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matlabModelBuilder.py
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matlabModelBuilder.py
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import snoPatternSplit,sys,re,random,negativeDataGen,trainHoldSplit,snoPatternSplit,subprocess,fastaU,os
hacaFastaHum = "seq/haca_human_snoRNA.fa"
cdFastaHum = "seq/cd_human_snoRNA.fa"
hacaFastaGB = "seq/unique_haca_genbank.fa"
cdFastaGB = "seq/unique_cd_genbank.fa"
ncRNAGB = "seq/unique_rand_ncRNA_50-200_genbank.fa"
allDNAGB = "seq/unique_rand_allDNA_50-200_genbank.fa"
"""
A function that writes the feature vectors as well as the type vectors to an m-file for matlab processing
featuresIn = the feature vectors to write to the file
headersIn = the headers associated with the sequences and features (not neccesarily used)
nameOut = the name for the file and the matlab function to load it
debug = boolean for printing debug data
"""
def writeToMfile(featuresIn,typesIn,headersIn,nameOut,directory,debug):
totalData = featuresIn
mFileHeader = "function [X,t,header] = " + nameOut + "()\n"
mFileEnd = "end"
#generating strings for an m-file
fOut = "\tX={\n"
nRow = 0
counter = 0;
nItem = 0
#features
for item in featuresIn:
counter += 1
nItem += 1
fOut += "\t\t"
fOut += str(item)
if nItem<len(featuresIn):
fOut += ";\n"
else:
fOut += "\n\t};\n"
nItem = 0
tOut = "\tt={\n\t\t"
counter = 0;
#types
if type(typesIn[0]) is list:
for item in typesIn:
counter += 1
nItem += 1
tOut += str(item)
if nItem<len(typesIn):
tOut += ";"
if (counter%50)==0:
tOut += "\n\t\t"
else:
tOut += "\n\t};"
else:
for item in typesIn:
counter += 1
nItem += 1
tOut += " " + str(item) + " "
if nItem<len(typesIn):
tOut += ";"
else:
tOut += "\n\t};"
#headers
nItem = 0
hOut = "\n\theader={\n\t\t"
counter = 0;
for item in headersIn:
counter += 1
nItem += 1
hOut += "'" + item + "'"
if nItem<len(headersIn):
hOut += " ; "
else:
hOut += "\n\t};\n"
if debug >=1:
print tOut
print fOut
mFile = open((directory+nameOut + ".m"), 'w')
mFile.write(mFileHeader)
mFile.write(fOut)
mFile.write(tOut)
mFile.write(hOut)
mFile.write(mFileEnd)
mFile.close()
return
def randomizeSeqs(heads,seqs):
seqOut = []
headOut = []
indices = range(len(heads))
random.shuffle(indices)
for n in indices:
seqOut.append(seqs[n])
headOut.append(heads[n])
return [headOut,seqOut]
def dataGen(percHold,allIn,ncIn,typeIn):
#build datasets for the models
#1. create postive and negative datasets
pHead,pSeq = fastaU.read(typeIn,True,False)
nHead,nSeq = negativeDataGen.compileData(len(pSeq),ncIn,40,allIn,40,True,20,True,False)
#randomize the data
[pHead,pSeq] = randomizeSeqs(pHead,pSeq)
[nHead,nSeq] = randomizeSeqs(nHead,nSeq)
return pHead,pSeq,nHead,nSeq
def foldSplit(split,heads,seqs,types):
lenSplit = len(heads)/split
if lenSplit*split<len(heads):
lenSplit += 1
headOut = [0]*split
seqOut = [0]*split
typesOut = [0]*split
i = 0
#print "len(headOut) = " + str(len(headOut))
for start in range(len(heads))[::lenSplit]:
end = start + lenSplit
#print "index is: " + str(i) + "\nstart is: " + str(start) + "\nend is: " + str(end)
if end<len(heads):
headOut[i] = heads[start:end]
seqOut[i] = seqs[start:end]
typesOut[i] = types[start:end]
else:
headOut[i] = heads[start:]
seqOut[i] = seqs[start:]
typesOut[i] = types[start:]
