listDocOrg = fTxtOrg.readlines()
print 'len(listDocOrg): ', len(listDocOrg)
for rowOfListDocOrg in listDocOrg:
# print 'rowOfListDocOrg: ', rowOfListDocOrg
# myResult = p.search(rowOfListDocOrg)
# if myResult <> None:
# myData = re.sub('^Title: |^Abstract: ','',myResult.group())
# outf.write(myData)
outf.write(rowOfListDocOrg)
listMyWords.extend(rowOfListDocOrg.split())
# listDoc.append((myData.split(),fileOne[9:11]))
print '(rowOfListDocOrg.split(),outputFileNameDiff): ', (outputFileNameDiff, rowOfListDocOrg.split())
listDoc.append((rowOfListDocOrg.split(),outputFileNameDiff))
idxCrossValidation = 0
for listTrainWithDiff, listValidationWithDiff in k_fold_cross_validation(listDoc, numFold, randomize = True):
# outputPercentageFilenameBase = typeTextPreprocess+str(idxCrossValidation)+'-Per'
outputPercentageFilenameBase = typeTextPreprocess+str(idxCrossValidation)
with open(dirMain+dirOutput+outputPercentageFilenameBase+'-'+outputFileNameDiff+'-Train-'+'.txt', 'wb') as outf2:
for oneRowOfListTrainWithDiff in listTrainWithDiff:
# listAllDocWords.extend(oneRowOfListTrainWithDiff[0])
outf2.write(' '.join(oneRowOfListTrainWithDiff[0])+'\n')
with open(dirMain+dirOutput+outputPercentageFilenameBase+'-'+outputFileNameDiff+'-Test-'+'.txt', 'wb') as outf3:
for oneRowOflistValidationWithDiff in listValidationWithDiff:
print 'oneRowOflistValidationWithDiff: ', oneRowOflistValidationWithDiff
outf3.write(' '.join(oneRowOflistValidationWithDiff[0])+'\n')
idxCrossValidation = idxCrossValidation + 1
def fCreadeCrossValidationFiles(numFold):
# numFold = 10
listMyType = ['stp-', 'wnl-', 'ptr-']
#typeTextPreprocess = 'stp-'
typeTextPreprocess = 'wnl-'
#typeTextPreprocess = 'ptr-'
myRe = '((^Title: |^Abstract: )(.*))'
p = re.compile(myRe)
# dirMain = ''
# dirMain = os.path.expanduser('~')+'/' # '/home/kimiko'
dirMain = os.path.expanduser('~')+'/' + 'Data/TestDir/' # '/home/kimiko'
logging.info("dirMain = os.path.expanduser('~')+'/': " + dirMain)
dirInput = 'Output1/'
dirOutputTest = 'Output2_TestingSet/'
logging.info("dirOutputTest: " + dirOutputTest)
dirOutputMergeFile = 'Output2_Merge/'
dirOutputTrain = 'Output2_TrainingSet/'
#filesInput = ['pure-doc-dx.txt', 'pure-doc-tx.txt']
#filesInput = ['intervention.txt', 'patient.txt', 'outcome.txt']
ListInputFilenameTxt = []
# ListInputFilenameTxt = ['intervention.txt', 'patient.txt', 'outcome.txt']
ListInputFilenameTxtTmp = os.listdir(dirMain + dirInput)
for itemOfListInputFilenameTxtTmp in ListInputFilenameTxtTmp:
statinfo = os.stat(dirMain + dirInput + itemOfListInputFilenameTxtTmp)
if statinfo.st_size > numFold*1500:
ListInputFilenameTxt.append(itemOfListInputFilenameTxtTmp)
else:
os.remove(dirMain + dirInput + itemOfListInputFilenameTxtTmp)
# ListInputFilenameTxt = os.listdir(dirMain + dirInput)
# print "Unique Combinations of 2 letters from :",ListInputFil?enameTxt
logging.info("Unique Combinations of 2 letters from: " + ', '.join(ListInputFilenameTxt))
# exit()
#for uc in xuniqueCombinations(['l','o','v','e'],2): print ''.join(uc)
#listFilesInputCombinations = [ [typeTextPreprocess+'intervention.txt', typeTextPreprocess+'patient.txt']
# ,[typeTextPreprocess+'intervention.txt', typeTextPreprocess+'outcome.txt']
# ,[typeTextPreprocess+'patient.txt', typeTextPreprocess+'outcome.txt']
# ]
#filesInput = [typeTextPreprocess+'intervention.txt', typeTextPreprocess+'patient.txt']
#filesInput = [typeTextPreprocess+'intervention.txt', typeTextPreprocess+'outcome.txt']
#filesInput = [typeTextPreprocess+'patient.txt', typeTextPreprocess+'outcome.txt']
#for typeTextPreprocess in listMyType:
# dirMain = os.getcwd()+'/'
logging.info("dirMain + dirOutputTest: " + dirMain + dirOutputTest)
if os.path.isdir(dirMain + dirOutputTest):
try:
shutil.rmtree(dirMain+dirOutputTest)
# os.mkdir(dirMain + dirOutputTest)
except:
raise
os.mkdir(dirMain + dirOutputTest)
logging.info("dirMain + dirOutputMergeFile: " + dirMain + dirOutputMergeFile)
if os.path.isdir(dirMain + dirOutputMergeFile):
try:
shutil.rmtree(dirMain+dirOutputMergeFile)
except:
raise
os.mkdir(dirMain + dirOutputMergeFile)
logging.info("dirMain + dirOutputTrain: " + dirMain + dirOutputTrain)
if os.path.isdir(dirMain + dirOutputTrain):
try:
shutil.rmtree(dirMain+dirOutputTrain)
except:
raise
os.mkdir(dirMain + dirOutputTrain)
