID,'%6.4f\t',predictions(i,1:end-1)); + fprintf(fileID,'%6.4f\n',predictions(i,end)); +end + + + + + +end + |