def main(args):
paramsfn = args[0]
exec(open(paramsfn,'r').read())
if len(args) > 1:
crange = [float(args[1])]
gammarange = [float(args[2])]
output,input,fieldnames,fold_inds = load_data(datafilename,use_specific_fold_inds)
fold_start = [-1]
if use_specific_fold_inds:
unique_fold_ids = unique(fold_inds)
row_inds = []
outputcopy = []
inputcopy = zeros([size(input,0),size(input,1)],dtype='float64')
fold_start = [0]
curind = 0
for ind in unique_fold_ids:
row_inds = [i for i in xrange(len(fold_inds)) if fold_inds[i] == ind]
inputcopy[curind:curind+len(row_inds),:] = input[row_inds,:]
outputcopy.extend([output[i] for i in row_inds])
curind += len(row_inds)
fold_start.append(fold_start[-1]+len(row_inds))
input = inputcopy
output = outputcopy
nf = len(fold_start)-1
if testdatafilename != '':
output_test,input_test,fieldnames,fold_inds_test = load_data(testdatafilename,False)
if doscale:
maxinput = input.max(0);
mininput = input.min(0);
input = (input-mininput)/(maxinput-mininput)
if testdatafilename != '':
input_test = (input_test-mininput)/(maxinput-mininput)
if savemodel:
save_scale_data(datafilename+'_scales.dat',maxinput,mininput)
if donormalize:
means = input.mean(0)
stds = sqrt(input.var(0))
input = (input-means)/stds
if testdatafilename != '':
input_test = (input_test-means)/stds
if savemodel:
save_zscore_data(datafilename+'_meansstdevs.dat',means,stds)
if numcpus == 'auto':
p = Pool()
else:
if numcpus == 1:
p = ''
else:
p = Pool(numcpus)
if choose_specific_features:
for specific_selected_choice in specific_selected_features:
inputfiltered = input[:,specific_selected_choice]
with Timer():
if use_specific_fold_inds:
results = mygrid.grid_classify_multi (crange,gammarange,output,[list(x) for x in inputfiltered],nf,useprob,timeout,p,fold_start)
else:
results = mygrid.grid_classify_multi (crange,gammarange,output,[list(x) for x in inputfiltered],nf,useprob,timeout,p)
param = svm.svm_parameter('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
prob = svm.svm_problem(output, [list(x) for x in inputfiltered])
target = (c_double * prob.l)()
fold_start_p = (c_int *len(fold_start))()
for i in xrange(len(fold_start)):
fold_start_p[i] = fold_start[i]
libsvm.svm_cross_validation_labeltargets(prob, fold_start_p,param, nf, target)
labels = unique(output)
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output, target,labels)
probchance = 0
N = len(output)
for i in xrange(len(labels)):
nums_per_class_pred =sum(confusionmatrix[:,i])
probchance += (sum(confusionmatrix[:,i])*sum(confusionmatrix[i,:]))*1.0/(N*N)
kappa = (ACC/100-probchance)*1.0/(1-probchance);
print 'Optimal gamma = %g\nOptimal c = %g' % (results[-1],results[-2])
print 'Top CV ACC = %g' % (ACC)
print 'Top CV kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if savemodel:
param = ('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
m = svm_train(output,[list(x) for x in inputfiltered],param)
svm_save_model(datafilename + '.model',m)
if outputlog != '':
fout = open(outputlog,'a')
print >> fout, '========================='
print >> fout, datafilename
print >> fout, doscale, donormalize, dopca, '(scale/norm/pca)'
print >> fout, crange[0],crange[-1], gammarange[0], gammarange[-1], '(cs,gammas)'
print >> fout, use_specific_fold_inds, nf, '(use specific folds, numfold)'
print >> fout, 'SPECIFIC FIELDS:'
print >> fout, specific_selected_choice
if fieldnames != []:
for i in specific_selected_choice:
print >> fout, fieldnames[i],
print >> fout
print >> fout, 'train: '
print >> fout, ' ACC=%g,kappa=%g (g=%g,c=%g)' % (ACC,kappa,results[-1],results[-2])
fout.write(' =======')
for i in xrange(len(labels)):
fout.write('=========')
print >> fout, '=========='
print >> fout, ' || ||',
for i in xrange(len(labels)):
print >> fout, '%6d |' % labels[i],
print >> fout, ' ||'
fout.write(' ||=====')
for i in xrange(len(labels)):
fout.write('=========')
print >> fout, '========||'
for i in xrange(len(labels)):
print >> fout, ' ||%3d||' % labels[i],
for j in xrange(len(labels)):
print >> fout, '%6g |' % confusionmatrix[i,j],
print >> fout, '%6g ||' % sum(confusionmatrix[i,:])
fout.write(' ||-----')
for i in xrange(len(labels)):
fout.write('---------')
print >> fout, '--------||'
print >> fout, ' || ||',
for i in xrange(len(labels)):
print >> fout, '%6g |' % sum(confusionmatrix[:,i]),
print >> fout, '%6g ||' % N
fout.write(' =======')
for i in xrange(len(labels)):
fout.write('=========')
print >> fout, '=========='
fout.close()
if testdatafilename != '':