# exit()
for fileOne in ListInputFilenameTxt:
# outputFileNameDiff = fileOne[0:3]
outputFileNameDiff = fileOne[0:-4]
# print 'outputFileNameDiff: ', outputFileNameDiff
logging.info('outputFileNameDiff: '+ outputFileNameDiff)
listMyWords = []
listDoc = []
logging.info(dirMain+dirOutputTest+typeTextPreprocess+outputFileNameDiff+'.csv')
with open(dirMain+dirOutputTest+typeTextPreprocess+outputFileNameDiff+'.csv', 'wb') as outf:
# filePioTxt= dirMain+dirInput+typeTextPreprocess+fileOne
filePioTxt= dirMain+dirInput + fileOne
with open(filePioTxt) as fTxtOrg:
listDocOrg = fTxtOrg.readlines()
# print 'len(listDocOrg): ', len(listDocOrg)
logging.info('len(listDocOrg): '+ str(len(listDocOrg)))
for rowOfListDocOrg in listDocOrg:
# print 'rowOfListDocOrg: ', rowOfListDocOrg
# myResult = p.search(rowOfListDocOrg)
# if myResult <> None:
# myData = re.sub('^Title: |^Abstract: ','',myResult.group())
# outf.write(myData)
outf.write(rowOfListDocOrg)
listMyWords.extend(rowOfListDocOrg.split())
# listDoc.append((myData.split(),fileOne[9:11]))
# print '(rowOfListDocOrg.split(),outputFileNameDiff): ', (outputFileNameDiff, rowOfListDocOrg.split())
logging.debug('(rowOfListDocOrg.split(),outputFileNameDiff): '+ outputFileNameDiff + " - "+ str(rowOfListDocOrg.split()))
listDoc.append((rowOfListDocOrg.split(),outputFileNameDiff))
# exit()
idxCrossValidation = 0
# def k_fold_cross_validation(X, K, randomise = False):
# for listTrainWithDiff, listValidationWithDiff in k_fold_cross_validation(listDoc, numFold, randomize = True):
for listTrainWithDiff, listValidationWithDiff in k_fold_cross_validation(listDoc, numFold, True):
# outputPercentageFilenameBase = typeTextPreprocess+str(idxCrossValidation)+'-Per'
outputPercentageFilenameBase = typeTextPreprocess+str(idxCrossValidation)
with open(dirMain+dirOutputMergeFile+typeTextPreprocess+'-'+str(idxCrossValidation)+'-Train-'+'.csv', 'a') as outfFullTrain:
with open(dirMain+dirOutputMergeFile+typeTextPreprocess+'-'+str(idxCrossValidation)+'-Test-'+'.csv', 'a') as outfFullTest:
with open(dirMain+dirOutputTrain+outputPercentageFilenameBase+'-'+outputFileNameDiff+'-Train-'+'.csv', 'wb') as outf2:
for oneRowOfListTrainWithDiff in listTrainWithDiff:
# listAllDocWords.extend(oneRowOfListTrainWithDiff[0])
outf2.write(' '.join(oneRowOfListTrainWithDiff[0])+'\n')
outfFullTrain.write(' '.join(oneRowOfListTrainWithDiff[0])+'\n')
with open(dirMain+dirOutputTest+outputPercentageFilenameBase+'-'+outputFileNameDiff+'-Test-'+'.csv', 'wb') as outf3:
for oneRowOflistValidationWithDiff in listValidationWithDiff:
# print 'oneRowOflistValidationWithDiff: ', oneRowOflistValidationWithDiff
# print 'type(oneRowOflistValidationWithDiff): ', type(oneRowOflistValidationWithDiff)
# exit()
logging.debug('oneRowOflistValidationWithDiff: ' + str(oneRowOflistValidationWithDiff))
outf3.write(' '.join(oneRowOflistValidationWithDiff[0])+'\n')
outfFullTest.write(' '.join(oneRowOflistValidationWithDiff[0])+'\n')
idxCrossValidation = idxCrossValidation + 1
es_entrenamiento = numpy.asarray(es_entrenamiento)
archivo = numpy.concatenate((datos,es_entrenamiento[numpy.newaxis, :].T), axis=1)
#Verificamos el tipo de CV solicitado y ejecutamos el CV correspondiente,
#CV hay q ejecutarlo 3 veces, uno para predicciones de 1R, otro para predicciones 2R y otro para 2R_1R
#Se hace kfold
if options.kf == True:
validation_k = int(options.kfolds)
#Se aplica cv para predecir 1r
respuestas, fold_error_t, fold_error_v, final_error_t, final_error_v = crossValidation.k_fold_cross_validation(validation_k, test_percentage, datos1r, options, "1r")
print("Ronda 1")
print("FoldAccuracyT", 100 - fold_error_t)
print("FoldAccuracyV", 100 -fold_error_v)
print("FinalAccuracyT",100 - final_error_t)
print("FinalAccuracyV",100 - final_error_v)
print("--------------------------------------")
respuestas = numpy.asarray(respuestas)
archivo = numpy.concatenate((archivo,respuestas[numpy.newaxis, :].T), axis=1)
#Se aplica cv para predecir 2r
respuestas, fold_error_t, fold_error_v, final_error_t, final_error_v = crossValidation.k_fold_cross_validation(validation_k, test_percentage, datos2r, options, "2r")
print("Ronda 2")
print("FoldAccuracyT", 100 - fold_error_t)
print("FoldAccuracyV", 100 - fold_error_v)
print("FinalAccuracyT",100 - final_error_t)
