def saveTrainingModel(rScript,
args,
path,
pTargetVariableKey,
pDouble="",
treeOrNot="",
treeFileName=""):
algo = getAlgoName(args)
if len(pDouble) == 0:
outputFileName = path+'/'+algo+pTargetVariableKey+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.model'
modelValueFileName = path+'/'+algo+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.coef'
else:
outputFileName = path+'/'+algo+pTargetVariableKey+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.model'
modelValueFileName = path+'/'+algo+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.coef'
rScript.write('\nprint (paste("Section8: Saving the model in file ' +
outputFileName + '")) \n')
rScript.write('save(fit, file = "' + outputFileName + '")\n')
rScript.write('l = coef(fit, s = "lambda.min")\n')
rScript.write('string_intercept = paste("' + pTargetVariableKey +
'" , "-intercept-value = ",toString(l[1]),"\\n",sep="")\n')
rScript.write(
'string_intercept = paste(string_intercept,"vector-of-alphas-' +
pTargetVariableKey + ' = ",sep="")\n')
rScript.write('for(i in 2:length(l)){\n')
rScript.write(
' string_intercept = paste(string_intercept,l[i],",",sep="")\n')
rScript.write('}\n')
rScript.write('string_intercept = paste(string_intercept,"\\n",sep="")\n')
rScript.write('cat(string_intercept,file="' + modelValueFileName +
'",sep="",append=TRUE)\n')
def ToReadTargetFile(rScript,config):
rScript.write('print ("Section2: Read target files") \n')
lTargetSet = config["target"]
rScript.write('lDirectorySet<-strsplit(args[2],";",fixed=TRUE,useBytes=FALSE)\n')
for target in lTargetSet:
userFriendlyName = lTargetSet[target]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
fileToRead = lTargetSet[target]+ attribute.generateExtension()
rScript.write('lengthOfEachDay = numeric()\n')
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/t/'+fileToRead+'.bin",sep=""))\n')
rScript.write(' ' + target+'<- ' + userFriendlyName + skipRowCode + '\n')
rScript.write(' rm(' + userFriendlyName + ')\n')
rScript.write(' lengthOfEachDay = c(lengthOfEachDay,nrow(' + target + '))\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else{\n')
rScript.write(' load(paste(file,"/t/'+fileToRead+'.bin",sep=""))\n')
rScript.write(' temp<-' + userFriendlyName + skipRowCode + '\n')
rScript.write(' rm(' + userFriendlyName + ')\n')
rScript.write(' lengthOfEachDay = c(lengthOfEachDay,nrow(temp))\n')
rScript.write(' '+target+'<-rbind('+target+',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading '+ fileToRead +'.target' + '") \n')
rScript.write('}\n')
def scriptWrapperForDayWiseCorrelation(pTrainingDay):
lDate = os.path.basename(os.path.abspath(pTrainingDay))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-dt." + args.dt + attribute.generateExtension() + ".r"
utility.runCommand([lFileName, '-d', pTrainingDay], args.run,
args.sequence)
def main():
parser = argparse.ArgumentParser(
description=
'Generates train.r. A sample command is :- src/corrRGenForE.py -e ob/e/nsefut/CorExpHINDALCO/ -td ob/data/ro/nsefut/20141017/ -dt 10 -iT HINDALCO -sP -1 -oT 0'
)
parser.add_argument(
'-e',
required=True,
help='Experiement folder to use to find the features and targets')
parser.add_argument('-td',
required=True,
help="Day on which it was trained")
parser.add_argument('-dt',
required=True,
help="Number of days it was trained")
parser.add_argument('-iT', required=False, help='Instrument name')
parser.add_argument('-sP',
required=False,
help='Strike price of instrument')
parser.add_argument('-oT', required=False, help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT, args.sP, args.oT)
print "Using the experiment folder " + args.e
print args.e + "/design.ini"
config = ConfigObj(args.e + "/design.ini")
print "The config parameters that I am working with are"
print config
dirName = os.path.dirname(args.e) + "/"
rProgName = "corr-td." + os.path.basename(os.path.abspath(
args.td)) + "-dt." + args.dt + attribute.generateExtension() + ".r"
rProgLocation = dirName + '/' + rProgName
rScript = open(rProgLocation, 'w')
rScript.write('#!/usr/bin/Rscript \n')
rCodeGen.ForSetUpChecks(rScript)
lCorrelationFileName = dirName + '/correlation-coef' + '-td.' + os.path.basename(
os.path.abspath(args.td)
) + '-dt.' + args.dt + attribute.generateExtension() + ".coef"
rCodeGen.ToReadTargetFile(rScript, config)
for target in config['target']:
rCodeGen.ToFindCorrelationAndPrintingToFile(rScript, config, target,
lCorrelationFileName)
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x " + rProgLocation)
def getTargetValuesIntoDict(pTargetValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.d.replace('/ro/', '/wf/')
config = ConfigObj(args.e + "/design.ini")
target = config["target"]
lTargetBuyValuesDict = dict()
targetBuyValuesFileName = dirName + "/t/" + target[
'buy'] + attribute.generateExtension() + ".target"
print("Buy Target values file : " + targetBuyValuesFileName)
sys.stdout.flush()
targetBuyValuesFile = open(targetBuyValuesFileName)
fileHasHeader = True
numberOfLinesInBuyTargetValuesFile = functionToReadTargetFileToDictionary(
targetBuyValuesFile, lTargetBuyValuesDict, fileHasHeader)
print("Finished reading the buy target values file")
print("The number of elements in the buy target values dictionary is : " +
str(len(lTargetBuyValuesDict)))
# if (numberOfLinesInBuyTargetValuesFile != len(lTargetBuyValuesDict)):
# print("Number of duplicate time stamps rejected in buy target values dictionary = " + str(numberOfLinesInBuyTargetValuesFile - len(lTargetBuyValuesDict)))
# os._exit(-1)
sys.stdout.flush()
lTargetSellValuesDict = dict()
targetSellValuesFileName = dirName + "/t/" + target[
'sell'] + attribute.generateExtension() + ".target"
print("Sell Target values file : " + targetSellValuesFileName)
sys.stdout.flush()
targetSellValuesFile = open(targetSellValuesFileName)
fileHasHeader = True
numberOfLinesInSellTargetValuesFile = functionToReadTargetFileToDictionary(
targetSellValuesFile, lTargetSellValuesDict, fileHasHeader)
print("Finished reading the sell target values file")
print("The number of elements in the sell target values dictionary is : " +
str(len(lTargetSellValuesDict)))
# if (numberOfLinesInSellTargetValuesFile != len(lTargetSellValuesDict)):
# print("Number of duplicate timestamps rejected in sell target values dictionary = " + str(numberOfLinesInSellTargetValuesFile - len(lTargetSellValuesDict)))
# os._exit(-1)
sys.stdout.flush()
#-----------------Getting target values into dictionary -------------------------------------
for elements in lTargetBuyValuesDict.keys():
pTargetValuesDict[elements] = {}
pTargetValuesDict[elements]['buy'] = lTargetBuyValuesDict[elements]
pTargetValuesDict[elements]['sell'] = lTargetSellValuesDict[elements]
def getPredictCommandList(experimentFolder,algoName,predictFolder,trainFolder,pNumberOfDays,pWtsTaken):
commandList = list()
# lets make a list of all the scripts that need to be run
predictScriptNames = glob.glob(experimentFolder+"/predict" + algoName + "-td." + os.path.basename(os.path.abspath(trainFolder))\
+ "-dt." + pNumberOfDays + "-pd." + os.path.basename(os.path.abspath(predictFolder)) +"-wt."\
+ pWtsTaken + attribute.generateExtension() + "-For*.r")
dirName = predictFolder.replace('/ro/','/wf/')
for predictScriptName in predictScriptNames:
commandList.append([predictScriptName,"-d",dirName])
return commandList
def getPredictCommandList(experimentFolder, algoName, predictFolder,
trainFolder, pNumberOfDays, pWtsTaken):
commandList = list()
# lets make a list of all the scripts that need to be run
predictScriptNames = glob.glob(experimentFolder+"/predict" + algoName + "-td." + os.path.basename(os.path.abspath(trainFolder))\
+ "-dt." + pNumberOfDays + "-pd." + os.path.basename(os.path.abspath(predictFolder)) +"-wt."\
+ pWtsTaken + attribute.generateExtension() + "-For*.r")
dirName = predictFolder.replace('/ro/', '/wf/')
for predictScriptName in predictScriptNames:
commandList.append([predictScriptName, "-d", dirName])
return commandList
def getTrainCommandList(experimentFolder,algoName,trainFolder,pNumberOfDays,pWtsTaken):
commandList = list()
# lets make a list of all the scripts that need to be run
trainScriptNames = glob.glob(experimentFolder+"/train" + algoName + "-td." + os.path.basename(os.path.abspath(trainFolder)) + \
"-dt." + pNumberOfDays +"-wt." + pWtsTaken + attribute.generateExtension() +"-For*.r")
dirName = trainFolder.replace('/ro/','/wf/')
trainingDataList = attribute.getListOfTrainingDirectoriesNames(args.dt,dirName,args.iT)
trainingDataListString = ";".join(trainingDataList)
# if len(trainingDataList)>1:
# trainingDataListString = "\"" + trainingDataListString + "\""
for trainScriptName in trainScriptNames:
commandList.append([trainScriptName,"-d",trainingDataListString])
return commandList
def saveTrainingModel(rScript,args,path,pTargetVariableKey,pDouble="", treeOrNot = "", treeFileName = ""):
algo = getAlgoName(args)
if len(pDouble)==0:
outputFileName = path+'/'+algo+pTargetVariableKey+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.model'
modelValueFileName = path+'/'+algo+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.coef'
else:
outputFileName = path+'/'+algo+pTargetVariableKey+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.model'
modelValueFileName = path+'/'+algo+ '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + \
args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.coef'
rScript.write('\nprint (paste("Section8: Saving the model in file '+ outputFileName +'")) \n')
rScript.write('save(fit, file = "'+ outputFileName+'")\n')
rScript.write('l = coef(fit, s = "lambda.min")\n')
rScript.write('string_intercept = paste("' + pTargetVariableKey + '" , "-intercept-value = ",toString(l[1]),"\\n",sep="")\n')
rScript.write('string_intercept = paste(string_intercept,"vector-of-alphas-'+ pTargetVariableKey + ' = ",sep="")\n')
rScript.write('for(i in 2:length(l)){\n')
rScript.write(' string_intercept = paste(string_intercept,l[i],",",sep="")\n')
rScript.write('}\n')
rScript.write('string_intercept = paste(string_intercept,"\\n",sep="")\n')
rScript.write('cat(string_intercept,file="'+ modelValueFileName + '",sep="",append=TRUE)\n')
def ToFindCorrelationAndPrintingToFile(rScript, config, pTargetVariableKey,
pFileName):
features = config["features-" + pTargetVariableKey]
rScript.write('\nprint ("Section6: To Find Correlation For ' +
pTargetVariableKey + '") \n')
rScript.write('string_intercept = paste("CorrelationCoeficient Of ","' +
pTargetVariableKey + '" , ":- ","\\n",sep="")\n')
rScript.write('cat(string_intercept,file="' + pFileName +
'",sep="",append=TRUE)\n')
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
userFriendlyName = userFriendlyName.replace('(', '')
userFriendlyName = userFriendlyName.replace(')', '')
featureNameWithoutBrackets = features[feature].replace(
'(', '').replace(')', '') + attribute.generateExtension()
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/f/' +
featureNameWithoutBrackets + '.bin",sep=""))\n')
rScript.write(' ' + feature + pTargetVariableKey + '<-get("' +
userFriendlyName + '")' + skipRowCode + ' \n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else {\n')
rScript.write(' load(paste(file,"/f/' +
featureNameWithoutBrackets + '.bin",sep=""))\n')
rScript.write(' temp<-get("' + userFriendlyName + '")' +
skipRowCode + '\n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' ' + feature + pTargetVariableKey + '<-rbind(' +
feature + pTargetVariableKey + ',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading ' + featureNameWithoutBrackets +
'.feature' + '") \n')
rScript.write('}\n')
userFriendlyName = features[feature]
rScript.write('tempCor <- cor(' + pTargetVariableKey + '[,2] , ' +
feature + pTargetVariableKey + '[,2] )\n')
rScript.write('string_intercept = paste("' + userFriendlyName +
'" ,"=",toString(tempCor),"\\n",sep="")\n')
rScript.write('cat(string_intercept,file="' + pFileName +
'",sep="",append=TRUE)\n')
rScript.write('rm(' + feature + pTargetVariableKey + ')\n')
rScript.write('string_intercept = paste("\\n","\\n",sep="")\n')
rScript.write('cat(string_intercept,file="' + pFileName +
'",sep="",append=TRUE)\n')
def getTargetValuesIntoDict(pTargetValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.d.replace('/ro/','/wf/')
config = ConfigObj(args.e+"/design.ini")
target = config["target"]
lTargetBuyValuesDict = dict()
targetBuyValuesFileName = dirName+"/t/" + target['buy']+ attribute.generateExtension() + ".target"
print("Buy Target values file : "+ targetBuyValuesFileName)
sys.stdout.flush()
targetBuyValuesFile = open(targetBuyValuesFileName)
fileHasHeader = True
numberOfLinesInBuyTargetValuesFile = functionToReadTargetFileToDictionary(targetBuyValuesFile,lTargetBuyValuesDict,fileHasHeader)
print("Finished reading the buy target values file")
print("The number of elements in the buy target values dictionary is : " + str(len(lTargetBuyValuesDict)))
# if (numberOfLinesInBuyTargetValuesFile != len(lTargetBuyValuesDict)):
# print("Number of duplicate time stamps rejected in buy target values dictionary = " + str(numberOfLinesInBuyTargetValuesFile - len(lTargetBuyValuesDict)))
# os._exit(-1)
sys.stdout.flush()
lTargetSellValuesDict = dict()
targetSellValuesFileName = dirName+"/t/" + target['sell']+ attribute.generateExtension() + ".target"
