def nyObjOfModel(self, pmmlObj, singMod):
import nyokaBase.PMML43Ext as ny
if singMod['pmmlModelObject'].__dict__[
'original_tagname_'] == 'MiningModel':
nyokaObj = ny.PMML(MiningBuildTask=pmmlObj.MiningBuildTask,
DataDictionary=pmmlObj.DataDictionary,
MiningModel=[singMod['pmmlModelObject']])
elif singMod['pmmlModelObject'].__dict__[
'original_tagname_'] == 'DeepNetwork':
nyokaObj = ny.PMML(DataDictionary=pmmlObj.DataDictionary,
DeepNetwork=[singMod['pmmlModelObject']])
else:
nyokaObj = None
return nyokaObj
def xgboost_to_pmml(pipeline, col_names, target_name, pmml_f_name='from_xgboost.pmml'):
"""
Exports xgboost pipeline object into pmml
Parameters
----------
pipeline :
Contains an instance of Pipeline with preprocessing and final estimator
col_names : List
Contains list of feature/column names.
target_name : String
Name of the target column.
pmml_f_name : String
Name of the pmml file. (Default='from_xgboost.pmml')
Returns
-------
Returns a pmml file
"""
try:
model = pipeline.steps[-1][1]
except:
raise TypeError("Exporter expects pipeleine_instance and not an estimator_instance")
else:
if isinstance(col_names, np.ndarray):
col_names = col_names.tolist()
ppln_sans_predictor = pipeline.steps[:-1]
trfm_dict_kwargs = dict()
derived_col_names = col_names
categoric_values = tuple()
mining_imp_val = tuple()
if ppln_sans_predictor:
pml_pp = pp.get_preprocess_val(ppln_sans_predictor, col_names, model)
trfm_dict_kwargs['TransformationDictionary'] = pml_pp['trfm_dict']
derived_col_names = pml_pp['derived_col_names']
col_names = pml_pp['preprocessed_col_names']
categoric_values = pml_pp['categorical_feat_values']
mining_imp_val = pml_pp['mining_imp_values']
PMML_kwargs = get_PMML_kwargs(model,
derived_col_names,
col_names,
target_name,
mining_imp_val,
categoric_values)
pmml = pml.PMML(
version=sklToPmml.get_version(),
Header=sklToPmml.get_header(),
MiningBuildTask=sklToPmml.get_mining_buildtask(pipeline),
DataDictionary=sklToPmml.get_data_dictionary(model, col_names, target_name, categoric_values),
**trfm_dict_kwargs,
**PMML_kwargs
)
pmml.export(outfile=open(pmml_f_name, "w"), level=0)
def writePMML(self, model, predictedClass, fileName, dataSet):
try:
from nyokaBase.keras.keras_model_to_pmml import KerasToPmml
pmmlToBack = KerasToPmml(model,
model_name="TrainedModel",
description="Keras Models in PMML",
dataSet=dataSet,
predictedClasses=predictedClass)
except Exception as e:
data_details = self.upDateStatus()
data_details['status'] = 'Training Failed'
data_details[
'errorMessage'] = 'Error while converting Keras to PMML >> ' + str(
e)
data_details['errorTraceback'] = traceback.format_exc()
with open(self.statusFile, 'w') as filetosave:
json.dump(data_details, filetosave)
# sys.exit()
return -1
scriptCode = self.pmmlObj['script']
if scriptCode == []:
scriptCode = None
else:
for sc in scriptCode:
sc.__dict__['valueOf_'] = sc.get_valueOf_().replace(
'<', '<')
pmmlObjNew = pmmlToBack.__dict__
dDict = pmmlObjNew['DataDictionary']
netw = pmmlObjNew['DeepNetwork']
netw = self.updateSectionInfo(netw)
extensionInfoForData = [
ny.Extension(value=self.hdExtDet, anytypeobjs_=[''])
]
hd = ny.Header(copyright="Copyright (c) 2018 Software AG",
Extension=extensionInfoForData,
description="Neural Network Model",
Timestamp=ny.Timestamp(datetime.now()))
with open(fileName, 'w') as filetosave:
jj = ny.PMML(version="4.3Ext",
DeepNetwork=netw,
DataDictionary=dDict,
Header=hd,
script=scriptCode)
jj.export(filetosave, 0)
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