def test_mvm_main(workmode):
params=mmr_setparams.cls_params()
xdatacls=mvm_mvm_cls.cls_mvm()
nfold=xdatacls.nfold
if xdatacls.itestmode==0:
nfold0=1 ## active learning
else:
nfold0=nfold ## n-fold cross validation
nparacc=2 ## rmse, time
npar=1
xsummary=np.zeros((npar,nparacc))
ifile=0
pselect=0.05
itrates=1
print('ifile:',ifile)
print('itrates:',itrates)
print('pselect:',pselect)
lfiles=[]
for ipar in range(npar):
rmatrix=mvm_random_matrix.cls_label_files()
(xdata,nrow2,ncol2)=rmatrix.load(ifile,pselect,itrain=itrates)
xdatacls.load_data(xdata,xdatacls.categorymax, \
int(nrow2),int(ncol2),None)
scombine=''
if xdatacls.itestmode==0:
if xdatacls.ibootstrap==0:
fname='xresultte_rand'+scombine+'.csv'
elif xdatacls.ibootstrap==1:
fname='xresultte_active'+scombine+'.csv'
elif xdatacls.ibootstrap==2:
fname='xresultte_greedy'+scombine+'.csv'
elif xdatacls.ibootstrap==3:
fname='xresultte_act_rand'+scombine+'.csv'
else:
fname='xresultte_ncross'+scombine+'.csv'
xdatacls.YKernel.ymax=1
# it will be recomputed in mvm_ranges
xdatacls.YKernel.ymin=-1
xdatacls.YKernel.yrange=200 # it will be recomputed in classcol_ranges
xdatacls.YKernel.ystep=(xdatacls.YKernel.ymax-xdatacls.YKernel.ymin) \
/xdatacls.YKernel.yrange
## set_printoptions(precision=4)
nparam=4 # C,D,par1,par2
nreport=4 ## accuracy, precision, recall, f1
xdatacls.prepare_repetition_folding(init_train_size=100)
nrepeat0=xdatacls.nrepeat0
nfold0=xdatacls.nfold0
creport=mmr_report_cls.cls_mmr_report()
creport.create_xaprf(nrepeat=nrepeat0,nfold=nfold0,nreport=nreport)
xbest_param=np.zeros((nrepeat0,nfold0,nparam))
# ############################################################
nval=max(xdatacls.YKernel.valrange)+1
xconfusion3=np.zeros((nrepeat0,nfold0,xdatacls.YKernel.ndim,nval,nval))
xsolvertime=0.0
ireport=0
for irepeat in range(nrepeat0):
xdatacls.prepare_repetition_training()
for ifold in range(nfold0):
xdatacls.prepare_fold_training(ifold)
# validation to choose the best parameters
print('Validation')
xdatacls.set_validation()
cvalidation=mvm_validation_cls.cls_mvm_validation()
cvalidation.validation_rkernel=xdatacls.XKernel[0].title
best_param=cvalidation.mvm_validation(xdatacls)
print('Parameters:',best_param.c,best_param.d, \
best_param.par1,best_param.par2)
print('Best parameters found by validation')
xbest_param[irepeat,ifold,0]=best_param.c
xbest_param[irepeat,ifold,1]=best_param.d
xbest_param[irepeat,ifold,2]=best_param.par1
xbest_param[irepeat,ifold,3]=best_param.par2
# training with the best parameters
print('training')
time0=time.time()
cOptDual= xdatacls.mvm_train()
xsolvertime+=xdatacls.solvertime
print('Training time:',time.time()-time0)
sys.stdout.flush()
# check the train accuracy
print('test on training')
# check the test accuracy
print('test on test')
time0=time.time()
cPredict=xdatacls.mvm_test()
print('Test time:',time.time()-time0)
sys.stdout.flush()
# counts the proportion the ones predicted correctly
# ####################################
time0=time.time()
(cEval,icandidate_w,icandidate_b)=mvm_eval(xdatacls.ieval_type, \
xdatacls.nrow,xdatacls,cPredict.Zrow)
print('Evaluation time:',time.time()-time0)
(qtest,qpred)=makearray(xdatacls,cPredict.Zrow)
if xdatacls.ieval_type==0:
creport.set_xaprf(irepeat,ifold,cEval)
elif xdatacls.ieval_type==10:
creport.set_xaprf(irepeat,ifold,cEval)
xconfusion3[irepeat,ifold]=cEval.xconfusion3