print("Sell Target values file : "+ targetSellValuesFileName)
sys.stdout.flush()
targetSellValuesFile = open(targetSellValuesFileName)
fileHasHeader = True
numberOfLinesInSellTargetValuesFile = functionToReadTargetFileToDictionary(targetSellValuesFile,lTargetSellValuesDict,fileHasHeader)
print("Finished reading the sell target values file")
print("The number of elements in the sell target values dictionary is : " + str(len(lTargetSellValuesDict)))
# if (numberOfLinesInSellTargetValuesFile != len(lTargetSellValuesDict)):
# print("Number of duplicate timestamps rejected in sell target values dictionary = " + str(numberOfLinesInSellTargetValuesFile - len(lTargetSellValuesDict)))
# os._exit(-1)
sys.stdout.flush()
#-----------------Getting target values into dictionary -------------------------------------
for elements in lTargetBuyValuesDict.keys():
pTargetValuesDict[elements] = {}
pTargetValuesDict[elements]['buy'] = lTargetBuyValuesDict[elements]
pTargetValuesDict[elements]['sell'] = lTargetSellValuesDict[elements]
def getTrainCommandList(experimentFolder, algoName, trainFolder, pNumberOfDays,
pWtsTaken):
commandList = list()
# lets make a list of all the scripts that need to be run
trainScriptNames = glob.glob(experimentFolder+"/train" + algoName + "-td." + os.path.basename(os.path.abspath(trainFolder)) + \
"-dt." + pNumberOfDays +"-wt." + pWtsTaken + attribute.generateExtension() +"-For*.r")
dirName = trainFolder.replace('/ro/', '/wf/')
trainingDataList = attribute.getListOfTrainingDirectoriesNames(
args.dt, dirName, args.iT)
trainingDataListString = ";".join(trainingDataList)
# if len(trainingDataList)>1:
# trainingDataListString = "\"" + trainingDataListString + "\""
for trainScriptName in trainScriptNames:
commandList.append([trainScriptName, "-d", trainingDataListString])
return commandList
def ForLoadingModel(rScript,args,path,pTargetVariableKey,config):
features = config["features-"+pTargetVariableKey]
if(args.a == 'glmnet'):
rScript.write('print ("Section7: Running glmnet") \n')
rScript.write('X <- cbind(')
currentFeatureNumber=0
for feature in features:
rScript.write(features.keys()[currentFeatureNumber]+pTargetVariableKey+'[,2]')
currentFeatureNumber = currentFeatureNumber+1
if(len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(')\n')
predictionModel = args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + '.model'
rScript.write('load("'+os.path.dirname(path)+'/'+predictionModel+'")\n')
def ForLoadingModel(rScript, args, path, pTargetVariableKey, config):
features = config["features-" + pTargetVariableKey]
if (args.a == 'glmnet'):
rScript.write('print ("Section7: Running glmnet") \n')
rScript.write('X <- cbind(')
currentFeatureNumber = 0
for feature in features:
rScript.write(features.keys()[currentFeatureNumber] +
pTargetVariableKey + '[,2]')
currentFeatureNumber = currentFeatureNumber + 1
if (len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(')\n')
predictionModel = args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + '.model'
rScript.write('load("' + os.path.dirname(path) + '/' + predictionModel +
'")\n')
def ToReadFeatureFiles(rScript,
config,
targetVariable,
pUseWhichArgumentForData=2):
features = config["features-" + targetVariable]
rScript.write('\nprint ("Section3: Read feature files") \n')
if pUseWhichArgumentForData == 4:
rScript.write(
'lDirectorySet<-strsplit(args[4],";",fixed=TRUE,useBytes=FALSE)\n')
else:
rScript.write(
'lDirectorySet<-strsplit(args[2],";",fixed=TRUE,useBytes=FALSE)\n')
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
userFriendlyName = userFriendlyName.replace('(', '')
userFriendlyName = userFriendlyName.replace(')', '')
featureNameWithoutBrackets = features[feature].replace(
'(', '').replace(')', '') + attribute.generateExtension()
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/f/' +
featureNameWithoutBrackets + '.bin",sep=""))\n')
rScript.write(' ' + feature + targetVariable + '<-get("' +
userFriendlyName + '")' + skipRowCode + ' \n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else {\n')
rScript.write(' load(paste(file,"/f/' +
featureNameWithoutBrackets + '.bin",sep=""))\n')
rScript.write(' temp<-get("' + userFriendlyName + '")' +
skipRowCode + '\n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' ' + feature + targetVariable + '<-rbind(' +
feature + targetVariable + ',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading ' + featureNameWithoutBrackets +
'.feature' + '") \n')
rScript.write('}\n')
def ToReadTargetFile(rScript, config):
rScript.write('print ("Section2: Read target files") \n')
lTargetSet = config["target"]
rScript.write(
'lDirectorySet<-strsplit(args[2],";",fixed=TRUE,useBytes=FALSE)\n')
for target in lTargetSet:
userFriendlyName = lTargetSet[target]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
fileToRead = lTargetSet[target] + attribute.generateExtension()
rScript.write('lengthOfEachDay = numeric()\n')
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/t/' + fileToRead +
'.bin",sep=""))\n')
rScript.write(' ' + target + '<- ' + userFriendlyName +
skipRowCode + '\n')
rScript.write(' rm(' + userFriendlyName + ')\n')
rScript.write(' lengthOfEachDay = c(lengthOfEachDay,nrow(' +
target + '))\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else{\n')
rScript.write(' load(paste(file,"/t/' + fileToRead +
'.bin",sep=""))\n')
rScript.write(' temp<-' + userFriendlyName + skipRowCode + '\n')
rScript.write(' rm(' + userFriendlyName + ')\n')
rScript.write(
' lengthOfEachDay = c(lengthOfEachDay,nrow(temp))\n')
rScript.write(' ' + target + '<-rbind(' + target + ',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading ' + fileToRead + '.target' +
'") \n')
rScript.write('}\n')
def main():
parser = argparse.ArgumentParser(
description=
'Generates predict.r which will use design.model to make predictions. Sample command is pGenForE.py -e ob/e1/'
)
parser.add_argument('-e',
required=True,
help='Directory to find the experiement designs')
parser.add_argument('-a', required=True, help='Algorithm name')
parser.add_argument(
'-s',
required=True,
help='Location of the subfolder that contains the sub experiments')
parser.add_argument('-pd', required=True, help='Prediction directory')
parser.add_argument('-td',
required=True,
help="Day on which it was trained")
parser.add_argument('-dt',
required=True,
help="Number of days it was trained")
parser.add_argument(
'-targetClass',
required=True,
help=
"For which model was used ; binomial(target takes only true and false) / multinomial (target values takes more than 2 values)"
)
parser.add_argument(
'-skipP',
required=False,
help=
"yes or no , If you want to regenerate already generated algorithm prediction file then make this value No"
)
parser.add_argument(
'-wt',
required=False,
help="default/exp , weight type to be given to different days")
parser.add_argument('-iT', required=False, help='Instrument name')
parser.add_argument('-sP',
required=False,
help='Strike price of instrument')
parser.add_argument('-oT', required=False, help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT, args.sP, args.oT)
if args.skipP == None:
args.skipP = "yes"
print "\nRunning pGen.py to generate the predict script"
print "Using the experiment folder " + args.e
config = ConfigObj(args.e + "/design.ini")
print "The config parameters that I am working with are"
print config
dirName = os.path.dirname(args.e)
if args.a is None:
algo = 'glmnet'
else:
algo = args.a
args.s = args.s + "/"
predictDataDirectoryName = args.pd.replace('/ro/', '/wf/')
predictDataDirectoryName = predictDataDirectoryName + "/p/" + os.path.basename(
os.path.dirname(args.e))
if not os.path.exists(predictDataDirectoryName):
os.mkdir(predictDataDirectoryName)
rProgName = "predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-pd." + os.path.basename(os.path.abspath(args.pd)) \
+ "-wt." + args.wt + attribute.generateExtension() +"-For"+os.path.basename(os.path.dirname(args.s))+"SubE.r"
rProgLocation = dirName + '/' + rProgName
rScript = open(rProgLocation, 'w')
rScript.write('#!/usr/bin/Rscript \n')
if (args.a == 'glmnet'):
rScript.write('require (glmnet) \n')
elif (args.a == 'randomForest'):
rScript.write('require (randomForest) \n')
rCodeGen.ForSetUpChecks(rScript)
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript, config, target)
rCodeGen.ForSanityChecks(rScript, config, target)
designFiles = utility.list_files(args.s)
for designFile in designFiles:
print "Generating r code for " + designFile
rScript.write('\n\nprint ("Running r code for' + designFile + '")\n')
config = ConfigObj(designFile)
for target in config['target']:
predictionFileName = predictDataDirectoryName + "/" + args.a + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt +\
'-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(designFile)) + "-wt." + args.wt+ attribute.generateExtension() +".predictions"
if not os.path.isfile(predictionFileName) or (args.skipP.lower()
== "no"):
rCodeGen.ForPredictions(rScript, config, args, designFile,
target)
else:
print predictionFileName + "Already exists , not generating it again . If you want to generate it again then rerun it with -skipP no "
rScript.write('rm(list=ls())')
rScript.close()
print "Finished generating R prediction program: " + rProgLocation
os.system("chmod +x " + rProgLocation)
def main():
dataFile.getDataIntoMatrix(args.pd)
predictedValuesDict = dict()
getPredictedValuesIntoDict(predictedValuesDict)
lower_entry = totalEntryCL[0]
upper_entry = totalEntryCL[1]
lower_exit = totalExitCL[0]
l_index_gap = 0.2
l_max_gap = (upper_entry - lower_entry)/(4)
while l_index_gap < l_max_gap:
exitCL2 = lower_exit + float(l_index_gap)
exitCL1 = lower_exit + 2*float(l_index_gap)
exitCL0 = lower_exit + 3*float(l_index_gap)
exitCL = lower_exit + 4*float(l_index_gap)
if(lower_entry < exitCL):
lower_entry = lower_exit + 4*float(l_index_gap)
entryCL2 = lower_entry + float(l_index_gap)
entryCL1 = lower_entry + 2*float(l_index_gap)
entryCL0 = lower_entry + 3*float(l_index_gap)
entryCL = lower_entry + 4*float(l_index_gap)
l_entry_list = [entryCL2,entryCL1,entryCL0 , entryCL]
l_exit_list = [exitCL2,exitCL1,exitCL0 , exitCL]
print("Entry Exit list" + str(l_entry_list)+ str(l_exit_list))
if(exitCL < entryCL2 and entryCL <= upper_entry):
lInitialFileName = args.a + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + \
'-l.'+entryCL+"-"+entryCL0+"-"+entryCL1+"-"+entryCL2+"-"+exitCL +"-"+exitCL0 +"-"+exitCL1 +"-"+exitCL2 + "-tq." + args.orderQty + "-te.10"
initialFileName.append(lInitialFileName)
for lFileName in initialFileName:
readOnceAndWrite(lFileName, l_entry_list, l_exit_list, predictedValuesDict)
else:
l_index_gap = l_index_gap + 0.1
gEntryCLList = args.entryCL.split(";")
gExitCLList = args.exitCL.split(";")
gStandingAtAskPMinusOneTickInCloseSell = 0
gStandingAtBidPPlusOneTickInCloseBuy = 0
gPipsTaken = int(args.pipTaken)
gOpenBuyFillPrice = 0
gOpenSellFillPrice = 0
initialFileName = []
for indexOfCL in range(0,len(gEntryCLList)):
lInitialFileName = args.a + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ "-pipTaken." + args.pipTaken+ attribute.generateExtension() + \
'-l.'+gEntryCLList[indexOfCL]+"-"+gExitCLList[indexOfCL] + "-tq." + args.orderQty + "-te.13"
initialFileName.append(lInitialFileName)
g_quantity_adjustment_list_for_sell = {}
g_quantity_adjustment_list_for_buy = {}
g_bestqty_list_for_sell = {}
g_bestqty_list_for_buy = {}
class ticks_values_to_be_stored(object):
def __init__(self):
self.MsgCode = ''
self.OrderType = ''
self.NewP = 0.0
self.NewQ = 0
self.OldP = 0.0
def main():
parser = argparse.ArgumentParser(description='Generates train.r. A sample command is mGenForE.py -e ob/e1/ ')
parser.add_argument('-e', required=True,help='Experiement folder to use to find the features and targets')
parser.add_argument('-a', required=True,help='Algorithm name')
parser.add_argument('-targetClass',required=True,help="binomial(target takes only true and false) / multinomial (target values takes more than 2 values)")
parser.add_argument('-skipT',required=False,help="yes or no , If you want to regenerate already generated algorithm model file then make this value No")
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-wt',required=True,help="default/exp , weight type to be given to different days")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
parser.add_argument('-treeType',required=False,help="Tree read for trade engine")
parser.add_argument('-tTD',required=False,help="Tree number of days to be used")
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.skipT == None:
args.skipT = "yes"
if args.tTD == None:
args.tTD = args.dt
print "Using the experiment folder " + args.e
config = ConfigObj(args.e+"/design1.ini")
configInit = ConfigObj(args.e+"design.ini")
# configInitList = []
# for iniFile in os.listdir(args.e + "/"):
# if '.ini' in iniFile and iniFile != 'design.ini':
# index = iniFile[ file.index(".") - 1 ]
# configInitList.append( ( index, ConfigObj(args.e+"/"+iniFile) ) )
# configInit = dict(configInitList)
print "The config parameters that I am working with are"
print config
dirName=os.path.dirname(args.e)+"/"
algo = rCodeGen.getAlgoName(args)
