def writePmml(pmmlObj, filepath, lockForPMML):
try:
lockForPMML.acquire()
pmmlObj = removeExtraNewLinesFromWeights(pmmlObj)
scrptVal2 = []
scrptVal = pmmlObj.script
if len(scrptVal) > 0:
for num, sc in enumerate(scrptVal):
urlOfScript = sc.class_
useFor = sc.for_
code = None
scripCode = sc.get_valueOf_()
code = scripCode.lstrip('\n')
lines = []
code = scripCode.lstrip('\n')
leading_spaces = len(code) - len(code.lstrip(' '))
for line in code.split('\n'):
lines.append(line[leading_spaces:])
code = '\n'.join(lines)
scriptCode = code.replace('<', '<')
# print (scriptCode)
scrp = pml.script(content=scriptCode,
class_=urlOfScript,
for_=useFor)
scrptVal2.append(scrp)
pmmlObj.script = scrptVal2
# print ('Code Step 10.1')
pmmlObj.export(open(filepath, 'w'), 0)
# print('>>>>>>>>>>>, PMML written')
except Exception as e:
print('>>>>>>>>>>>> ', str(e))
finally:
lockForPMML.release()
def writePmml(pmmlObj, filepath, lockForPMML):
_deepNetworkObj = pmmlObj.DeepNetwork[0]
_deepNetworkObj.modelName = 'model1'
_deepNetworkObj.taskType = "trainAndscore"
pmmlObj.DeepNetwork[0] = _deepNetworkObj
# print ('came to write')
try:
lockForPMML.acquire()
pmmlObj = removeExtraNewLinesFromWeights(pmmlObj)
scrptVal2 = []
scrptVal = pmmlObj.script
if len(scrptVal) > 0:
for num, sc in enumerate(scrptVal):
scriptPurpose = sc.scriptPurpose
modelVal = sc.for_
classVal = sc.class_
filePathUrl = sc.filePath
scriptOutput = sc.scriptOutput
code = None
scripCode = sc.get_valueOf_()
code = scripCode.lstrip('\n')
lines = []
code = scripCode.lstrip('\n')
leading_spaces = len(code) - len(code.lstrip(' '))
for line in code.split('\n'):
lines.append(line[leading_spaces:])
code = '\n'.join(lines)
scriptCode = code.replace('<', '<')
# scrp=pml.script(content=scriptCode,for_=modelVal,class_=taskTypeVal,scriptPurpose=scriptPurpose,scriptOutput=scriptOutput,filePath=filePathUrl)
scrp = pml.script(content=scriptCode,
for_=modelVal,
class_=classVal,
scriptPurpose=scriptPurpose,
scriptOutput=scriptOutput,
filePath=filePathUrl)
scrptVal2.append(scrp)
pmmlObj.script = scrptVal2
# print ('Code Step 10.1')
pmmlObj.export(open(filepath, 'w'), 0)
# print('>>>>>>>>>>>, PMML written')
except Exception as e:
print('>>>>>>>>>>>> ', str(e))
finally:
lockForPMML.release()
def addLayerToPMML(_positionOfLayer, toUpdateLayer, processedOutput,
pmmlObject):
def checkIfFlushRequired(_oldPosition, _newPositon, _pmmlLayer,
_networkLayers):
if _oldPosition == _newPositon:
if (_pmmlLayer.LayerParameters.inputDimension
== _networkLayers[_oldPosition].LayerParameters.
inputDimension) & (
_pmmlLayer.LayerParameters.outputDimension
== _networkLayers[_oldPosition].