else:
creport.set_xaprf(irepeat,ifold,cEval)
xdatacls.icandidate_w=xdatacls.itest[icandidate_w]
xdatacls.icandidate_b=xdatacls.itest[icandidate_b]
ireport+=1
## print(cEval.xconfusion)
if xdatacls.ieval_type==0:
for xconfrow in cEval.xconfusion:
for ditem in xconfrow:
print('%7.0f'%ditem,end='')
print()
print()
elif xdatacls.ieval_type==10:
for xtable in cEval.xconfusion3:
xsum=np.sum(xtable)
if xsum==0:
xsum=1
xtable=100*xtable/xsum
for xconfrow in xtable:
for ditem in xconfrow:
print('%9.4f'%ditem,end='')
print()
print()
print()
# ####################################
print('*** ipar, repeatation, fold ***')
print(ipar,irepeat,ifold)
if xdatacls.itestmode==1: ## n-fold crossvalidation
creport.report_prf(xmask=[irepeat,ifold], \
stitle='Result in one fold and one repetation', \
ssubtitle='Accuracy on test')
creport.report_prf(xmask=[irepeat,None], \
stitle='Result in one repetation', \
ssubtitle='Mean and std of the accuracy on test')
sys.stdout.flush()
if xdatacls.itestmode==0: ## n-fold crossvalidation
np.savetxt(fname,creport.xresulttes[:ireport,0,:],delimiter=',', \
fmt='%6.4f')
else:
if xdatacls.ieval_type==0:
np.savetxt(fname,np.squeeze(creport.xaprf),delimiter=',', \
fmt='%6.4f')
else:
np.savetxt(fname,creport.xaprf[:,:,0],delimiter=',',fmt='%6.4f')
(xmean,xstd)=creport.report_prf(xmask=[None,None], \
stitle='***** Overall result ****', \
ssubtitle='Mean and std of the accuracy on test + error')
xsummary[ipar,0]=xmean[0]
xsummary[ipar,1]=xsolvertime/(nrepeat0*nfold0)
if xdatacls.ieval_type==10:
confusion_latex(xconfusion3,lfiles)
print('Average best parameters')
xlabels=('c','d','par1','par2')
for i in range(nparam):
print(xlabels[i],': ',np.mean(xbest_param[:,:,i]), \
'(',np.std(xbest_param[:,:,i]),')')
print('$$$$$$$$$ Summary results:')
(m,n)=xsummary.shape
for i in range(m):
for j in range(n):
print('%10.4f'%xsummary[i,j],end='')
print()
## np.savetxt(fname,xresultte[:ireport,0,:],delimiter=',',fmt='%6.4f')
print('Bye')
return
def test_mvm_main(workmode):
params = mmr_setparams.cls_params()
xdatacls = mvm_mvm_cls.cls_mvm()
nfold = xdatacls.nfold
if xdatacls.itestmode == 0:
nfold0 = 1 ## active learning
else:
nfold0 = nfold ## n-fold cross validation
nparacc = 2 ## rmse, time
npar = 1
xsummary = np.zeros((npar, nparacc))
lfilenames = ["affordances_instrument_for", "affordances_patient"]
ifile = 1 ## file index in list above
lfiles = [0, 1]
lfeatures = ["PointMutualInformation", "absolute frequency"]
ifeature = 0
if xdatacls.itestmode == 3:
iloadall = 1
else:
iloadall = 0
print("lfiles:", lfilenames)
print("ifeature:", lfeatures[ifeature])
for ipar in range(npar):
## possible values
Y0 = np.array([-1, 0, 1])
ctables = webrel_load_data.cls_label_files()
print(ctables.listcsv[ifile])
(xdata, nrow2, ncol2, ifixtrain, ifixtest) = ctables.load_objobj_act(lfiles, ifeature)
xdatacls.load_data(xdata, xdatacls.categorymax, int(nrow2), int(ncol2), Y0)
xdatacls.ifixtrain = ifixtrain
xdatacls.ifixtest = ifixtest
scombine = ""
if xdatacls.itestmode == 0:
if xdatacls.ibootstrap == 0:
fname = "xresultte_rand" + scombine + ".csv"
elif xdatacls.ibootstrap == 1:
fname = "xresultte_active" + scombine + ".csv"
elif xdatacls.ibootstrap == 2:
fname = "xresultte_greedy" + scombine + ".csv"
elif xdatacls.ibootstrap == 3:
fname = "xresultte_act_rand" + scombine + ".csv"
else:
fname = "xresultte_ncross" + scombine + ".csv"