rProgName = "traintree" + "-td." + os.path.basename(os.path.abspath(args.td)) + "-tTD" + args.tTD + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension() +".r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
rScript.write('require (rpart) \n')
rCodeGen.ForSetUpChecks(rScript)
lAllFilePresent = True
for target in config['target']:
lTreeFileName = dirName+"/"+algo+ target+'-td.' + os.path.basename(os.path.abspath(args.td)) + "-tTD" + args.tTD + '-dt.' + args.dt + attribute.generateExtension() +".tree" + args.treeType
if os.path.isfile(lTreeFileName) and ( args.skipT.lower() == "yes" ):
continue
else:
lAllFilePresent = False
break
if lAllFilePresent == False:
rCodeGen.ToReadTargetFile(rScript,config)
rCodeGen.ForWtVectorGeneration(rScript,args.wt.lower())
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript,config,target)
rCodeGen.ToReadPredictionFiles(rScript,config,target,configInit)
rCodeGen.ForSanityChecks(rScript,config,target)
lTreeFileName = dirName+"/"+algo+ target+'-td.' + os.path.basename(os.path.abspath(args.td)) + "-tTD" + args.tTD +'-dt.' + args.dt + attribute.generateExtension() +".tree" + args.treeType
if os.path.isfile(lTreeFileName) and ( args.skipT.lower() == "yes" ):
print "Model File " + lTreeFileName + " already exists . So it will not be formed again . If you want to re-generate model then re-run with -skipT=No"
else:
rCodeGen.ToRenameDataBeforeTraining(rScript,config,target)
rCodeGen.ForTrainingTree(rScript,args,config,target, args.treeType)
print lTreeFileName
rCodeGen.saveTrainingTree(rScript,args,dirName,target, lTreeFileName)
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x "+rProgLocation)
def getPredictedValuesIntoDict(pPredictedValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.pd.replace('/ro/', '/wf/')
config = ConfigObj(args.e + "/design.ini")
target = config["target"]
predictedValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[0] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
print("Predicted values file : " + predictedValuesFileName)
sys.stdout.flush()
predictedValuesFile = open(predictedValuesFileName)
fileHasHeader = True
numberOfLinesInPredictedValuesFile = 0
for line in predictedValuesFile:
if fileHasHeader == True:
fileHasHeader = False
continue
line = line.rstrip('\n')
splitLine = line.split(',', 2)
timeStamp = float(splitLine[1])
try: #TODO: remove this and then run the code to identify errors.
predictedProb = float(splitLine[2])
except:
predictedProb = 0
pPredictedValuesDict[timeStamp] = predictedProb
numberOfLinesInPredictedValuesFile += 1
print("Finished reading the predicted values file")
print("The number of elements in the predicted values dictionary is : " +
str(len(pPredictedValuesDict)))
if (numberOfLinesInPredictedValuesFile != len(pPredictedValuesDict)):
print("Number of duplicate timestamps rejected = " +
str(numberOfLinesInPredictedValuesFile -
len(pPredictedValuesDict)))
os._exit(-1)
sys.stdout.flush()
def ForPredictions(rScript,
config,
args,
pathToDesignFile,
pTargetVariableKey,
pUseWhichArgumentForData=2,
pDouble=""):
features = config["features-" + pTargetVariableKey]
#Renaming all features if model and predictions are done simultaneously , so that training and prediction data set do not conflict
algo = getAlgoName(args)
if len(pDouble) == 0:
predictionModel = algo + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + '.model'
else:
predictionModel = algo + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + 'double.model'
rScript.write('\nprint ("Section6: Read in prediction model' +
os.path.dirname(pathToDesignFile) + '/' + predictionModel +
'") \n')
rScript.write('load("' + os.path.dirname(pathToDesignFile) + '/' +
predictionModel + '")')
if (args.a == 'glmnet'):
rScript.write('\n\nprint ("Section7: Creating data frame") \n')
rScript.write('df = cbind(')
currentFeatureNumber = 0
for feature in features:
rScript.write(feature + pTargetVariableKey + '[,2]')
currentFeatureNumber = currentFeatureNumber + 1
if (len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a +
' prediction") \n')
rScript.write(
'Prob <- predict (fit, newx = df,s = "lambda.min",type = "response")'
)
rScript.write("\n\n")
elif (args.a == 'logitr'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber = 0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
userFriendlyName = userFriendlyName.replace('(', '')
userFriendlyName = userFriendlyName.replace(')', '')
rScript.write(userFriendlyName + '=' + feature +
pTargetVariableKey + '[,2]')
currentFeatureNumber = currentFeatureNumber + 1
if (len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a +
' prediction") \n')
rScript.write('Prob<- predict (fit, newdata = df, type = "response")')
rScript.write("\n\n")
elif (args.a == 'randomForest'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber = 0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
userFriendlyName = userFriendlyName.replace('(', '')
userFriendlyName = userFriendlyName.replace(')', '')
rScript.write(userFriendlyName + '=' + feature +
pTargetVariableKey + '[,2]')
currentFeatureNumber = currentFeatureNumber + 1
if (len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a +
' prediction") \n')
rScript.write('Prob<- predict (fit, df)')
rScript.write("\n\n")
elif (args.a == 'mda'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber = 0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[', '')
userFriendlyName = userFriendlyName.replace(']', '')
userFriendlyName = userFriendlyName.replace('(', '')
userFriendlyName = userFriendlyName.replace(')', '')
rScript.write(userFriendlyName + '=' + feature +
pTargetVariableKey + '[,2]')
currentFeatureNumber = currentFeatureNumber + 1
if (len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + algo + ' prediction") \n')
rScript.write('Prob<- predict (fit, df)')
rScript.write("\n\n")
else:
print "The only valid options are glmnet, logitr, randomForest or mda"
os._exit(-1)
rScript.write(
'\nprint ("Section9: Creating the data frame to write in the file") \n'
)
rScript.write('dfForFile <- data.frame(' + features.keys()[0] +
pTargetVariableKey + '[,1]) \n')
rScript.write(
'\nprint ("Section10: Putting the probabilities in the data frame") \n'
)
rScript.write('dfForFile <- cbind(dfForFile,Prob) \n')
if len(pDouble) == 0:
rScript.write('\nprint ("Section11: Saving the predictions in file /p/'+ os.path.basename(os.path.dirname(args.e))+'/' + args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) + \
"-wt." + args.wt+ attribute.generateExtension() +'.predictions") \n')
else:
rScript.write('\nprint ("Section11: Saving the predictions in file /p/'+ os.path.basename(os.path.dirname(args.e))+'/' + args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) + \
"-wt." + args.wt+ attribute.generateExtension() +'double.predictions") \n')
if pUseWhichArgumentForData == 4:
if len(pDouble) == 0:
rScript.write('fileName = paste(args[4],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile))+ \
"-wt." + args.wt+ attribute.generateExtension() +'.predictions",sep="") \n')
else:
rScript.write('fileName = paste(args[4],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile))+ \
"-wt." + args.wt+ attribute.generateExtension() +'double.predictions",sep="") \n')
else:
if len(pDouble) == 0:
rScript.write('fileName = paste(args[2],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) +\
"-wt." + args.wt+ attribute.generateExtension() + '.predictions",sep="") \n')
else:
rScript.write('fileName = paste(args[2],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) +\
"-wt." + args.wt+ attribute.generateExtension() + 'double.predictions",sep="") \n')
rScript.write('print (fileName) \n')
rScript.write(
'write.table(format(dfForFile,digits=16), file = fileName,sep=",",quote=FALSE)\n'
)
def scriptWrapperForPredictProgramRun(predictionDirAfterLastTD):
scriptName=lExperimentFolderName+"/predict" + args.a + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +"-pd." +\
os.path.basename(os.path.abspath(predictionDirAfterLastTD)) + "-wt." + args.wt + attribute.generateExtension() +".r"
dirName = predictionDirAfterLastTD.replace('/ro/','/wf/')
utility.runCommand([scriptName,"-d",dirName],args.run,args.sequence)
def ForPredictions(rScript,config,args,pathToDesignFile,pTargetVariableKey,pUseWhichArgumentForData=2,pDouble=""):
features = config["features-"+pTargetVariableKey]
#Renaming all features if model and predictions are done simultaneously , so that training and prediction data set do not conflict
algo = getAlgoName(args)
if len(pDouble)==0:
predictionModel = algo + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + '.model'
else:
predictionModel = algo + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass +\
"-wt." + args.wt+ attribute.generateExtension() + 'double.model'
rScript.write('\nprint ("Section6: Read in prediction model'+os.path.dirname(pathToDesignFile)+'/'+predictionModel+'") \n')
rScript.write('load("'+os.path.dirname(pathToDesignFile)+'/'+predictionModel+'")')
if(args.a == 'glmnet'):
rScript.write('\n\nprint ("Section7: Creating data frame") \n')
rScript.write('df = cbind(')
currentFeatureNumber=0
for feature in features:
rScript.write(feature+pTargetVariableKey+'[,2]')
currentFeatureNumber = currentFeatureNumber+1
if(len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a + ' prediction") \n')
rScript.write('Prob <- predict (fit, newx = df,s = "lambda.min",type = "response")')
rScript.write("\n\n")
elif(args.a == 'logitr'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber=0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
userFriendlyName = userFriendlyName.replace('(','')
userFriendlyName = userFriendlyName.replace(')','')
rScript.write(userFriendlyName+'='+feature+pTargetVariableKey+'[,2]')
currentFeatureNumber = currentFeatureNumber+1
if(len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a + ' prediction") \n')
rScript.write('Prob<- predict (fit, newdata = df, type = "response")')
rScript.write("\n\n")
elif(args.a == 'randomForest'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber=0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
userFriendlyName = userFriendlyName.replace('(','')
userFriendlyName = userFriendlyName.replace(')','')
rScript.write(userFriendlyName+'='+feature+pTargetVariableKey+'[,2]')
currentFeatureNumber = currentFeatureNumber+1
if(len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + args.a + ' prediction") \n')
rScript.write('Prob<- predict (fit, df)')
rScript.write("\n\n")
elif(args.a == 'mda'):
rScript.write('\n\nprint ("Section7: Creating the data frame") \n')
rScript.write('df = data.frame(')
currentFeatureNumber=0
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
userFriendlyName = userFriendlyName.replace('(','')
userFriendlyName = userFriendlyName.replace(')','')
rScript.write(userFriendlyName+'='+feature+pTargetVariableKey+'[,2]')
currentFeatureNumber = currentFeatureNumber+1
if(len(features) > currentFeatureNumber):
rScript.write(',')
rScript.write(")\n\n")
rScript.write('print ("Section8: Running ' + algo + ' prediction") \n')
rScript.write('Prob<- predict (fit, df)')
rScript.write("\n\n")
else:
print "The only valid options are glmnet, logitr, randomForest or mda"
os._exit(-1)
rScript.write('\nprint ("Section9: Creating the data frame to write in the file") \n')
rScript.write('dfForFile <- data.frame('+features.keys()[0]+pTargetVariableKey+'[,1]) \n')
rScript.write('\nprint ("Section10: Putting the probabilities in the data frame") \n')
rScript.write('dfForFile <- cbind(dfForFile,Prob) \n')
if len(pDouble)==0:
rScript.write('\nprint ("Section11: Saving the predictions in file /p/'+ os.path.basename(os.path.dirname(args.e))+'/' + args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) + \
"-wt." + args.wt+ attribute.generateExtension() +'.predictions") \n')
else:
rScript.write('\nprint ("Section11: Saving the predictions in file /p/'+ os.path.basename(os.path.dirname(args.e))+'/' + args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) + \
"-wt." + args.wt+ attribute.generateExtension() +'double.predictions") \n')
if pUseWhichArgumentForData == 4:
if len(pDouble)==0:
rScript.write('fileName = paste(args[4],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile))+ \
"-wt." + args.wt+ attribute.generateExtension() +'.predictions",sep="") \n')
else:
rScript.write('fileName = paste(args[4],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile))+ \
"-wt." + args.wt+ attribute.generateExtension() +'double.predictions",sep="") \n')
else:
if len(pDouble)==0:
rScript.write('fileName = paste(args[2],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) +\
"-wt." + args.wt+ attribute.generateExtension() + '.predictions",sep="") \n')
else:
rScript.write('fileName = paste(args[2],"/p/","' +os.path.basename(os.path.dirname(args.e))+'/'+ args.a + pTargetVariableKey + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(pathToDesignFile)) +\
"-wt." + args.wt+ attribute.generateExtension() + 'double.predictions",sep="") \n')
rScript.write('print (fileName) \n')
rScript.write('write.table(format(dfForFile,digits=16), file = fileName,sep=",",quote=FALSE)\n')
def main():
parser = argparse.ArgumentParser(
description=
'Generates train.r. A sample command is mGenForE.py -e ob/e1/ ')
parser.add_argument(
'-e',
required=True,
help='Experiement folder to use to find the features and targets')