i += 1
return headOut,seqOut,typesOut
def writeCrumInput(trainHead,trainFeat,trainType,holdHead,holdFeat,holdType,directory,functionName):
trainName = "FEAT_" + functionName + "_train"
holdName = "FEAT_" + functionName + "_hold"
writeToMfile(trainFeat,trainType,trainHead,trainName,directory,False)
writeToMfile(holdFeat,holdType,holdHead,holdName,directory,False)
mTemplate = open("TEMPLATE_ncRNA_testing.m")
mFile = open((directory + functionName + ".m"), 'w')
for line in mTemplate:
line = line.replace("%FUNCTONNAME%",functionName)
line = line.replace("%TRAINING%",trainName)
line = line.replace("%HOLDING%",holdName)
mFile.write(line)
mFile.close()
mTemplate.close()
def makeDirectory(name):
FNULL = open(os.devnull, 'w')
directory = "./" + name + "/"
if subprocess.call(["ls", directory],stdout=FNULL, stderr=subprocess.STDOUT) == 0:
subprocess.call(["rm", "-r", directory])
subprocess.call(["mkdir", directory])
subprocess.call(["chmod", "+s",directory])
else:
subprocess.call(["mkdir", directory])
subprocess.call(["chmod", "+s",directory])
def writeToMasterMfile(functionNameL,batchName,directory,masterName):
mTemplate = open("TEMPLATE_ncRNA_foldMaster.m")
mFile = open(("./" + directory + "/" + masterName + ".m"), 'a')
for line in mTemplate:
lineOut = line.replace("%BATCHNAME%", batchName)
if lineOut.find("%FUNCTIONNAME%") > 0:
lineTemp = lineOut
lineOut = ""
for functionName in functionNameL:
lineOut += (lineTemp.replace("%FUNCTIONNAME%", functionName).replace(";",";\n"))
mFile.write(lineOut)
mFile.close()
mTemplate.close()
maxWindow = 4
prefix = "haca-1"
foldN = 10
directory = prefix
makeDirectory(directory)
masterName = "run_" + prefix
print "reading in fastas..."
#read and generate
pHead,pSeq = fastaU.read(hacaFastaGB,True,False)
nHead,nSeq = negativeDataGen.compileData(len(pSeq),ncRNAGB,40,allDNAGB,40,True,20,True,False)
print "randomizing data..."
#randomize the data
pHead,pSeq = randomizeSeqs(pHead,pSeq)
nHead,nSeq = randomizeSeqs(nHead,nSeq)
print "making type lists..."
#make type lists
pType = [1]*len(pHead)
nType = [0]*len(nHead)
#make holdouts
print "spliting for fold validation..."
pHeadL,pSeqL,pTypeL = foldSplit(foldN,pHead,pSeq,pType)
nHeadL,nSeqL,nTypeL = foldSplit(foldN,nHead,nSeq,nType)
print "writing splits to fastas..."
for i in range(len(pHeadL)):
fastaU.write(pHeadL[i],pSeqL[i],("./" + directory + "/fold-" + str(i) + "_pos_" + prefix + ".fasta"))
for i in range(len(nHeadL)):
fastaU.write(nHeadL[i],nSeqL[i],("./" + directory + "/fold-" + str(i) + "_neg_" + prefix + ".fasta"))
print "generating scripts per window width..."
for window in range(maxWindow):
print "for window " + str(window) + "..."
#gather features
pFeatL = []
nFeatL = []
print "getting features..."
for seqL in pSeqL:
pFeatT = []
for seq in seqL:
pFeatT.append(str(snoPatternSplit.makeFeatureVector(seq,window+1)))
pFeatL.append(pFeatT)
for seqL in nSeqL:
nFeatT = []
for seq in seqL:
nFeatT.append(str(snoPatternSplit.makeFeatureVector(seq,window+1)))
nFeatL.append(nFeatT)
print "done."
#set up a directory
subPrefix = "window-" + str(window+1)
subDirectory = directory + "/" + subPrefix
makeDirectory(subDirectory)
batchName = (subPrefix + "_" + prefix)
functionBatchL = []
for j in range(foldN):
print "writing files for fold " + str(j) + "..."