xdatacls.YKernel.ymax = 10
# it will be recomputed in mvm_ranges
xdatacls.YKernel.ymin = -10
xdatacls.YKernel.yrange = 200 # it will be recomputed in classcol_ranges
xdatacls.YKernel.ystep = (xdatacls.YKernel.ymax - xdatacls.YKernel.ymin) / xdatacls.YKernel.yrange
## set_printoptions(precision=4)
nparam = 4 # C,D,par1,par2
nreport = 4 ## accuracy, precision, recall, f1
xdatacls.prepare_repetition_folding(init_train_size=100)
nrepeat0 = xdatacls.nrepeat0
nfold0 = xdatacls.nfold0
if xdatacls.itestmode == 3:
nfold0 = 1
creport = mmr_report_cls.cls_mmr_report()
creport.create_xaprf(nrepeat=nrepeat0, nfold=nfold0, nreport=nreport)
xbest_param = np.zeros((nrepeat0, nfold0, nparam))
# ############################################################
nval = max(xdatacls.YKernel.valrange) + 1
xconfusion3 = np.zeros((nrepeat0, nfold0, xdatacls.YKernel.ndim, nval, nval))
xsolvertime = 0.0
ireport = 0
for irepeat in range(nrepeat0):
xdatacls.nfold0 = xdatacls.nfold
xdatacls.prepare_repetition_training()
for ifold in range(nfold0):
xdatacls.prepare_fold_training(ifold)
# validation to choose the best parameters
print("Validation")
xdatacls.set_validation()
cvalidation = mvm_validation_cls.cls_mvm_validation()
cvalidation.validation_rkernel = xdatacls.XKernel[0].title
best_param = cvalidation.mvm_validation(xdatacls)
print("Parameters:", best_param.c, best_param.d, best_param.par1, best_param.par2)
print("Best parameters found by validation")
xbest_param[irepeat, ifold, 0] = best_param.c
xbest_param[irepeat, ifold, 1] = best_param.d
xbest_param[irepeat, ifold, 2] = best_param.par1
xbest_param[irepeat, ifold, 3] = best_param.par2
# training with the best parameters
print("training")
time0 = time.time()
cOptDual = xdatacls.mvm_train()
xsolvertime += xdatacls.solvertime
print("Training time:", time.time() - time0)
sys.stdout.flush()
# check the train accuracy
print("test on training")
# check the test accuracy
print("test on test")
time0 = time.time()
if xdatacls.ifulltest == 1:
xdatacls.xdata_tes = ctables.full_test()
xdatacls.xranges_rel_test = mvm_prepare.mvm_ranges(xdatacls.xdata_tes, xdatacls.nrow)
cPredict = xdatacls.mvm_test()
print("Test time:", time.time() - time0)
sys.stdout.flush()
filename = "predicted_missing.csv"
ctables.export_prediction(filename, xdatacls, cPredict.Zrow)
# counts the proportion the ones predicted correctly
# ####################################
time0 = time.time()
if xdatacls.knowntest == 1:
(cEval, icandidate_w, icandidate_b) = mvm_eval(
xdatacls.ieval_type, xdatacls.nrow, xdatacls, cPredict.Zrow
)
print("Evaluation time:", time.time() - time0)
(qtest, qpred, qpred0) = makearray(xdatacls, cPredict.Zrow)
if xdatacls.ieval_type in (0, 11):
creport.set_xaprf(irepeat, ifold, cEval)
elif xdatacls.ieval_type == 10:
creport.set_xaprf(irepeat, ifold, cEval)
xconfusion3[irepeat, ifold] = cEval.xconfusion3
else:
creport.set_xaprf(irepeat, ifold, cEval)
## xdatacls.icandidate_w=xdatacls.itest[icandidate_w]
## xdatacls.icandidate_b=xdatacls.itest[icandidate_b]
ireport += 1
## print(cEval.xconfusion)
if xdatacls.ieval_type in (0, 11):
for xconfrow in cEval.xconfusion:
for ditem in xconfrow:
print("%7.0f" % ditem, end="")
print()
print()
elif xdatacls.ieval_type == 10:
for xtable in cEval.xconfusion3:
xsum = np.sum(xtable)
if xsum == 0:
xsum = 1
xtable = 100 * xtable / xsum
for xconfrow in xtable:
for ditem in xconfrow:
print("%9.4f" % ditem, end="")
print()
print()
print()
# ####################################
print("*** ipar, repeatation, fold ***")
print(ipar, irepeat, ifold)
if xdatacls.itestmode == 1: ## n-fold crossvalidation
creport.report_prf(
xmask=[irepeat, ifold],
stitle="Result in one fold and one repetation",
ssubtitle="Accuracy on test",
)
if xdatacls.knowntest == 1:
creport.report_prf(
xmask=[irepeat, None],
stitle="Result in one repetation",
ssubtitle="Mean and std of the accuracy on test",
)
sys.stdout.flush()
if xdatacls.knowntest == 1:
(xmean, xstd) = creport.report_prf(
xmask=[None, None],
stitle="***** Overall result ****",
ssubtitle="Mean and std of the accuracy on test + error",
)
xsummary[ipar, 0] = xmean[0]
xsummary[ipar, 1] = xsolvertime / (nrepeat0 * nfold0)
if xdatacls.itestmode == 3:
filename = "predicted_missing.csv"
## ctables.export_prediction(filename,xdatacls,cPredict.Zrow)
## (qtest,qpred,qpred0)=makearray(xdatacls,cPredict.Zrow)
print("Average best parameters")
xlabels = ("c", "d", "par1", "par2")
for i in range(nparam):
print(xlabels[i], ": ", np.mean(xbest_param[:, :, i]), "(", np.std(xbest_param[:, :, i]), ")")
if xdatacls.knowntest == 1:
print("$$$$$$$$$ Summary results:")
(m, n) = xsummary.shape
for i in range(m):
for j in range(n):
print("%10.4f" % xsummary[i, j], end="")
print()
## np.savetxt(fname,xresultte[:ireport,0,:],delimiter=',',fmt='%6.4f')
print("Bye")
return
def test_mvm_main(workmode):
params = mmr_setparams.cls_params()
xdatacls = mvm_mvm_cls.cls_mvm()
nfold = xdatacls.nfold
if xdatacls.itestmode == 0:
nfold0 = 1 ## active learning
else:
nfold0 = nfold ## n-fold cross validation
nparacc = 2 ## rmse, time
npar = 1
xsummary = np.zeros((npar, nparacc))
ifile = 0
pselect = 0.05
itrates = 1
print('ifile:', ifile)
print('itrates:', itrates)
print('pselect:', pselect)
lfiles = []
for ipar in range(npar):
rmatrix = mvm_random_matrix.cls_label_files()
(xdata, nrow2, ncol2) = rmatrix.load(ifile, pselect, itrain=itrates)
xdatacls.load_data(xdata,xdatacls.categorymax, \
int(nrow2),int(ncol2),None)
scombine = ''
if xdatacls.itestmode == 0:
if xdatacls.ibootstrap == 0:
fname = 'xresultte_rand' + scombine + '.csv'
elif xdatacls.ibootstrap == 1:
fname = 'xresultte_active' + scombine + '.csv'
elif xdatacls.ibootstrap == 2:
fname = 'xresultte_greedy' + scombine + '.csv'
elif xdatacls.ibootstrap == 3:
fname = 'xresultte_act_rand' + scombine + '.csv'
else:
fname = 'xresultte_ncross' + scombine + '.csv'
xdatacls.YKernel.ymax = 1
# it will be recomputed in mvm_ranges
xdatacls.YKernel.ymin = -1
xdatacls.YKernel.yrange = 200 # it will be recomputed in classcol_ranges
xdatacls.YKernel.ystep=(xdatacls.YKernel.ymax-xdatacls.YKernel.ymin) \
/xdatacls.YKernel.yrange
## set_printoptions(precision=4)
nparam = 4 # C,D,par1,par2
nreport = 4 ## accuracy, precision, recall, f1
xdatacls.prepare_repetition_folding(init_train_size=100)
nrepeat0 = xdatacls.nrepeat0
nfold0 = xdatacls.nfold0
creport = mmr_report_cls.cls_mmr_report()
creport.create_xaprf(nrepeat=nrepeat0, nfold=nfold0, nreport=nreport)
xbest_param = np.zeros((nrepeat0, nfold0, nparam))
# ############################################################
nval = max(xdatacls.YKernel.valrange) + 1
xconfusion3 = np.zeros(
(nrepeat0, nfold0, xdatacls.YKernel.ndim, nval, nval))
xsolvertime = 0.0
ireport = 0