inputfiltered_test = input_test[:,specific_selected_choice]
param = ('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
m = svm_train(output,[list(x) for x in inputfiltered],param)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(output_test,[list(x) for x in inputfiltered_test],m,'-b %d' % (int(useprob)))
labels = m.get_labels()
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output_test, pred_labels, labels)
probchance = 0
N = len(output_test)
for i in xrange(len(labels)):
nums_per_class_pred =sum(confusionmatrix[:,i])
probchance += (sum(confusionmatrix[:,i])*sum(confusionmatrix[i,:]))*1.0/(N*N)
kappa = (ACC/100-probchance)*1.0/(1-probchance);
print 'Test optimized accuracy = %g' % (ACC)
print 'Test optimized kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
else:
with Timer():
results = mygrid.grid_classify_multi (crange,gammarange,output,[list(x) for x in input],nf,useprob,timeout,p)
param = svm.svm_parameter('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
prob = svm.svm_problem(output, [list(x) for x in input])
target = (c_double * prob.l)()
labels = unique(output)
print 'Optimal gamma = %g\nOptimal c = %g' % (results[-1],results[-2])
libsvm.svm_cross_validation_labeltargets(prob, param, nf, target)
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output, target,labels)
print 'Top CV ACC = %g' % (ACC)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % len(output)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
del target
if savemodel:
param = ('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
m = svm_train(output,[list(x) for x in input],param)
svm_save_model(datafilename+'.model',m)
if testdatafilename != '':
param = ('-c %g -g %g -b %d' % (results[-2],results[-1],int(useprob)))
m = svm_train(output,[list(x) for x in input],param)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(output_test,[list(x) for x in input_test],m,'-b %d' % (int(useprob)))
labels = m.get_labels()
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output_test, pred_labels, labels)
print 'Test optimized accuracy = %g' % (ACC)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % len(output_test)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if outputlog != '':
fout = open(outputlog,'a')
print >> fout, results#[:-1]
# for key in results[-1].keys():
# print >> fout, key, results[-1][key]
fout.close()
def main(args):
paramsfn = args[0]
exec(open(paramsfn,'r').read())
pdfpages = PdfPages('%s.pdf' % (outputlog))
output_test,input_test,fieldnames = load_data(testdatafilename)
if doscale:
maxinput,mininput = read_scale_data(scaledatafilename)
input_test = (input_test-mininput)/(maxinput-mininput)
if choose_specific_features:
for specific_selected_choice in specific_selected_features:
inputfiltered_test = input_test[:,specific_selected_choice]
if dopca:
coeffs,means = read_pcacoeffs(pcacoefffilename)
temp = (inputfiltered_test-means).T
inputfiltered_test = dot(coeffs.T,temp).T
m = svm_load_model(modelfilename)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(output_test,[list(x) for x in inputfiltered_test],m,'-b %d' % (int(useprob)))
labels = unique(output_test)
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output_test,pred_labels,labels)
probchance = 0
N = len(output_test)
for i in xrange(len(labels)):
nums_per_class_pred =sum(confusionmatrix[:,i])
probchance += (sum(confusionmatrix[:,i])*sum(confusionmatrix[i,:]))*1.0/(N*N)
kappa = (ACC/100-probchance)*1.0/(1-probchance);
print 'Test optimized accuracy = %g' % (ACC)
print 'Test optimized kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if outputpredictions:
fout = open(predictionslog,'w')
for labelind in xrange(len(pred_labels)):
print >> fout, output_test[labelind],pred_labels[labelind],pred_values[labelind]
fout.close()
if outputlog != '':
fout = open(outputlog,'a')
print >> fout, '========================='
print >> fout, 'SPECIFIC FIELDS:'
print >> fout, specific_selected_choice
for i in specific_selected_choice:
print >> fout, fieldnames[i],
print >> fout
print >> fout, 'test: ACC=%g' % (ACC)
fout.close()
else:
m = svm_load_model(modelfilename)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(output_test,[list(x) for x in input_test],m,'-b %d' % (int(useprob)))
if testlabelspresent:
labels = unique(output_test)
ACC,confusionmatrix = mygrid.evaluations_classify_multi(output_test,pred_labels,labels)
probchance = 0
N = len(output_test)
for i in xrange(len(labels)):
nums_per_class_pred =sum(confusionmatrix[:,i])