parser.add_argument('-a', required=True, help='Algorithm name')
parser.add_argument(
'-targetClass',
required=True,
help=
"binomial(target takes only true and false) / multinomial (target values takes more than 2 values)"
)
parser.add_argument(
'-skipT',
required=False,
help=
"yes or no , If you want to regenerate already generated algorithm model file then make this value No"
)
parser.add_argument('-td',
required=True,
help="Day on which it was trained")
parser.add_argument('-dt',
required=True,
help="Number of days it was trained")
parser.add_argument(
'-wt',
required=True,
help="default/exp , weight type to be given to different days")
parser.add_argument('-iT', required=False, help='Instrument name')
parser.add_argument('-sP',
required=False,
help='Strike price of instrument')
parser.add_argument('-oT', required=False, help='Options Type')
parser.add_argument('-treeType',
required=False,
help="Tree read for trade engine")
parser.add_argument('-tTD',
required=False,
help="Tree number of days to be used")
args = parser.parse_args()
attribute.initializeInstDetails(args.iT, args.sP, args.oT)
if args.skipT == None:
args.skipT = "yes"
if args.tTD == None:
args.tTD = args.dt
print "Using the experiment folder " + args.e
config = ConfigObj(args.e + "/design1.ini")
configInit = ConfigObj(args.e + "design.ini")
# configInitList = []
# for iniFile in os.listdir(args.e + "/"):
# if '.ini' in iniFile and iniFile != 'design.ini':
# index = iniFile[ file.index(".") - 1 ]
# configInitList.append( ( index, ConfigObj(args.e+"/"+iniFile) ) )
# configInit = dict(configInitList)
print "The config parameters that I am working with are"
print config
dirName = os.path.dirname(args.e) + "/"
algo = rCodeGen.getAlgoName(args)
rProgName = "traintree" + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-tTD" + args.tTD + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension(
) + ".r"
rProgLocation = dirName + '/' + rProgName
rScript = open(rProgLocation, 'w')
rScript.write('#!/usr/bin/Rscript \n')
rScript.write('require (rpart) \n')
rCodeGen.ForSetUpChecks(rScript)
lAllFilePresent = True
for target in config['target']:
lTreeFileName = dirName + "/" + algo + target + '-td.' + os.path.basename(
os.path.abspath(args.td)
) + "-tTD" + args.tTD + '-dt.' + args.dt + attribute.generateExtension(
) + ".tree" + args.treeType
if os.path.isfile(lTreeFileName) and (args.skipT.lower() == "yes"):
continue
else:
lAllFilePresent = False
break
if lAllFilePresent == False:
rCodeGen.ToReadTargetFile(rScript, config)
rCodeGen.ForWtVectorGeneration(rScript, args.wt.lower())
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript, config, target)
rCodeGen.ToReadPredictionFiles(rScript, config, target, configInit)
rCodeGen.ForSanityChecks(rScript, config, target)
lTreeFileName = dirName + "/" + algo + target + '-td.' + os.path.basename(
os.path.abspath(args.td)
) + "-tTD" + args.tTD + '-dt.' + args.dt + attribute.generateExtension(
) + ".tree" + args.treeType
if os.path.isfile(lTreeFileName) and (args.skipT.lower() == "yes"):
print "Model File " + lTreeFileName + " already exists . So it will not be formed again . If you want to re-generate model then re-run with -skipT=No"
else:
rCodeGen.ToRenameDataBeforeTraining(rScript, config, target)
rCodeGen.ForTrainingTree(rScript, args, config, target,
args.treeType)
print lTreeFileName
rCodeGen.saveTrainingTree(rScript, args, dirName, target,
lTreeFileName)
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x " + rProgLocation)
pLPerLotShort=(averageOpenSellPrice - averageCloseBuyPrice)* 1000
pLPerLotLong=(averageCloseSellPrice - averageOpenBuyPrice)* 1000
print("1 lot has 1000 qty's so P/L Short per lot is: " + str(pLPerLotShort), file = outputFile)
print("1 lot has 1000 qty's so P/L Long per lot is: " + str(pLPerLotLong), file = outputFile)
print("P/L for Short trading 10 lots is: " + str(pLPerLotShort * 10), file = outputFile)
print("P/L for Long trading 10 lots is: " + str(pLPerLotLong * 10), file = outputFile)
if __name__ == "__main__":
tStart = datetime.now()
dirName = args.pd.replace('/ro/','/rs/')
checkAllFilesAreExistOrNot = 'false'
lWFDirName = args.pd.replace('/ro/','/wf/')
predictedBuyValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'buy' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + \
args.dt + '-targetClass.' + args.targetClass + '-f.' + buyExperimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
predictedSellValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'sell' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' +\
args.dt + '-targetClass.' + args.targetClass + '-f.' + sellExperimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
lEntryClList = args.entryCL.split(";")
lExitClList = args.exitCL.split(";")
if len(lEntryClList)!= len(lExitClList):
print("Len of entry and exit list does match. Entry List length = " , len(lEntryClList) , " and ExitCL List length = " , len(lExitClList))
os._exit(-1)
lengthOfList = len(lEntryClList)
lMinOfExitCl = 9999.000
fileNameList = []
finalEntryClList = []
finalExitClList = []
parser.add_argument('-sequence', required=True,help='lp / dp / serial')
parser.add_argument('-dt',required=False,help='No of day from start for which it is to be trained ')
parser.add_argument('-wt',required=True,help="default/exp , weight type to be given to different days")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.a is not None:
algo = args.a
else:
algo = 'glmnet'
if args.dt == None:
args.dt = "1"
dirName = args.td.replace('/ro/','/wf/')
scriptName = args.e+"/train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension() +".r"
trainingDataList = attribute.getListOfTrainingDirectoriesNames(args.dt,dirName,args.iT)
trainingDataListString = ";".join(trainingDataList)
utility.runCommand([scriptName,"-d",trainingDataListString],args.run,args.sequence)
dirName = args.pd.replace('/ro/','/wf/')
scriptName=args.e+"/predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +\
"-pd." + os.path.basename(os.path.abspath(args.pd)) + "-wt." + args.wt + attribute.generateExtension() +".r"
utility.runCommand([scriptName,"-d",dirName],args.run,args.sequence)
else:
mainExperimentName = pathAfterE
mainExperimentNameList.append(mainExperimentName)
experimentName = os.path.basename(absPathOfExperimentName)
sys.path.append("./src/")
sys.path.append("./ob/generators/")
config = ConfigObj(experiment + "/design.ini")
featureTargetFilePath = args.pd.replace('ro', 'wf')
for feature in config["features-buy"]:
lName = config["features-buy"][feature].replace('(',
'').replace(')', '')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension(
) + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
for feature in config["features-sell"]:
lName = config["features-sell"][feature].replace('(',
'').replace(')', '')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension(
) + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
dirName = args.pd.replace('/ro/', '/wf/')
required=True,
help="default/exp , weight type to be given to different days")
parser.add_argument('-iT', required=False, help='Instrument name')
parser.add_argument('-sP', required=False, help='Strike price of instrument')
parser.add_argument('-oT', required=False, help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT, args.sP, args.oT)
if args.a is not None:
algo = args.a
else:
algo = 'glmnet'
if args.dt == None:
args.dt = "1"
dirName = args.td.replace('/ro/', '/wf/')
scriptName = args.e + "/train" + algo + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension() + ".r"
trainingDataList = attribute.getListOfTrainingDirectoriesNames(
args.dt, dirName, args.iT)
trainingDataListString = ";".join(trainingDataList)
utility.runCommand([scriptName, "-d", trainingDataListString], args.run,
args.sequence)
dirName = args.pd.replace('/ro/', '/wf/')
scriptName=args.e+"/predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +\
"-pd." + os.path.basename(os.path.abspath(args.pd)) + "-wt." + args.wt + attribute.generateExtension() +".r"
utility.runCommand([scriptName, "-d", dirName], args.run, args.sequence)
if len(args.nodes) == 0:
for target in ['buy', 'sell']:
lTreeFileName = "/home/vikas/ml/ob/e/nsecur/ABAll_AmBRAmBAll/s/2c/AmBRAmB//glmnet" + target + "-td.20140821-tTD30-dt.10.tree1" #args.e+"/"+args.a+ target+'-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + attribute.generateExtension() +".tree" + args.treeType
dd.gGlobalTree[target], lVariable = reading_tree.reading_tree(
lTreeFileName, args.treeType)
dd.gTreeVariablesPresent = dd.gTreeVariablesPresent + lVariable
dd.gFinalCondition[target]["0"] = ''
dd.gFinalCondition[target]["0"] = reading_tree.traverse_tree(
1, args.treeType, 0.0, dd.gGlobalTree[target],
dd.gFinalCondition[target]["0"])
print("Calling tree traversal ") #,dd.gFinalCondition[target]["0"])
else:
for target in ['buy', 'sell']:
lTreeFileName = args.e + "/" + args.a + target + '-td.' + os.path.basename(
os.path.abspath(
args.td)) + '-dt.' + args.dt + attribute.generateExtension(
) + ".tree" + args.treeType
dd.gGlobalTree[target], lVariable = reading_tree.reading_tree(
lTreeFileName, args.treeType)
dd.gTreeVariablesPresent = dd.gTreeVariablesPresent + lVariable
nodes = args.nodes.split(";")
dd.gFinalCondition[target]['nodes'] = reading_tree.traverse_nodes(
args.treeType, nodes, dd.gGlobalTree[target])
config = ConfigObj(args.e + "/design1.ini")
lListOfPredictionDirectory = attribute.getListOfTrainingDirectoriesNames(
int(args.nPD), args.pd, args.iT)
lBuyOutputFileObject = args.e + "/Buy-OutOfSampleTree-" + '-pd.' + os.path.basename(
os.path.abspath(
args.pd)) + '-nPD.' + args.nPD + attribute.generateExtension(
) + ".tree" + args.treeType
lSellOutputFileObject = args.e + "/Sell-OutOfSampleTree-" + '-pd.' + os.path.basename(
if 'nsecur' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nsecur/")+8:]
elif 'nsefut' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nsefut/")+8:]
elif 'nseopt' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nseopt/")+8:]
if "/" in pathAfterE:
mainExperimentName = pathAfterE[:pathAfterE.index("/")]
else:
mainExperimentName = pathAfterE
experimentName = os.path.basename(absPathOfExperimentName)
initialFileName = args.a + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + \
'-l.'+args.entryCL+"-"+args.exitCL + "-te3"
gTickSize = args.tickSize
def getPredictedValuesIntoDict(pPredictedValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.pd.replace('/ro/','/wf/')
config = ConfigObj(args.e+"/design.ini")
target = config["target"]
predictedValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[0] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
print("Predicted values file : "+ predictedValuesFileName)
sys.stdout.flush()
predictedValuesFile = open(predictedValuesFileName)
fileHasHeader = True
numberOfLinesInPredictedValuesFile = 0
lTreeTrainingList = []
for i in range(len(allDataDirectories)-int(args.dt)):
args.td = allDataDirectories[i]
predictionDirLastTD = allDataDirectories[i + int(args.dt) - 1]
predictionDirAfterLastTD = allDataDirectories[i + int(args.dt)]
lRCodeGenCommandList.append(["mRGenForE.py","-e",lExperimentFolderName,"-a",algo,"-targetClass",args.targetClass,"-skipM",args.skipM,"-td",args.td, "-dt" , \
args.dt , '-wt' , wt,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP ,'-double', args.double])
# lRCodeGenCommandList.append(["pRGenForE.py","-e",args.e,"-s",lExperimentFolderName,"-a",algo,"-skipP",args.skipP,"-td",args.td , "-pd" , predictionDirLastTD , \
# "-dt" , args.dt , "-targetClass" , args.targetClass , '-wt' , wt,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP,'-double', args.double])
lRCodeGenCommandList.append(["pRGenForE.py","-e",args.e,"-s",lExperimentFolderName,"-a",algo,"-skipP",args.skipP,"-td",args.td , "-pd" , predictionDirAfterLastTD ,\
"-dt" , args.dt , "-targetClass" , args.targetClass , '-wt' , wt,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP,'-double', args.double])
dirName = args.td.replace('/ro/','/wf/')
if args.double:
scriptName = lExperimentFolderName+"/train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + wt + attribute.generateExtension() +"double.r"
else:
scriptName = lExperimentFolderName+"/train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + wt + attribute.generateExtension() +".r"
trainingDataCorrespondingDateList = []
trainingDataList = [] #";".join(allDataDirectories[i:i+ int(args.dt) ])
lCount = i
treeDataList = []
for trainDirs in allDataDirectories[i:i+ int(args.dt)]:
trainingDataList.append(trainDirs.replace('/ro/','/wf/'))
try:
allDataDirectories[i+ int(args.tTD)]
for treeDirs in allDataDirectories[i:i+ int(args.tTD)]:
#results = map(scriptWrapperForFeatureGeneration,allDataDirectories)
pass
#==========R Code formation to find correlation between features and target file ==============================0
utility.runCommand(["corrRGenForEForAllDays.py","-e",l_exp_dir,"-td",args.td,"-dt",args.dt,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP],args.run,args.sequence)
if args.sequence=="dp":
print dp.printGroupStatus()
#========Running the correlation R program=========================
allWorkingFileDirectories = attribute.getListOfTrainingDirectoriesNames( int(args.nDays) , args.td.replace('/ro/','/wf/') ,args.iT)