for irepeat in range(nrepeat0):
xdatacls.prepare_repetition_training()
for ifold in range(nfold0):
xdatacls.prepare_fold_training(ifold)
# validation to choose the best parameters
print('Validation')
xdatacls.set_validation()
cvalidation = mvm_validation_cls.cls_mvm_validation()
cvalidation.validation_rkernel = xdatacls.XKernel[0].title
best_param = cvalidation.mvm_validation(xdatacls)
print('Parameters:',best_param.c,best_param.d, \
best_param.par1,best_param.par2)
print('Best parameters found by validation')
xbest_param[irepeat, ifold, 0] = best_param.c
xbest_param[irepeat, ifold, 1] = best_param.d
xbest_param[irepeat, ifold, 2] = best_param.par1
xbest_param[irepeat, ifold, 3] = best_param.par2
# training with the best parameters
print('training')
time0 = time.time()
cOptDual = xdatacls.mvm_train()
xsolvertime += xdatacls.solvertime
print('Training time:', time.time() - time0)
sys.stdout.flush()
# check the train accuracy
print('test on training')
# check the test accuracy
print('test on test')
time0 = time.time()
cPredict = xdatacls.mvm_test()
print('Test time:', time.time() - time0)
sys.stdout.flush()
# counts the proportion the ones predicted correctly
# ####################################
time0 = time.time()
(cEval,icandidate_w,icandidate_b)=mvm_eval(xdatacls.ieval_type, \
xdatacls.nrow,xdatacls,cPredict.Zrow)
print('Evaluation time:', time.time() - time0)
(qtest, qpred) = makearray(xdatacls, cPredict.Zrow)
if xdatacls.ieval_type == 0:
creport.set_xaprf(irepeat, ifold, cEval)
elif xdatacls.ieval_type == 10:
creport.set_xaprf(irepeat, ifold, cEval)
xconfusion3[irepeat, ifold] = cEval.xconfusion3
else:
creport.set_xaprf(irepeat, ifold, cEval)
xdatacls.icandidate_w = xdatacls.itest[icandidate_w]
xdatacls.icandidate_b = xdatacls.itest[icandidate_b]
ireport += 1
## print(cEval.xconfusion)
if xdatacls.ieval_type == 0:
for xconfrow in cEval.xconfusion:
for ditem in xconfrow:
print('%7.0f' % ditem, end='')
print()
print()
elif xdatacls.ieval_type == 10:
for xtable in cEval.xconfusion3:
xsum = np.sum(xtable)
if xsum == 0:
xsum = 1
xtable = 100 * xtable / xsum
for xconfrow in xtable:
for ditem in xconfrow:
print('%9.4f' % ditem, end='')
print()
print()
print()
# ####################################
print('*** ipar, repeatation, fold ***')
print(ipar, irepeat, ifold)
if xdatacls.itestmode == 1: ## n-fold crossvalidation
creport.report_prf(xmask=[irepeat,ifold], \
stitle='Result in one fold and one repetation', \
ssubtitle='Accuracy on test')
creport.report_prf(xmask=[irepeat,None], \
stitle='Result in one repetation', \
ssubtitle='Mean and std of the accuracy on test')
sys.stdout.flush()
if xdatacls.itestmode == 0: ## n-fold crossvalidation
np.savetxt(fname,creport.xresulttes[:ireport,0,:],delimiter=',', \
fmt='%6.4f')
else:
if xdatacls.ieval_type == 0:
np.savetxt(fname,np.squeeze(creport.xaprf),delimiter=',', \
fmt='%6.4f')
else:
np.savetxt(fname,
creport.xaprf[:, :, 0],
delimiter=',',
fmt='%6.4f')
(xmean,xstd)=creport.report_prf(xmask=[None,None], \
stitle='***** Overall result ****', \
ssubtitle='Mean and std of the accuracy on test + error')
xsummary[ipar, 0] = xmean[0]
xsummary[ipar, 1] = xsolvertime / (nrepeat0 * nfold0)
if xdatacls.ieval_type == 10:
confusion_latex(xconfusion3, lfiles)
print('Average best parameters')
xlabels = ('c', 'd', 'par1', 'par2')
for i in range(nparam):