cpHeadL = pHeadL[:]
cpTypeL = pTypeL[:]
cpFeatL = pFeatL[:]
cnHeadL = nHeadL[:]
cnTypeL = nTypeL[:]
cnFeatL = nFeatL[:]
functionName = ("fold_" + str(j) + "_" + subPrefix + "_" + prefix).replace("-","_")
functionBatchL.append(functionName)
holdHead = cpHeadL.pop(j) + cnHeadL.pop(j)
trainHeadL = cpHeadL + cnHeadL
trainHead = []
for tHead in trainHeadL:
trainHead += tHead
holdFeat = cpFeatL.pop(j) + cnFeatL.pop(j)
trainFeat = []
trainFeatL = cpFeatL + cnFeatL
for tFeat in trainFeatL:
trainFeat += tFeat
holdType = cpTypeL.pop(j) + cnTypeL.pop(j)
trainType = []
trainTypeL = cpTypeL + cnTypeL
for tType in trainTypeL:
trainType += tType
writeCrumInput(trainHead,trainFeat,trainType,holdHead,holdFeat,holdType,(subDirectory + "/"),functionName)
writeToMasterMfile(functionBatchL,batchName.replace("-","_").replace("window_","w"),directory,masterName.replace("-","_"))
subprocess.call(["chmod", "775","-R", ("./" + directory + "/")])
#################################################################################################################################
maxWindow = 4
prefix = "cd-1"
foldN = 10
directory = prefix
makeDirectory(directory)
masterName = "run_" + prefix
print "reading in fastas..."
#read and generate
pHead,pSeq = fastaU.read(cdFastaGB,True,False)
nHead,nSeq = negativeDataGen.compileData(len(pSeq),ncRNAGB,40,allDNAGB,40,True,20,True,False)
print "randomizing data..."
#randomize the data
pHead,pSeq = randomizeSeqs(pHead,pSeq)
nHead,nSeq = randomizeSeqs(nHead,nSeq)
print "making type lists..."
#make type lists
pType = [1]*len(pHead)
nType = [0]*len(nHead)
#make holdouts
print "spliting for fold validation..."
pHeadL,pSeqL,pTypeL = foldSplit(foldN,pHead,pSeq,pType)
nHeadL,nSeqL,nTypeL = foldSplit(foldN,nHead,nSeq,nType)
print "writing splits to fastas..."
for i in range(len(pHeadL)):
fastaU.write(pHeadL[i],pSeqL[i],("./" + directory + "/fold-" + str(i) + "_pos_" + prefix + ".fasta"))
for i in range(len(nHeadL)):
fastaU.write(nHeadL[i],nSeqL[i],("./" + directory + "/fold-" + str(i) + "_neg_" + prefix + ".fasta"))
print "generating scripts per window width..."
for window in range(maxWindow):
print "for window " + str(window) + "..."
#gather features
pFeatL = []
nFeatL = []
print "getting features..."
for seqL in pSeqL:
pFeatT = []
for seq in seqL:
pFeatT.append(str(snoPatternSplit.makeFeatureVector(seq,window+1)))
pFeatL.append(pFeatT)
for seqL in nSeqL:
nFeatT = []
for seq in seqL:
nFeatT.append(str(snoPatternSplit.makeFeatureVector(seq,window+1)))
nFeatL.append(nFeatT)
print "done."
#set up a directory
subPrefix = "window-" + str(window+1)
subDirectory = directory + "/" + subPrefix
makeDirectory(subDirectory)
batchName = (subPrefix + "_" + prefix)
functionBatchL = []
for j in range(foldN):
print "writing files for fold " + str(j) + "..."
cpHeadL = pHeadL[:]
cpTypeL = pTypeL[:]
cpFeatL = pFeatL[:]
cnHeadL = nHeadL[:]
cnTypeL = nTypeL[:]
cnFeatL = nFeatL[:]
functionName = ("fold_" + str(j) + "_" + subPrefix + "_" + prefix).replace("-","_")
functionBatchL.append(functionName)
holdHead = cpHeadL.pop(j) + cnHeadL.pop(j)
trainHeadL = cpHeadL + cnHeadL
trainHead = []
for tHead in trainHeadL:
trainHead += tHead
holdFeat = cpFeatL.pop(j) + cnFeatL.pop(j)
trainFeat = []
trainFeatL = cpFeatL + cnFeatL
for tFeat in trainFeatL:
trainFeat += tFeat
holdType = cpTypeL.pop(j) + cnTypeL.pop(j)
trainType = []
trainTypeL = cpTypeL + cnTypeL
for tType in trainTypeL:
trainType += tType
writeCrumInput(trainHead,trainFeat,trainType,holdHead,holdFeat,holdType,(subDirectory + "/"),functionName)
writeToMasterMfile(functionBatchL,batchName.replace("-","_").replace("window_","w"),directory,masterName.replace("-","_"))
subprocess.call(["chmod", "775","-R", ("./" + directory + "/")])