allWorkingFileDirectoriesString = ";".join(allWorkingFileDirectories)
lCorrCommandList = []
if args.sequence == "dp":
for l_training_day in allWorkingFileDirectories:
lDate = os.path.basename(os.path.abspath(l_training_day))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + attribute.generateExtension() +".r"
lCorrCommandList.append([lFileName,'-d',l_training_day])
utility.runListOfCommandsWithMaxUtlilizationOfWorkers(lCorrCommandList,args,"Day-wise Correlation",int(args.nComputers))
else:
def scriptWrapperForDayWiseCorrelation(pTrainingDay):
lDate = os.path.basename(os.path.abspath(pTrainingDay))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + attribute.generateExtension() +".r"
utility.runCommand([lFileName,'-d',pTrainingDay],args.run,args.sequence)
results = map(scriptWrapperForDayWiseCorrelation,allWorkingFileDirectories)
summary_file_name = l_exp_dir + '/correlation-coef' + '-td.' + os.path.basename(os.path.abspath(args.td))+ '-dt.' + args.dt + attribute.generateExtension() + ".coef"
wfo = open(summary_file_name, 'w')
lBuyDict = {}
lSellDict = {}
lDayWiseBuy = {}
def main():
parser = argparse.ArgumentParser(description='Generates train.r. A sample command is mGenForE.py -e ob/e1/ ')
parser.add_argument('-e', required=True,help='Experiement folder to use to find the features and targets')
parser.add_argument('-a', required=True,help='Algorithm name')
parser.add_argument('-s', required=True,help='Location of the folder containing all the sub experiments')
parser.add_argument('-targetClass',required=True,help="binomial(target takes only true and false) / multinomial (target values takes more than 2 values)")
parser.add_argument('-skipM',required=False,help="yes or no , If you want to regenerate already generated algorithm model file then make this value No")
parser.add_argument('-skipP',required=False,help="yes or no , If you want to regenerate already generated algorithm prediction file then make this value No")
parser.add_argument('-pd', required=True,help='Prediction directory')
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-wt',required=True,help="default/exp , weight type to be given to different days")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.skipM == None:
args.skipM = "yes"
if args.skipP == None:
args.skipP = "yes"
print "Using the experiment folder " + args.e
print "Training files steps"
config = ConfigObj(args.e+"/design.ini")
print "The config parameters that I am working with are"
print config
predictionDataDirectoryName = args.pd.replace('/ro/','/wf/')
predictionDataDirectoryName = predictionDataDirectoryName + "/p/" + os.path.basename(os.path.dirname(args.e))
if not os.path.exists(predictionDataDirectoryName):
os.mkdir(predictionDataDirectoryName)
dirName=os.path.dirname(args.e)
algo = rCodeGen.getAlgoName(args)
args.s = args.s + "/"
rProgName = "trainPredict"+ algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +\
"-pd." + os.path.basename(os.path.abspath(args.pd)) + "-wt." + args.wt+ attribute.generateExtension() + "-For"+os.path.basename(os.path.dirname(args.s))+"SubE.r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
if(algo == 'glmnet'):
rScript.write('require (glmnet) \n')
elif(algo == 'randomForest'):
rScript.write('require (randomForest) \n')
elif(algo == 'mda'):
rScript.write('require (mda) \n')
rCodeGen.ForSetUpChecksForTrainPredictTogather(rScript)
rCodeGen.ToReadTargetFile(rScript,config)
rCodeGen.ForWtVectorGeneration(rScript,args.wt.lower())
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript,config,target,2)
rCodeGen.ForSanityChecks(rScript,config,target)
print "For prediction data set"
configForPredictions = ConfigObj(args.e+"/design.ini")
print "The config parameters that I am working with are"
for target in configForPredictions['target']:
feature_keys = configForPredictions['features-'+target].keys()
features = configForPredictions['features-'+target]
for key in feature_keys:
new_key = key + "P"
features[new_key] = features[key]
del features[key]
print configForPredictions
rCodeGen.ToReadFeatureFiles(rScript,configForPredictions,target,4)
rCodeGen.ForSanityChecks(rScript,configForPredictions,target)
designFiles = utility.list_files(args.s)
for designFile in designFiles:
print "Generating r code for " + designFile
rScript.write('\n\nprint ("Running r code for ' + designFile + '")\n')
config = ConfigObj(designFile)
configForPredictions = ConfigObj(designFile)
#--------------MODEL--------------------
for target in config['target']:
feature_keys = configForPredictions['features-'+target].keys()
features = configForPredictions['features-'+target]
for key in feature_keys:
new_key = key + "P"
features[new_key] = features[key]
del features[key]
lModelGeneratedAfterTraining = os.path.dirname(designFile) + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() + '.model'
if os.path.isfile(lModelGeneratedAfterTraining)and ( args.skipM.lower() == "yes" ):
print "Model File " + lModelGeneratedAfterTraining + " already exists . So it will not be formed again . If you want to re-generate model then re-run with -skipM=No"
else:
rCodeGen.ToCreateDataFrameForTraining(rScript,config,target)
rCodeGen.ForTraining(rScript,args,config,target)
rCodeGen.saveTrainingModel(rScript,args,os.path.dirname(designFile),target)
#--------------Prediction Part--------------------
predictionFileName = predictionDataDirectoryName + "/" + args.a + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(designFile)) +\
"-wt." + args.wt+ attribute.generateExtension() +".predictions"
if not os.path.isfile(predictionFileName) or ( args.skipP.lower() == "no" ):
rCodeGen.ForPredictions(rScript,configForPredictions,args,designFile,target,4)
else:
print "Prediction File " + predictionFileName + "Already exists , not generating it again . If you want to generate it again then rerun it with -skipP no "
rScript.write('rm(list=ls())')
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x "+rProgLocation)
for target in ['buy','sell']:
lTreeFileName = "/home/vikas/ml/ob/e/nsecur/ABAll_AmBRAmBAll//s/2c/AmBRAmB//glmnet" + target + "-td.20140821-tTD30-dt.10.tree1"#args.e+"/"+args.a+ target+'-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + attribute.generateExtension() +".tree" + args.treeType
dd.gGlobalTree[target],lVariable = reading_tree.reading_tree(lTreeFileName,args.treeType)
dd.gTreeVariablesPresent = dd.gTreeVariablesPresent + lVariable
for entry,exit in zip(lEntryClList,lExitClList):
if entry not in dd.gFinalCondition[target]:
dd.gFinalCondition[target][entry] = ''
dd.gFinalCondition[target][entry] = reading_tree.traverse_tree(1,args.treeType,float("."+entry),dd.gGlobalTree[target],dd.gFinalCondition[target][entry])
print("Calling tree traversal ",dd.gFinalCondition[target][entry])
if exit not in dd.gFinalCondition[target]:
dd.gFinalCondition[target][exit] = ''
dd.gFinalCondition[target][exit] = reading_tree.traverse_tree(1,args.treeType,float("."+exit),dd.gGlobalTree[target],dd.gFinalCondition[target][exit])
print("Calling tree traversal " ,dd.gFinalCondition[target][exit])
else:
for target in ['buy','sell']:
lTreeFileName = args.e+"/"+args.a+ target+'-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + attribute.generateExtension() +".tree" + args.treeType
dd.gGlobalTree[target],lVariable = reading_tree.reading_tree(lTreeFileName,args.treeType)
dd.gTreeVariablesPresent = dd.gTreeVariablesPresent + lVariable
nodes = args.nodes.split(";")
dd.gFinalCondition[target]['nodes'] = reading_tree.traverse_nodes(args.treeType,nodes,dd.gGlobalTree[target])
config = ConfigObj(args.e+"/design1.ini")
for variable in dd.gTreeVariablesPresent:
if variable.lower()=="j":
predictedBuyValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'buy' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + \
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
dd.gFileObjectsOfVariablesPresent.append(open(predictedBuyValuesFileName,'r'))
elif variable.lower()=="k":
predictedSellValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'sell' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' +\
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nsecur/")+8:]
elif 'nsefut' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nsefut/")+8:]
elif 'nseopt' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.index("/nseopt/")+8:]
if "/" in pathAfterE:
mainExperimentName = pathAfterE[:pathAfterE.index("/")]
else:
mainExperimentName = pathAfterE
experimentName = os.path.basename(absPathOfExperimentName)
gTickSize = int(args.tickSize)
gMaxQty = int(args.orderQty)
initialFileName ='TradeOnTarget-d.' + os.path.basename(os.path.abspath(args.d))+ attribute.generateExtension() + "-tq." + args.orderQty + attribute.generateExtension() + "-dte.7"
g_quantity_adjustment_list_for_sell = {}
g_quantity_adjustment_list_for_buy = {}
gOpenBuyPrice = 0.0
gCloseSellPrice = 0.0
gOpenSellPrice = 0.0
gCloseBuyPrice = 0.0
def functionToReadTargetFileToDictionary(pTargetValuesFile,pTargetValuesDict,pFileHeader):
lNumberOfLinesInTargetValuesFile = 0
for line in pTargetValuesFile:
if pFileHeader == True:
pFileHeader = False
continue
def getPredictedValuesIntoDict(pPredictedValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.pd.replace('/ro/','/wf/')
config = ConfigObj(args.e+"/design.ini")
target = config["target"]
lPredictedBuyValuesDict = dict()
predictedBuyValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[0] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
print("Buy Predicted values file : "+ predictedBuyValuesFileName)
sys.stdout.flush()
predictedBuyValuesFile = open(predictedBuyValuesFileName)
fileHasHeader = True
numberOfLinesInBuyPredictedValuesFile = functionToReadPredictionFileToDictionary(predictedBuyValuesFile,lPredictedBuyValuesDict,fileHasHeader)
print("Finished reading the buy predicted values file")
print("The number of elements in the buy predicted values dictionary is : " + str(len(lPredictedBuyValuesDict)))
if (numberOfLinesInBuyPredictedValuesFile != len(lPredictedBuyValuesDict)):
print("Number of duplicate time stamps rejected in buy predicted values dictionary = " + str(numberOfLinesInBuyPredictedValuesFile - len(lPredictedBuyValuesDict)))
#os._exit(-1)
sys.stdout.flush()
lPredictedSellValuesDict = dict()
predictedSellValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[1] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt + attribute.generateExtension()+ ".predictions"
print("Sell Predicted values file : "+ predictedSellValuesFileName)
sys.stdout.flush()
predictedSellValuesFile = open(predictedSellValuesFileName)
fileHasHeader = True
numberOfLinesInSellPredictedValuesFile = functionToReadPredictionFileToDictionary(predictedSellValuesFile,lPredictedSellValuesDict,fileHasHeader)
print("Finished reading the sell predicted values file")
print("The number of elements in the sell predicted values dictionary is : " + str(len(lPredictedSellValuesDict)))
if (numberOfLinesInSellPredictedValuesFile != len(lPredictedSellValuesDict)):
print("Number of duplicate timestamps rejected in sell predicted values dictionary = " + str(numberOfLinesInSellPredictedValuesFile - len(lPredictedSellValuesDict)))
#os._exit(-1)
sys.stdout.flush()
#-----------------Getting predicted values into dictionary -------------------------------------
for elements in lPredictedBuyValuesDict.keys():
pPredictedValuesDict[elements] = {}
pPredictedValuesDict[elements]['buy'] = lPredictedBuyValuesDict[elements]
pPredictedValuesDict[elements]['sell'] = lPredictedSellValuesDict[elements]
if "/" in pathAfterE:
mainExperimentName = pathAfterE[:pathAfterE.index("/")]
else:
mainExperimentName = pathAfterE
experimentName = os.path.basename(absPathOfExperimentName)
gTickSize = int(args.tickSize)
gMaxQty = int(args.orderQty)
startTimeList = args.startTime.split(";")
endTimeList = args.endTime.split(";")
initialFileName = []
for indexOfCL in range(0, len(startTimeList)):
lInitialFileName = 'DummyTradeEngine-d.' + os.path.basename(
os.path.abspath(args.d)
) + attribute.generateExtension(
) + '-l.' + startTimeList[indexOfCL] + "-" + endTimeList[
indexOfCL] + "-tq." + args.orderQty + "-tarType" + args.targetType + attribute.generateExtension(
) + "-dte.7"
initialFileName.append(lInitialFileName)
g_quantity_adjustment_list_for_sell = {}
g_quantity_adjustment_list_for_buy = {}
def functionToReadTargetFileToDictionary(pTargetValuesFile, pTargetValuesDict,
pFileHeader):
lNumberOfLinesInTargetValuesFile = 0
for line in pTargetValuesFile:
if pFileHeader == True:
pFileHeader = False
continue
def getPredictedValuesIntoDict(pPredictedValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.pd.replace('/ro/','/wf/')
config = ConfigObj(args.e+"/design.ini")
target = config["target"]
predictedValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[0] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
print("Predicted values file : "+ predictedValuesFileName)
sys.stdout.flush()
predictedValuesFile = open(predictedValuesFileName)
fileHasHeader = True
numberOfLinesInPredictedValuesFile = 0
for line in predictedValuesFile:
if fileHasHeader == True:
fileHasHeader = False
continue
line=line.rstrip('\n')
splitLine = line.split(',',2)
timeStamp = float(splitLine[1])
try:#TODO: remove this and then run the code to identify errors.