print(xlabels[i],': ',np.mean(xbest_param[:,:,i]), \
'(',np.std(xbest_param[:,:,i]),')')
print('$$$$$$$$$ Summary results:')
(m, n) = xsummary.shape
for i in range(m):
for j in range(n):
print('%10.4f' % xsummary[i, j], end='')
print()
## np.savetxt(fname,xresultte[:ireport,0,:],delimiter=',',fmt='%6.4f')
print('Bye')
return
def test_mvm_main(workmode):
params=mmr_setparams.cls_params()
xdatacls=mvm_mvm_cls.cls_mvm()
nfold=xdatacls.nfold
if xdatacls.itestmode==0:
nfold0=1 ## active learning
else:
nfold0=nfold ## n-fold cross validation
nparacc=2 ## rmse, time
npar=1
xsummary=np.zeros((npar,nparacc))
## ['full','full_20','full_40','full_60', \
## 'known','known_20','known_40','known_60']
ifile1=0 ## file index in list known
ifile2=0 ## file index in list full
iknown1=1 ## known
iknown2=0 ## full
iloadall=1 ## =0 one file for crossvalidation =1 two files: training + test
print('iknown1:',iknown1,'iknown2:',iknown2)
print('ifile1:',ifile1,'ifile2:',ifile2)
for ipar in range(npar):
## possible values
Y0=np.array([0,1])
ctables=kingsc_load_data.cls_label_files() ## data loading object
print(ctables.listknown[ifile1])
print(ctables.listfull[ifile2])
if iloadall==0: ## only one file is loaded for cross validation
(xdata,nrow2,ncol2)=ctables.load_onefile(iknown1,ifile1)
xdatacls.load_data(xdata,xdatacls.categorymax, \
int(nrow2),int(ncol2),Y0)
else: ## the first file gives trining the second serves as test
(xdata,nrow2,ncol2,ifixtrain,ifixtest)=ctables.load_twofiles( \
iknown1,iknown2,ifile1,ifile2)
xdatacls.load_data(xdata,xdatacls.categorymax, \
int(nrow2),int(ncol2),Y0)
xdatacls.ifixtrain=ifixtrain
xdatacls.ifixtest=ifixtest
scombine=''
if xdatacls.itestmode==0:
if xdatacls.ibootstrap==0:
fname='xresultte_rand'+scombine+'.csv'
elif xdatacls.ibootstrap==1:
fname='xresultte_active'+scombine+'.csv'
elif xdatacls.ibootstrap==2:
fname='xresultte_greedy'+scombine+'.csv'
elif xdatacls.ibootstrap==3:
fname='xresultte_act_rand'+scombine+'.csv'
else:
fname='xresultte_ncross'+scombine+'.csv'
xdatacls.YKernel.ymax=1
# it will be recomputed in mvm_ranges
xdatacls.YKernel.ymin=0
xdatacls.YKernel.yrange=100 # it will be recomputed in classcol_ranges
xdatacls.YKernel.ystep=(xdatacls.YKernel.ymax-xdatacls.YKernel.ymin) \
/xdatacls.YKernel.yrange
## set_printoptions(precision=4)
nparam=4 # C,D,par1,par2
nreport=4 ## accuracy, precision, recall, f1
xdatacls.prepare_repetition_folding(init_train_size=100)
nrepeat0=xdatacls.nrepeat0
nfold0=xdatacls.nfold0
creport=mmr_report_cls.cls_mmr_report()
creport.create_xaprf(nrepeat=nrepeat0,nfold=nfold,nreport=nreport)
xbest_param=np.zeros((nrepeat0,nfold0,nparam))
# ############################################################
nval=max(xdatacls.YKernel.valrange)+1
xconfusion3=np.zeros((nrepeat0,nfold0,xdatacls.YKernel.ndim,nval,nval))
xsolvertime=0.0
ireport=0
for irepeat in range(nrepeat0):
xdatacls.nfold0=xdatacls.nfold
xdatacls.prepare_repetition_training()
## nfold0=1
for ifold in range(nfold0):
xdatacls.prepare_fold_training(ifold)
# validation to choose the best parameters
print('Validation')
xdatacls.set_validation()
cvalidation=mvm_validation_cls.cls_mvm_validation()
cvalidation.validation_rkernel=xdatacls.XKernel[0].title
best_param=cvalidation.mvm_validation(xdatacls)
print('Parameters:',best_param.c,best_param.d, \
best_param.par1,best_param.par2)
print('Best parameters found by validation')