probchance += (sum(confusionmatrix[:,i])*sum(confusionmatrix[i,:]))*1.0/(N*N)
kappa = (ACC/100-probchance)*1.0/(1-probchance);
print 'Test optimized accuracy = %g' % (ACC)
print 'Test optimized kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i,j],
print '%6g ||' % sum(confusionmatrix[i,:])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:,i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if outputpredictions:
fout = open(predictionslog,'w')
for labelind in xrange(len(pred_labels)):
if testlabelspresent:
print >> fout, output_test[labelind],
print >> fout, pred_labels[labelind],pred_values[labelind]
fout.close()
if outputlog != '':
fout = open(outputlog,'a')
print >> fout, '========================='
print >> fout, 'test: ACC=%g' % (ACC)
fout.close()
pdfpages.close()
def main(args):
paramsfn = args[0]
exec(open(paramsfn, 'r').read())
pdfpages = PdfPages('%s.pdf' % (outputlog))
output_test, input_test, fieldnames = load_data(testdatafilename)
if doscale:
maxinput, mininput = read_scale_data(scaledatafilename)
input_test = (input_test - mininput) / (maxinput - mininput)
if choose_specific_features:
for specific_selected_choice in specific_selected_features:
inputfiltered_test = input_test[:, specific_selected_choice]
if dopca:
coeffs, means = read_pcacoeffs(pcacoefffilename)
temp = (inputfiltered_test - means).T
inputfiltered_test = dot(coeffs.T, temp).T
m = svm_load_model(modelfilename)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(
output_test, [list(x) for x in inputfiltered_test], m,
'-b %d' % (int(useprob)))
labels = unique(output_test)
ACC, confusionmatrix = mygrid.evaluations_classify_multi(
output_test, pred_labels, labels)
probchance = 0
N = len(output_test)
for i in xrange(len(labels)):
nums_per_class_pred = sum(confusionmatrix[:, i])
probchance += (sum(confusionmatrix[:, i]) *
sum(confusionmatrix[i, :])) * 1.0 / (N * N)
kappa = (ACC / 100 - probchance) * 1.0 / (1 - probchance)
print 'Test optimized accuracy = %g' % (ACC)
print 'Test optimized kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i, j],
print '%6g ||' % sum(confusionmatrix[i, :])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:, i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if outputpredictions:
fout = open(predictionslog, 'w')
for labelind in xrange(len(pred_labels)):
print >> fout, output_test[labelind], pred_labels[
labelind], pred_values[labelind]
fout.close()
if outputlog != '':
fout = open(outputlog, 'a')
print >> fout, '========================='
print >> fout, 'SPECIFIC FIELDS:'
print >> fout, specific_selected_choice
for i in specific_selected_choice:
print >> fout, fieldnames[i],
print >> fout
print >> fout, 'test: ACC=%g' % (ACC)
fout.close()
else:
m = svm_load_model(modelfilename)
pred_labels, (ACC, MSE, SCC), pred_values = svm_predict(
output_test, [list(x) for x in input_test], m,
'-b %d' % (int(useprob)))
if testlabelspresent:
labels = unique(output_test)
ACC, confusionmatrix = mygrid.evaluations_classify_multi(
output_test, pred_labels, labels)
probchance = 0
N = len(output_test)
for i in xrange(len(labels)):
nums_per_class_pred = sum(confusionmatrix[:, i])
probchance += (sum(confusionmatrix[:, i]) *
sum(confusionmatrix[i, :])) * 1.0 / (N * N)
kappa = (ACC / 100 - probchance) * 1.0 / (1 - probchance)
print 'Test optimized accuracy = %g' % (ACC)
print 'Test optimized kappa = %g' % (kappa)
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
print '|| ||',
for i in xrange(len(labels)):
print '%6d |' % labels[i],
print ' ||'
sys.stdout.write('||=====')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '========||'
for i in xrange(len(labels)):
print '||%3d||' % labels[i],
for j in xrange(len(labels)):
print '%6g |' % confusionmatrix[i, j],
print '%6g ||' % sum(confusionmatrix[i, :])
sys.stdout.write('||-----')
for i in xrange(len(labels)):
sys.stdout.write('---------')
print '--------||'
print '|| ||',
for i in xrange(len(labels)):
print '%6g |' % sum(confusionmatrix[:, i]),
print '%6g ||' % N
sys.stdout.write('=======')
for i in xrange(len(labels)):
sys.stdout.write('=========')
print '=========='
if outputpredictions:
fout = open(predictionslog, 'w')
for labelind in xrange(len(pred_labels)):
if testlabelspresent:
print >> fout, output_test[labelind],
print >> fout, pred_labels[labelind], pred_values[labelind]
fout.close()
if outputlog != '':
fout = open(outputlog, 'a')
print >> fout, '========================='
print >> fout, 'test: ACC=%g' % (ACC)
fout.close()
pdfpages.close()