predictedProb = float(splitLine[2])
except:
predictedProb = 0
pPredictedValuesDict[timeStamp] = predictedProb
numberOfLinesInPredictedValuesFile += 1
print("Finished reading the predicted values file")
print("The number of elements in the predicted values dictionary is : " + str(len(pPredictedValuesDict)))
if (numberOfLinesInPredictedValuesFile != len(pPredictedValuesDict)):
print("Number of duplicate timestamps rejected = " + str(numberOfLinesInPredictedValuesFile - len(pPredictedValuesDict)))
os._exit(-1)
sys.stdout.flush()
else:
mainExperimentName = pathAfterE
mainExperimentNameList.append(mainExperimentName)
experimentName = os.path.basename(absPathOfExperimentName)
sys.path.append("./src/")
sys.path.append("./ob/generators/")
config = ConfigObj(experiment+"/design.ini")
featureTargetFilePath = args.pd.replace('ro', 'wf')
for feature in config["features-buy"]:
lName = config["features-buy"][feature].replace('(','').replace(')','')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName+ attribute.generateExtension() + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
for feature in config["features-sell"]:
lName = config["features-sell"][feature].replace('(','').replace(')','')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension() + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
dirName = args.pd.replace('/ro/','/wf/')
targetSet = config['target']
args.dt, "-iT", args.iT, "-oT", args.oT, "-sP", args.sP
], args.run, args.sequence)
if args.sequence == "dp":
print dp.printGroupStatus()
#========Running the correlation R program=========================
allWorkingFileDirectories = attribute.getListOfTrainingDirectoriesNames(
int(args.nDays), args.td.replace('/ro/', '/wf/'), args.iT)
allWorkingFileDirectoriesString = ";".join(allWorkingFileDirectories)
lCorrCommandList = []
if args.sequence == "dp":
for l_training_day in allWorkingFileDirectories:
lDate = os.path.basename(os.path.abspath(l_training_day))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-dt." + args.dt + attribute.generateExtension() + ".r"
lCorrCommandList.append([lFileName, '-d', l_training_day])
utility.runListOfCommandsWithMaxUtlilizationOfWorkers(
lCorrCommandList, args, "Day-wise Correlation", int(args.nComputers))
else:
def scriptWrapperForDayWiseCorrelation(pTrainingDay):
lDate = os.path.basename(os.path.abspath(pTrainingDay))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-dt." + args.dt + attribute.generateExtension() + ".r"
utility.runCommand([lFileName, '-d', pTrainingDay], args.run,
args.sequence)
results = map(scriptWrapperForDayWiseCorrelation,
allWorkingFileDirectories)
def scriptWrapperForDayWiseCorrelation(pTrainingDay):
lDate = os.path.basename(os.path.abspath(pTrainingDay))
lFileName = l_exp_dir + "/corr-date-" + lDate + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + attribute.generateExtension() +".r"
utility.runCommand([lFileName,'-d',pTrainingDay],args.run,args.sequence)
def main():
parser = argparse.ArgumentParser(description='Generates predict.r which will use design.model to make predictions. Sample command is pGenForE.py -e ob/e1/')
parser.add_argument('-e', required=True,help='Directory to find the experiement designs')
parser.add_argument('-a', required=True,help='Algorithm name')
parser.add_argument('-pd', required=True,help='Prediction directory')
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-wt',required=True,help="exp/default")
parser.add_argument('-targetClass',required=True,help="For which model was used ; binomial(target takes only true and false) / multinomial (target values takes more than 2 values)")
parser.add_argument('-skipP',required=False,help="yes or no , If you want to regenerate already generated algorithm prediction file then make this value No")
parser.add_argument('-s',required=False,help="Experiment sub folders")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
parser.add_argument('-double',required=False,help='Double training of in model')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.skipP == None:
args.skipP = "yes"
if args.s == None:
args.s = args.e
print "\nRunning pGen.py to generate the predict script"
print "Using the experiment folder " + args.e
config = ConfigObj(args.s+"/design.ini")
print "The config parameters that I am working with are"
print config
dirName=os.path.dirname(args.s)
if args.a is None:
algo ='glmnet'
else:
algo =args.a
import pdb
#pdb.set_trace()
if args.double:
rProgName = "predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-pd." + os.path.basename(os.path.abspath(args.pd)) \
+ "-wt." + args.wt+ attribute.generateExtension() + "double.r"
else:
rProgName = "predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-pd." + os.path.basename(os.path.abspath(args.pd)) \
+ "-wt." + args.wt+ attribute.generateExtension() + ".r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
predictDataDirectoryName = args.pd.replace('/ro/','/wf/')
predictDataDirectoryName = predictDataDirectoryName + "/p/" + os.path.basename(os.path.dirname(args.e)) + "/"
if not os.path.exists(predictDataDirectoryName):
os.mkdir(predictDataDirectoryName)
if(args.a == 'glmnet'):
rScript.write('require (glmnet) \n')
elif(args.a == 'randomForest'):
rScript.write('require (randomForest) \n')
rCodeGen.ForSetUpChecks(rScript)
lAllFilePresent = True
for target in config['target']:
if args.double:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +"double.predictions"
else:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +".predictions"
if os.path.isfile(predictionFileName) and ( args.skipP.lower() == "yes" ):
continue
else:
lAllFilePresent = False
break
if lAllFilePresent == False:
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript,config,target)
rCodeGen.ForSanityChecks(rScript,config,target)
if args.double:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +"double.predictions"
else:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +".predictions"
if not os.path.isfile(predictionFileName) or ( args.skipP.lower() == "no" ):
if args.double:
lModelGeneratedAfterTraining = args.s + '/' + args.a + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt + 'double.model'
rCodeGen.ForPredictions(rScript,config,args,args.s,target,2,"double")
else:
lModelGeneratedAfterTraining = args.s + '/' + args.a + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt + '.model'
rCodeGen.ForPredictions(rScript,config,args,args.s,target)
print lModelGeneratedAfterTraining
else:
print predictionFileName + "Already exists , not generating it again . If you want to generate it again then rerun it with -skipP no "
rScript.close()
print "Finished generating R prediction program: " + rProgLocation
os.system("chmod +x "+rProgLocation)
def scriptWrapperForPredictProgramRun(predictionDirAfterLastTD):
scriptName=lExperimentFolderName+"/predict" + args.a + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +"-pd." +\
os.path.basename(os.path.abspath(predictionDirAfterLastTD)) + "-wt." + args.wt + attribute.generateExtension() +".r"
dirName = predictionDirAfterLastTD.replace('/ro/', '/wf/')
utility.runCommand([scriptName, "-d", dirName], args.run,
args.sequence)
def main():
parser = argparse.ArgumentParser(description='Generates train.r. A sample command is :- src/corrRGenForE.py -e ob/e/nsefut/CorExpHINDALCO/ -td ob/data/ro/nsefut/20141017/ -dt 10 -iT HINDALCO -sP -1 -oT 0')
parser.add_argument('-e', required=True,help='Experiement folder to use to find the features and targets')
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
print "Using the experiment folder " + args.e
print args.e+"/design.ini"
config = ConfigObj(args.e+"/design.ini")
print "The config parameters that I am working with are"
print config
dirName=os.path.dirname(args.e)+"/"
trainingDaysDirectory = attribute.getListOfTrainingDirectoriesNames( int(args.dt) , args.td ,args.iT)
for l_trainingday in trainingDaysDirectory:
rProgName = "corr-date-"+ os.path.basename(os.path.abspath(l_trainingday)) +"-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + attribute.generateExtension() +".r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
rCodeGen.ForSetUpChecks(rScript)
lCorrelationFileName = dirName + '/correlation-coef-date-'+ os.path.basename(os.path.abspath(l_trainingday)) + '-td.' + os.path.basename(os.path.abspath(args.td))+ '-dt.' + args.dt + attribute.generateExtension() + ".coef"
rCodeGen.ToReadTargetFile(rScript,config)
for target in config['target']:
rCodeGen.ToFindCorrelationDatewiseAndPrintingToFile(rScript,config,target,lCorrelationFileName)
rScript.close()
os.system("chmod +x "+rProgLocation)
def getPredictedValuesIntoDict(pPredictedValuesDict):
# The following will take care if args.e = "ob/e1/" or args.e = "ob/e1"
dirName = args.pd.replace('/ro/','/wf/')
config = ConfigObj(args.e+"/design.ini")
target = config["target"]
lPredictedBuyValuesDict = dict()
predictedBuyValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[0] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
print("Buy Predicted values file : "+ predictedBuyValuesFileName)
sys.stdout.flush()
predictedBuyValuesFile = open(predictedBuyValuesFileName)
fileHasHeader = True
numberOfLinesInBuyPredictedValuesFile = functionToReadPredictionFileToDictionary(predictedBuyValuesFile,lPredictedBuyValuesDict,fileHasHeader)
print("Finished reading the buy predicted values file")
print("The number of elements in the buy predicted values dictionary is : " + str(len(lPredictedBuyValuesDict)))
if (numberOfLinesInBuyPredictedValuesFile != len(lPredictedBuyValuesDict)):
print("Number of duplicate time stamps rejected in buy predicted values dictionary = " + str(numberOfLinesInBuyPredictedValuesFile - len(lPredictedBuyValuesDict)))
# os._exit(-1)
sys.stdout.flush()
lPredictedSellValuesDict = dict()
predictedSellValuesFileName = dirName+"/p/"+mainExperimentName+"/"+args.a + target.keys()[1] + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt + attribute.generateExtension()+ ".predictions"
print("Sell Predicted values file : "+ predictedSellValuesFileName)
sys.stdout.flush()
predictedSellValuesFile = open(predictedSellValuesFileName)
fileHasHeader = True
numberOfLinesInSellPredictedValuesFile = functionToReadPredictionFileToDictionary(predictedSellValuesFile,lPredictedSellValuesDict,fileHasHeader)
print("Finished reading the sell predicted values file")
print("The number of elements in the sell predicted values dictionary is : " + str(len(lPredictedSellValuesDict)))
if (numberOfLinesInSellPredictedValuesFile != len(lPredictedSellValuesDict)):
print("Number of duplicate timestamps rejected in sell predicted values dictionary = " + str(numberOfLinesInSellPredictedValuesFile - len(lPredictedSellValuesDict)))
# os._exit(-1)
sys.stdout.flush()
#-----------------Getting predicted values into dictionary -------------------------------------
for elements in lPredictedBuyValuesDict.keys():
pPredictedValuesDict[elements] = {}
pPredictedValuesDict[elements]['buy'] = lPredictedBuyValuesDict[elements]
pPredictedValuesDict[elements]['sell'] = lPredictedSellValuesDict[elements]
pLPerLotLong=(averageCloseSellPrice - averageOpenBuyPrice)* 1000
print("1 lot has 1000 qty's so P/L Short per lot is: " + str(pLPerLotShort), file = outputFile)
print("1 lot has 1000 qty's so P/L Long per lot is: " + str(pLPerLotLong), file = outputFile)
print("P/L for Short trading 10 lots is: " + str(pLPerLotShort * 10), file = outputFile)
print("P/L for Long trading 10 lots is: " + str(pLPerLotLong * 10), file = outputFile)
if __name__ == "__main__":
tStart = datetime.now()
dirName = args.pd.replace('/ro/','/rs/')
checkAllFilesAreExistOrNot = 'false'
lWFDirName = args.pd.replace('/ro/','/wf/')
if args.double:
predictedBuyValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'buy' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + \
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + "double.predictions"
predictedSellValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'sell' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' +\
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + "double.predictions"
else:
predictedBuyValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'buy' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + \
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
predictedSellValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'sell' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' +\
args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
lEntryClList = args.entryCL.split(";")
lExitClList = args.exitCL.split(";")
if len(lEntryClList)!= len(lExitClList):
print("Len of entry and exit list does match. Entry List length = " , len(lEntryClList) , " and ExitCL List length = " , len(lExitClList))
os._exit(-1)
def main():
parser = argparse.ArgumentParser(description='Generates train.r. A sample command is mGenForE.py -e ob/e1/ ')
parser.add_argument('-e', required=True,help='Experiement folder to use to find the features and targets')
parser.add_argument('-a', required=True,help='Algorithm name')
parser.add_argument('-targetClass',required=True,help="binomial(target takes only true and false) / multinomial (target values takes more than 2 values)")
parser.add_argument('-skipM',required=False,help="yes or no , If you want to regenerate already generated algorithm model file then make this value No")
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-wt',required=True,help="default/exp , weight type to be given to different days")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
parser.add_argument('-double',required=False,help='Double training of in model')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.skipM == None:
args.skipM = "yes"
print "Using the experiment folder " + args.e
config = ConfigObj(args.e+"/design.ini")
print "The config parameters that I am working with are"
print config
dirName=os.path.dirname(args.e)+"/"
algo = rCodeGen.getAlgoName(args)
if args.double:
rProgName = "train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension() +"double.r"
else:
rProgName = "train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension() +".r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
if(algo == 'glmnet'):
rScript.write('require (glmnet) \n')
elif(algo == 'randomForest'):
rScript.write('require (randomForest) \n')
elif(algo == 'mda'):
rScript.write('require (mda) \n')
rCodeGen.ForSetUpChecks(rScript)
lAllFilePresent = True
for target in config['target']:
if args.double:
lModelGeneratedAfterTraining = dirName + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td))\
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.model'
else:
lModelGeneratedAfterTraining = dirName + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td))\
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.model'
if os.path.isfile(lModelGeneratedAfterTraining) and ( args.skipM.lower() == "yes" ):
continue
else:
lAllFilePresent = False
break
if lAllFilePresent == False:
rCodeGen.ToReadTargetFile(rScript,config)
rCodeGen.ForWtVectorGeneration(rScript,args.wt.lower())
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript,config,target)
rCodeGen.ForSanityChecks(rScript,config,target)
if args.double:
lModelGeneratedAfterTraining = dirName + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td))\
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'double.model'
lTempModelName = dirName + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td))\
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.model'
else:
lModelGeneratedAfterTraining = dirName + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td))\
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() +'.model'
if os.path.isfile(lModelGeneratedAfterTraining) and ( args.skipM.lower() == "yes" ):
print "Model File " + lModelGeneratedAfterTraining + " already exists . So it will not be formed again . So it will not be formed again . If you want to re-generate model then re-run with -skipM=No"
else:
rCodeGen.ToCreateDataFrameForTraining(rScript,config,target)
if args.double:
if os.path.isfile(lTempModelName):
rCodeGen.ForLoadingModel(rScript,args,dirName,target,config)
else:
rCodeGen.ForTraining(rScript,args,config,target)
rCodeGen.forPreparingWtVectorForDoubleTraining(rScript,args,target)
rCodeGen.saveTrainingModel(rScript,args,dirName,target,"double")
else:
rCodeGen.ForTraining(rScript,args,config,target)
rCodeGen.saveTrainingModel(rScript,args,dirName,target)
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x "+rProgLocation)
index("/nsefut/") + 8:]
elif 'nseopt' in absPathOfExperimentName:
pathAfterE = absPathOfExperimentName[absPathOfExperimentName.
index("/nseopt/") + 8:]
if "/" in pathAfterE:
mainExperimentName = pathAfterE[:pathAfterE.index("/")]
else:
mainExperimentName = pathAfterE
experimentName = os.path.basename(absPathOfExperimentName)
gTickSize = int(args.tickSize)
gMaxQty = int(args.orderQty)
initialFileName = 'TradeOnTarget-d.' + os.path.basename(os.path.abspath(
args.d)) + attribute.generateExtension(
) + "-tq." + args.orderQty + attribute.generateExtension() + "-dte.7"
g_quantity_adjustment_list_for_sell = {}
g_quantity_adjustment_list_for_buy = {}
gOpenBuyPrice = 0.0
gCloseSellPrice = 0.0
gOpenSellPrice = 0.0
gCloseBuyPrice = 0.0
def functionToReadTargetFileToDictionary(pTargetValuesFile, pTargetValuesDict,
pFileHeader):
lNumberOfLinesInTargetValuesFile = 0
for line in pTargetValuesFile:
if pFileHeader == True:
pFileHeader = False
print("1 lot has 1000 qty's so P/L Long per lot is: " + str(pLPerLotLong),
file=outputFile)
print("P/L for Short trading 10 lots is: " + str(pLPerLotShort * 10),
file=outputFile)
print("P/L for Long trading 10 lots is: " + str(pLPerLotLong * 10),
file=outputFile)
if __name__ == "__main__":
tStart = datetime.now()
dirName = args.pd.replace('/ro/', '/rs/')
checkAllFilesAreExistOrNot = 'false'
lWFDirName = args.pd.replace('/ro/', '/wf/')
predictedBuyValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'buy' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' + \
args.dt + '-targetClass.' + args.targetClass + '-f.' + buyExperimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
predictedSellValuesFileName = lWFDirName+"/p/"+mainExperimentName+"/"+args.a + 'sell' + '-td.' + os.path.basename(os.path.abspath(args.td)) + '-dt.' +\
args.dt + '-targetClass.' + args.targetClass + '-f.' + sellExperimentName + "-wt." + args.wt+ attribute.generateExtension() + ".predictions"
lEntryClList = args.entryCL.split(";")
lExitClList = args.exitCL.split(";")
if len(lEntryClList) != len(lExitClList):
print("Len of entry and exit list does match. Entry List length = ",
len(lEntryClList), " and ExitCL List length = ",
len(lExitClList))
os._exit(-1)
lengthOfList = len(lEntryClList)
lMinOfExitCl = 9999.000
fileNameList = []
for designFile in designFiles:
lExperimentFolderName = os.path.dirname(designFile) + "/"
experimentFolderDirectory.append(lExperimentFolderName)
indexOfFeatures += 1
else:
experimentFolderDirectory.append(args.e)
print "Experiment Folder Lsit ", experimentFolderDirectory
#==========Running the model in serial mode and rest thing in serial , lp or dp mode as given
for lExperimentFolderName in experimentFolderDirectory:
# utility.runCommand(["mRGenForE.py","-e",lExperimentFolderName,"-a",args.a,"-targetClass",args.targetClass,"-skipM",args.skipM,"-td",args.td, "-dt" , \
# args.dt , '-wt' , args.wt,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP ] , args.run , "serial")
scriptName = lExperimentFolderName + "/train" + args.a + "-td." + os.path.basename(
os.path.abspath(args.td)
) + "-dt." + args.dt + "-wt." + args.wt + attribute.generateExtension(
) + ".r"
trainingDataListString = ";".join(trainingDaysDirectory).replace(
"/ro/", "/wf/")
# utility.runCommand([scriptName,"-d",trainingDataListString] , args.run , "serial")
if args.sequence == "dp":
#=========Putting all command in alist-========
lRCodeGenCommandList = []
lPGenRCodeList = []
lTradingCommandList = []
for i in range(len(predictionDaysDirectory)):
predictionDirAfterLastTD = predictionDaysDirectory[i]
lRCodeGenCommandList.append((["pRGenForE.py","-e",args.e,"-s",lExperimentFolderName,"-a",args.a,"-skipP",args.skipP,"-td",args.td , "-pd" , predictionDirAfterLastTD , "-dt" , args.dt ,\
"-targetClass" , args.targetClass , '-wt' , args.wt ,"-iT",args.iT,"-oT",args.oT,"-sP",args.sP]))
scriptName=lExperimentFolderName+"/predict" + args.a + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt +"-pd." +\
def main():
parser = argparse.ArgumentParser(
description=
'Generates predict.r which will use design.model to make predictions. Sample command is pGenForE.py -e ob/e1/'
)
parser.add_argument('-e',
required=True,
help='Directory to find the experiement designs')
parser.add_argument('-a', required=True, help='Algorithm name')
parser.add_argument('-pd', required=True, help='Prediction directory')
parser.add_argument('-td',
required=True,
help="Day on which it was trained")
parser.add_argument('-dt',
required=True,
help="Number of days it was trained")
parser.add_argument('-wt', required=True, help="exp/default")
parser.add_argument(
'-targetClass',
required=True,
help=
"For which model was used ; binomial(target takes only true and false) / multinomial (target values takes more than 2 values)"
)
parser.add_argument(
'-skipP',
required=False,
help=
"yes or no , If you want to regenerate already generated algorithm prediction file then make this value No"
)
parser.add_argument('-s', required=False, help="Experiment sub folders")
parser.add_argument('-iT', required=False, help='Instrument name')
parser.add_argument('-sP',
required=False,
help='Strike price of instrument')
parser.add_argument('-oT', required=False, help='Options Type')
parser.add_argument('-double',
required=False,
help='Double training of in model')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT, args.sP, args.oT)
if args.skipP == None:
args.skipP = "yes"
if args.s == None:
args.s = args.e
print "\nRunning pGen.py to generate the predict script"
print "Using the experiment folder " + args.e
config = ConfigObj(args.s + "/design.ini")
print "The config parameters that I am working with are"
print config
dirName = os.path.dirname(args.s)
if args.a is None:
algo = 'glmnet'
else:
algo = args.a
import pdb
#pdb.set_trace()
if args.double:
rProgName = "predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-pd." + os.path.basename(os.path.abspath(args.pd)) \
+ "-wt." + args.wt+ attribute.generateExtension() + "double.r"
else:
rProgName = "predict" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-pd." + os.path.basename(os.path.abspath(args.pd)) \
+ "-wt." + args.wt+ attribute.generateExtension() + ".r"
rProgLocation = dirName + '/' + rProgName
rScript = open(rProgLocation, 'w')
rScript.write('#!/usr/bin/Rscript \n')
predictDataDirectoryName = args.pd.replace('/ro/', '/wf/')
predictDataDirectoryName = predictDataDirectoryName + "/p/" + os.path.basename(
os.path.dirname(args.e)) + "/"
if not os.path.exists(predictDataDirectoryName):
os.mkdir(predictDataDirectoryName)
if (args.a == 'glmnet'):
rScript.write('require (glmnet) \n')
elif (args.a == 'randomForest'):
rScript.write('require (randomForest) \n')
rCodeGen.ForSetUpChecks(rScript)
lAllFilePresent = True
for target in config['target']:
if args.double:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +"double.predictions"
else:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +".predictions"
if os.path.isfile(predictionFileName) and (args.skipP.lower()
== "yes"):
continue
else:
lAllFilePresent = False
break
if lAllFilePresent == False:
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript, config, target)
rCodeGen.ForSanityChecks(rScript, config, target)