xbest_param[irepeat,ifold,0]=best_param.c
xbest_param[irepeat,ifold,1]=best_param.d
xbest_param[irepeat,ifold,2]=best_param.par1
xbest_param[irepeat,ifold,3]=best_param.par2
# training with the best parameters
print('training')
time0=time.time()
cOptDual= xdatacls.mvm_train()
xsolvertime+=xdatacls.solvertime
print('Training time:',time.time()-time0)
sys.stdout.flush()
# check the train accuracy
print('test on training')
# check the test accuracy
print('test on test')
time0=time.time()
# xdatacls.xdata_tes=ctables.full_test()
# xdatacls.xranges_rel_test=mvm_prepare.mvm_ranges(xdatacls.xdata_tes, \
# xdatacls.nrow)
cPredict=xdatacls.mvm_test()
print('Test time:',time.time()-time0)
sys.stdout.flush()
## ctables.export_prediction(cPredict.Zrow)
# counts the proportion the ones predicted correctly
# ####################################
time0=time.time()
if xdatacls.knowntest==1:
(cEval,icandidate_w,icandidate_b)=mvm_eval(xdatacls.ieval_type, \
xdatacls.nrow, \
xdatacls,cPredict.Zrow)
print('Evaluation time:',time.time()-time0)
## (qtest,qpred,qpred0)=makearray(xdatacls,cPredict.Zrow)
if xdatacls.ieval_type in (0,11):
creport.set_xaprf(irepeat,ifold,cEval)
elif xdatacls.ieval_type==10:
creport.set_xaprf(irepeat,ifold,cEval)
xconfusion3[irepeat,ifold]=cEval.xconfusion3
else:
creport.set_xaprf(irepeat,ifold,cEval)
## xdatacls.icandidate_w=xdatacls.itest[icandidate_w]
## xdatacls.icandidate_b=xdatacls.itest[icandidate_b]
ireport+=1
## print(cEval.xconfusion)
if xdatacls.ieval_type in (0,11):
for xconfrow in cEval.xconfusion:
for ditem in xconfrow:
print('%7.0f'%ditem,end='')
print()
print()
elif xdatacls.ieval_type==10:
for xtable in cEval.xconfusion3:
xsum=np.sum(xtable)
if xsum==0:
xsum=1
xtable=100*xtable/xsum
for xconfrow in xtable:
for ditem in xconfrow:
print('%9.4f'%ditem,end='')
print()
print()
print()
# ####################################
print('*** ipar, repeatation, fold ***')
print(ipar,irepeat,ifold)
if xdatacls.itestmode==1: ## n-fold crossvalidation
creport.report_prf(xmask=[irepeat,ifold], \
stitle='Result in one fold and one repetation', \
ssubtitle='Accuracy on test')
if xdatacls.knowntest==1:
creport.report_prf(xmask=[irepeat,None], \
stitle='Result in one repetation', \
ssubtitle='Mean and std of the accuracy on test')
sys.stdout.flush()
if xdatacls.knowntest==1:
(xmean,xstd)=creport.report_prf(xmask=[None,None], \
stitle='***** Overall result ****', \
ssubtitle='Mean and std of the accuracy on test + error')
xsummary[ipar,0]=xmean[0]
xsummary[ipar,1]=xsolvertime/(nrepeat0*nfold0)
if iloadall==1:
filename='predicted_missing'
if iknown1==1:
filename+='_'+ctables.listknown[ifile1]
else:
filename+='_'+ctables.listfull[ifile1]
if iknown2==1:
filename+='_'+ctables.listknown[ifile2]
else:
filename+='_'+ctables.listfull[ifile2]
filename+='.csv'
ctables.export_test_prediction(filename,xdatacls,cPredict.Zrow)
## (qtest,qpred,qpred0)=makearray(xdatacls,cPredict.Zrow)
print('Average best parameters')
xlabels=('c','d','par1','par2')
for i in range(nparam):
print(xlabels[i],': ',np.mean(xbest_param[:,:,i]), \
'(',np.std(xbest_param[:,:,i]),')')
if xdatacls.knowntest==1:
print('$$$$$$$$$ Summary results:')
(m,n)=xsummary.shape
for i in range(m):
for j in range(n):
print('%10.4f'%xsummary[i,j],end='')
print()
## np.savetxt(fname,xresultte[:ireport,0,:],delimiter=',',fmt='%6.4f')
print('Bye')
return