LayerParameters.outputDimension):
return False
else:
return True
else:
return True
if processedOutput['itemType'] == 'LAYER':
_inputForPMML = nyokaPMMLUtilities.convertToStandardJson(
processedOutput)
# print ('',_inputForPMML)
_pmmlOfLayer = nyokaPMMLUtilities.addLayer(_inputForPMML)
# print('>>>>>>>',_pmmlOfLayer.__dict__)
_deepNetworkObj = pmmlObject.DeepNetwork[0]
# print ('step 1')
_NetworkLayersObject = _deepNetworkObj.NetworkLayer
# print ('step 2')
_idsOfNetworklayer = [i.layerId for i in _NetworkLayersObject]
flushMemory = False
if toUpdateLayer == True:
_oldPositionOFLayer = _idsOfNetworklayer.index(
processedOutput['layerId'])
# print('>>>> layer id is ',processedOutput['layerId'])
# print('>>>> old position to delete',_oldPositionOFLayer)
# print('>>>> new position ',_positionOfLayer)
# print ('_idsOfNetworklayer',_idsOfNetworklayer)
flushMemory = checkIfFlushRequired(_oldPositionOFLayer,
_positionOfLayer,
_pmmlOfLayer,
_NetworkLayersObject)
# print('flushmemory is ',flushMemory)
if flushMemory == False:
_NetworkLayersObject[
_oldPositionOFLayer].Extension = _pmmlOfLayer.Extension
_pmmlOfLayer = _NetworkLayersObject[
_oldPositionOFLayer]
del _NetworkLayersObject[_oldPositionOFLayer]
_NetworkLayersObject.insert(_positionOfLayer, _pmmlOfLayer)
# print ('step 3')
if flushMemory == True:
_NetworkLayersObject = resetNetworkLayer(
_NetworkLayersObject, _positionOfLayer)
# print ('step 6')
_NetworkLayersObject = reorderIdsOfPmml(_NetworkLayersObject)
_deepNetworkObj.NetworkLayer = _NetworkLayersObject
_deepNetworkObj.numberOfLayers = len(_NetworkLayersObject)
_deepNetworkObj.modelName = 'model1'
_deepNetworkObj.taskType = "trainAndscore"
pmmlObject.DeepNetwork[0] = _deepNetworkObj
elif processedOutput['itemType'] == 'DATA':
# print ("DATA layer came",processedOutput['filePath'])
try:
dataUrl = processedOutput['filePath']
# if processedOutput['for']:
# dataTagValues=pml.Data(filePath=dataVal,for_=processedOutput['for'])
# else:
dataTagValues = pml.Data(filePath=dataUrl, for_='model1')
pmmlObject.Data = [dataTagValues]
# print ('Data Step 3')
# pmmlObject.export(sys.stdout,0)
except:
pass
elif processedOutput['itemType'] == 'CODE':
print("CODE layer came")
# print ('processedOutput',processedOutput)
try:
scrptVal = pmmlObject.script
urlOfScript = processedOutput['url']
filePathUrl = processedOutput['filePath']
scriptFile = open(processedOutput['filePath'], 'r')
scriptCode = scriptFile.read()
scriptCode = scriptCode.replace('<', '<')
# print (scriptCode)
modelVal = 'model1'
taskTypeVal = processedOutput['taskType']
scriptPurpose = processedOutput['scriptPurpose']
scriptOutput = processedOutput['scriptOutput']
scrp = pml.script(content=scriptCode,
for_=modelVal,
class_=taskTypeVal,
scriptPurpose=scriptPurpose,
scriptOutput=scriptOutput,
filePath=filePathUrl)
scrp.export(sys.stdout, 0)
scrptVal.append(scrp)
pmmlObject.script = scrptVal
# print ('Code Step 10')
# pmmlObject.export(sys.stdout,0)
except:
pass
return (pmmlObject)
def addLayerToPMML(_positionOfLayer, toUpdateLayer, processedOutput,
pmmlObject):
def checkIfFlushRequired(_oldPosition, _newPositon, _pmmlLayer,
_networkLayers):
if _oldPosition == _newPositon:
if (_pmmlLayer.LayerParameters.inputDimension
== _networkLayers[_oldPosition].LayerParameters.
inputDimension) & (
_pmmlLayer.LayerParameters.outputDimension
== _networkLayers[_oldPosition].