if args.double:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +"double.predictions"
else:
predictionFileName = predictDataDirectoryName + "/" + args.a + target +'-td.' + os.path.basename(os.path.abspath(args.td)) \
+ '-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + os.path.basename(os.path.dirname(args.s)) + \
"-wt." + args.wt+ attribute.generateExtension() +".predictions"
if not os.path.isfile(predictionFileName) or (args.skipP.lower()
== "no"):
if args.double:
lModelGeneratedAfterTraining = args.s + '/' + args.a + target + '-td.' + os.path.basename(
os.path.abspath(args.td)
) + '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt + 'double.model'
rCodeGen.ForPredictions(rScript, config, args, args.s,
target, 2, "double")
else:
lModelGeneratedAfterTraining = args.s + '/' + args.a + target + '-td.' + os.path.basename(
os.path.abspath(args.td)
) + '-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt + '.model'
rCodeGen.ForPredictions(rScript, config, args, args.s,
target)
print lModelGeneratedAfterTraining
else:
print predictionFileName + "Already exists , not generating it again . If you want to generate it again then rerun it with -skipP no "
rScript.close()
print "Finished generating R prediction program: " + rProgLocation
os.system("chmod +x " + rProgLocation)
if "/" in pathAfterE:
mainExperimentName = pathAfterE[:pathAfterE.index("/")]
else:
mainExperimentName = pathAfterE
experimentName = os.path.basename(absPathOfExperimentName)
gTickSize = int(args.tickSize)
gMaxQty = int(args.orderQty)
totalEntryCL = args.entryCL.split(";")
totalExitCL = args.exitCL.split(";")
initialFileName = []
for indexOfCL in range(0,len(totalEntryCL)):
lInitialFileName = args.a + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + '-f.' + experimentName + "-wt." + args.wt+ attribute.generateExtension() + \
'-l.'+totalEntryCL[indexOfCL]+"-"+totalExitCL[indexOfCL] + "-tq." + args.orderQty + "-te.7"
initialFileName.append(lInitialFileName)
g_quantity_adjustment_list_for_sell = {}
g_quantity_adjustment_list_for_buy = {}
def functionToReadPredictionFileToDictionary(pPredictedValuesFile,pPredictedValuesDict,pFileHeader):
lNumberOfLinesInPredictedValuesFile = 0
for line in pPredictedValuesFile:
if pFileHeader == True:
pFileHeader = False
continue
line=line.rstrip('\n')
splitLine = line.split(',')
else:
mainExperimentName = pathAfterE
mainExperimentNameList.append(mainExperimentName)
experimentName = os.path.basename(absPathOfExperimentName)
sys.path.append("./src/")
sys.path.append("./ob/generators/")
config = ConfigObj(experiment + "/design.ini")
featureTargetFilePath = args.pd.replace('ro', 'wf')
for feature in config["features-buy"]:
lName = config["features-buy"][feature].replace('(',
'').replace(')', '')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension(
) + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
for feature in config["features-sell"]:
lName = config["features-sell"][feature].replace('(',
'').replace(')', '')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension(
) + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
dirName = args.pd.replace('/ro/', '/wf/')
def main():
parser = argparse.ArgumentParser(description='Generates train.r. A sample command is mGenForE.py -e ob/e1/ ')
parser.add_argument('-e', required=True,help='Experiement folder to use to find the features and targets')
parser.add_argument('-a', required=True,help='Algorithm name')
parser.add_argument('-s', required=True,help='Location of the folder containing all the sub experiments')
parser.add_argument('-targetClass',required=True,help="binomial(target takes only true and false) / multinomial (target values takes more than 2 values)")
parser.add_argument('-skipM',required=False,help="yes or no , If you want to regenerate already generated algorithm model file then make this value No")
parser.add_argument('-td',required=True,help="Day on which it was trained")
parser.add_argument('-dt',required=True,help="Number of days it was trained")
parser.add_argument('-wt',required=True,help="default/exp , weight type to be given to different days")
parser.add_argument('-iT',required=False,help='Instrument name')
parser.add_argument('-sP',required=False,help='Strike price of instrument')
parser.add_argument('-oT',required=False,help='Options Type')
args = parser.parse_args()
attribute.initializeInstDetails(args.iT,args.sP,args.oT)
if args.skipM == None:
args.skipM = "yes"
print "Using the experiment folder " + args.e
config = ConfigObj(args.e+"/design.ini")
print "The config parameters that I am working with are"
print config
dirName=os.path.dirname(args.e)
algo = rCodeGen.getAlgoName(args)
args.s = args.s + "/"
rProgName = "train" + algo + "-td." + os.path.basename(os.path.abspath(args.td)) + "-dt." + args.dt + "-wt." + args.wt+ attribute.generateExtension() +\
"-For"+os.path.basename(os.path.dirname(args.s))+"SubE.r"
rProgLocation = dirName+'/'+rProgName
rScript = open(rProgLocation,'w')
rScript.write('#!/usr/bin/Rscript \n')
if(algo == 'glmnet'):
rScript.write('require (glmnet) \n')
elif(algo == 'randomForest'):
rScript.write('require (randomForest) \n')
elif(algo == 'mda'):
rScript.write('require (mda) \n')
rCodeGen.ForSetUpChecks(rScript)
rCodeGen.ToReadTargetFile(rScript,config)
rCodeGen.ForWtVectorGeneration(rScript,args.wt.lower())
for target in config['target']:
rCodeGen.ToReadFeatureFiles(rScript,config,target)
rCodeGen.ForSanityChecks(rScript,config,target)
designFiles = utility.list_files(args.s)
for designFile in designFiles:
print "Generating r code for " + designFile
rScript.write('\n\nprint ("Running r code for ' + designFile + '")\n')
config = ConfigObj(designFile)
for target in config['target']:
lModelGeneratedAfterTraining = os.path.dirname(designFile) + '/' + algo + target + '-td.' + os.path.basename(os.path.abspath(args.td)) + \
'-dt.' + args.dt + '-targetClass.' + args.targetClass + "-wt." + args.wt+ attribute.generateExtension() + '.model'
if os.path.isfile(lModelGeneratedAfterTraining)and ( args.skipM.lower() == "yes" ):
print "Model File " + lModelGeneratedAfterTraining + " already exists . So it will not be formed again . If you want to re-generate model then re-run with -skipM=No"
else:
rCodeGen.ToCreateDataFrameForTraining(rScript,config,target)
rCodeGen.ForTraining(rScript,args,config,target)
rCodeGen.saveTrainingModel(rScript,args,os.path.dirname(designFile),target)
rScript.write('rm(list=ls())')
rScript.close()
print "Finished generating R training program: " + rProgLocation
os.system("chmod +x "+rProgLocation)
def ToReadFeatureFiles(rScript,config,targetVariable,pUseWhichArgumentForData=2):
features = config["features-"+targetVariable]
rScript.write('\nprint ("Section3: Read feature files") \n')
if pUseWhichArgumentForData == 4:
rScript.write('lDirectorySet<-strsplit(args[4],";",fixed=TRUE,useBytes=FALSE)\n')
else:
rScript.write('lDirectorySet<-strsplit(args[2],";",fixed=TRUE,useBytes=FALSE)\n')
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
userFriendlyName = userFriendlyName.replace('(','')
userFriendlyName = userFriendlyName.replace(')','')
featureNameWithoutBrackets = features[feature].replace('(','').replace(')','') + attribute.generateExtension()
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/f/'+featureNameWithoutBrackets+'.bin",sep=""))\n')
rScript.write(' '+feature+targetVariable+'<-get("'+userFriendlyName+'")' + skipRowCode + ' \n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else {\n')
rScript.write(' load(paste(file,"/f/'+featureNameWithoutBrackets+'.bin",sep=""))\n')
rScript.write(' temp<-get("'+userFriendlyName+ '")' + skipRowCode + '\n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' '+feature+targetVariable+'<-rbind('+feature+targetVariable+',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading '+ featureNameWithoutBrackets +'.feature' + '") \n')
rScript.write('}\n')
def ToFindCorrelationDatewiseAndPrintingToFile(rScript,config,pTargetVariableKey,pFileName):
features = config["features-"+pTargetVariableKey]
rScript.write('\nprint ("Section6: To Find Correlation For ' +pTargetVariableKey +'") \n')
rScript.write('string_intercept = paste("CorrelationCoeficient Of ","' + pTargetVariableKey + '" , ":- ","\\n",sep="")\n')
rScript.write('cat(string_intercept,file="'+ pFileName + '",sep="",append=TRUE)\n')
for feature in features:
userFriendlyName = features[feature]
userFriendlyName = userFriendlyName.replace('[','')
userFriendlyName = userFriendlyName.replace(']','')
userFriendlyName = userFriendlyName.replace('(','')
userFriendlyName = userFriendlyName.replace(')','')
featureNameWithoutBrackets = features[feature].replace('(','').replace(')','') + attribute.generateExtension()
rScript.write('lFlag=FALSE\n')
rScript.write('for (file in lDirectorySet[[1]]){\n')
rScript.write(' if (!lFlag){\n')
rScript.write(' load(paste(file,"/f/'+featureNameWithoutBrackets+'.bin",sep=""))\n')
rScript.write(' '+feature+pTargetVariableKey+'<-get("'+userFriendlyName+'")' + skipRowCode + ' \n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' lFlag=TRUE\n')
rScript.write(' }\n')
rScript.write(' else {\n')
rScript.write(' load(paste(file,"/f/'+featureNameWithoutBrackets+'.bin",sep=""))\n')
rScript.write(' temp<-get("'+userFriendlyName+ '")' + skipRowCode + '\n')
rScript.write(' rm("' + userFriendlyName + '")\n')
rScript.write(' '+feature+pTargetVariableKey+'<-rbind('+feature+pTargetVariableKey+',temp)\n')
rScript.write(' rm(temp)\n')
rScript.write(' }\n')
rScript.write(' print ("Reading '+ featureNameWithoutBrackets +'.feature' + '") \n')
rScript.write('}\n')
userFriendlyName = features[feature]
rScript.write('tempXY <- sum('+pTargetVariableKey+'[,2] * '+ feature+pTargetVariableKey+'[,2] )\n')
rScript.write('tempY2 <- sum('+pTargetVariableKey+'[,2] ^ 2 )\n')
rScript.write('tempX2 <- sum('+feature+pTargetVariableKey+'[,2] ^ 2 )\n')
rScript.write('tempY <- sum('+pTargetVariableKey+'[,2] )\n')
rScript.write('tempX <- sum('+feature+pTargetVariableKey+'[,2] )\n')
rScript.write('n <- length('+feature+pTargetVariableKey+'[,2] )\n')
rScript.write('string_intercept = paste("'+ userFriendlyName +'_XY" ,"=",toString(tempXY),"\\n"')
rScript.write(',"'+ userFriendlyName +'_Y2" ,"=",toString(tempY2),"\\n"')
rScript.write(',"'+ userFriendlyName +'_X2" ,"=",toString(tempX2),"\\n"')
rScript.write(',"'+ userFriendlyName +'_Y" ,"=",toString(tempY),"\\n"')
rScript.write(',"'+ userFriendlyName +'_X" ,"=",toString(tempX),"\\n"')
rScript.write(',"'+ userFriendlyName +'_n" ,"=",toString(n),"\\n"')
rScript.write(',sep="")\n')
rScript.write('cat(string_intercept,file="'+ pFileName + '",sep="",append=TRUE)\n')
rScript.write('rm('+ feature+pTargetVariableKey + ')\n')
rScript.write('string_intercept = paste("\\n","\\n",sep="")\n')
rScript.write('cat(string_intercept,file="'+ pFileName + '",sep="",append=TRUE)\n')
else:
mainExperimentName = pathAfterE
mainExperimentNameList.append(mainExperimentName)
experimentName = os.path.basename(absPathOfExperimentName)
sys.path.append("./src/")
sys.path.append("./ob/generators/")
config = ConfigObj(experiment+"/design.ini")
featureTargetFilePath = args.pd.replace('ro', 'wf')
for feature in config["features-buy"]:
lName = config["features-buy"][feature].replace('(','').replace(')','')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName+ attribute.generateExtension() + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
for feature in config["features-sell"]:
lName = config["features-sell"][feature].replace('(','').replace(')','')
if lName not in featureNames:
lFeatureFile = featureTargetFilePath + "/f/" + lName + attribute.generateExtension() + ".feature"
featureFP = open(lFeatureFile, "rb")
featureFpList.append(featureFP)
featureNames.append(lName)
dirName = args.pd.replace('/ro/','/wf/')
targetSet = config['target']
# for target in targetSet.keys():