LayerParameters.outputDimension):
return False
else:
return True
else:
return True
if processedOutput['itemType'] == 'LAYER':
_inputForPMML = nyokaPMMLUtilities.convertToStandardJson(
processedOutput)
# print ('',_inputForPMML)
_pmmlOfLayer = nyokaPMMLUtilities.addLayer(_inputForPMML)
# print('>>>>>>>',_pmmlOfLayer.__dict__)
_deepNetworkObj = pmmlObject.DeepNetwork[0]
# print ('step 1')
_NetworkLayersObject = _deepNetworkObj.NetworkLayer
# print ('step 2')
_idsOfNetworklayer = [i.layerId for i in _NetworkLayersObject]
flushMemory = False
if toUpdateLayer == True:
_oldPositionOFLayer = _idsOfNetworklayer.index(
processedOutput['layerId'])
# print('>>>> layer id is ',processedOutput['layerId'])
# print('>>>> old position to delete',_oldPositionOFLayer)
# print('>>>> new position ',_positionOfLayer)
# print ('_idsOfNetworklayer',_idsOfNetworklayer)
flushMemory = checkIfFlushRequired(_oldPositionOFLayer,
_positionOfLayer,
_pmmlOfLayer,
_NetworkLayersObject)
# print('flushmemory is ',flushMemory)
if flushMemory == False:
_NetworkLayersObject[
_oldPositionOFLayer].Extension = _pmmlOfLayer.Extension
_pmmlOfLayer = _NetworkLayersObject[
_oldPositionOFLayer]
del _NetworkLayersObject[_oldPositionOFLayer]
_NetworkLayersObject.insert(_positionOfLayer, _pmmlOfLayer)
# print ('step 3')
if flushMemory == True:
_NetworkLayersObject = resetNetworkLayer(
_NetworkLayersObject, _positionOfLayer)
# print ('step 6')
_NetworkLayersObject = reorderIdsOfPmml(_NetworkLayersObject)
_deepNetworkObj.NetworkLayer = _NetworkLayersObject
_deepNetworkObj.numberOfLayers = len(_NetworkLayersObject)
pmmlObject.DeepNetwork[0] = _deepNetworkObj
elif processedOutput['itemType'] == 'DATA':
# print ("DATA layer came")
extensionInfoForData = [
pml.Extension(value=[], anytypeobjs_=[''])
]
dataVal = {}
try:
dataVal['dataUrl'] = processedOutput['filePath']
extensionInfoForData = [
pml.Extension(value=dataVal, anytypeobjs_=[''])
]
pmmlObject.Header.Extension = extensionInfoForData
# print ('Data Step 3')
# pmmlObject.export(sys.stdout,0)
except:
pass
elif processedOutput['itemType'] == 'CODE':
# print ("CODE layer came")
try:
scrptVal = pmmlObject.script
urlOfScript = processedOutput['url']
scriptFile = open(processedOutput['filePath'], 'r')
scriptCode = scriptFile.read()
scriptCode = scriptCode.replace('<', '<')
# print (scriptCode)
useFor = processedOutput['useFor']
scrp = pml.script(content=scriptCode,
class_=urlOfScript,
for_=useFor)
# scrp.export(sys.stdout,0)
scrptVal.append(scrp)
pmmlObject.script = scrptVal
# print ('Code Step 10')
# pmmlObject.export(sys.stdout,0)
except:
pass
return (pmmlObject)
def getPmml(self, architecture):
fName = 'classification'
lenOfArch = len(architecture)
mName = 'Keras Model'
netWorkInfo = []
scriptVal = []
extensionInfoForData = [pml.Extension(value=[], anytypeobjs_=[''])]
dataVal = {}
for counta, j in enumerate(architecture):
if counta == 0:
someConectionId = 'na'
else:
someConectionId = tempConId
if j['itemType'] in ['CODE']:
# print ('##################',j)
scriptFile = open(j['filePath'], 'r')
scriptCode = scriptFile.read()
scriptCode = scriptCode.replace('<', '<')
scriptInfo = {}
scrptVal = []
# dataVal['scriptUrl']=j['url']
useFor = j['useFor']
extensionInfoForScript = [
pml.Extension(value=scrptVal, anytypeobjs_=[''])
]
scrp = pml.script(content=scriptCode,
Extension=extensionInfoForScript,
for_=useFor)
scriptVal.append(scrp)
tempConId = None
elif j['itemType'] in ['DATA']:
try:
dataVal['dataUrl'] = j['filePath']
extensionInfoForData = [
pml.Extension(value=dataVal, anytypeobjs_=[''])
]
except:
pass
tempConId = None
elif j['itemType'] == 'FOLDING':
# print (j)
for k in j['children']:
tempdata7 = self.convertToStandardJson(k)
tempdata7['connectionLayerId'] = someConectionId
tempConId = tempdata7['layerId']
pp = addLayer(tempdata7)
netWorkInfo.append(pp)
someConectionId = tempConId
else:
# print ('Start of tamasha$$$$$$$$$$',j)
tempdata7 = self.convertToStandardJson(j)
tempdata7['connectionLayerId'] = someConectionId
tempConId = tempdata7['layerId']
# print ('pakda', tempdata7)
pp = self.addLayer(tempdata7)
netWorkInfo.append(pp)
kk = pml.DeepNetwork(modelName=mName,
functionName=fName,
NetworkLayer=netWorkInfo,
numberOfLayers=lenOfArch)
tt = pml.Timestamp(datetime.now())
hd = pml.Header(copyright="Copyright (c) 2018 Software AG",
Extension=extensionInfoForData,
description="Neural Network Model",
Timestamp=pml.Timestamp(datetime.now()))
jj = pml.PMML(version="4.3Ext",
script=scriptVal,
Header=hd,
DeepNetwork=[kk])
return jj