def trainMulticlassBaggingModel(DataSet,VegeTypes,varnames,params,multiclassmethod,baggingmetric='kappa',baggingweightindex=1,\
baggingmetricthres=0.7,varlabelweights=[-1],colsamplerate=0.7,train_percent=0.75,runtimes=300,\
bool_autolabel=True,varlabels=[],n_varlabels=5,bool_weight=False,bool_strclass=False,labelHeaderName="",\
bool_save=False,savedirbase=""):
if bool_autolabel:
varlabels = vc.KMeansLabel(DataSet, varnames, n_varlabels=n_varlabels)
selectruntimesvarnames = _stratifiedRandomChoice_column(
varnames, varlabels, varlabelweights, colsamplerate, runtimes)
evalValues = np.zeros(runtimes)
weights = np.zeros(runtimes)
for runtime in range(runtimes):
savedir = savedirbase + os.sep + "runtime_" + str(runtime)
if not os.path.exists(savedir):
os.makedirs(savedir)
RuntimeDataSet=xgbf.trainingDataSet(DataSet,VegeTypes,selectruntimesvarnames[runtime],\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName)
[train_x, test_x, train_y,
test_y] = xgbf.splitTrainTestData(RuntimeDataSet,
train_percent,
bool_stratify=True)
try:
if multiclassmethod == 'softmax':
ModelList=mlc.trainMulticlassSoftmaxModel([train_y,train_x],VegeTypes,varnames,params,runtime=runtime,bool_weight=bool_weight,\
bool_pandas=False,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mlc.testMulticlassSoftmaxModel(ModelList,[test_y,test_x],VegeTypes,varnames,params,runtime=runtime,\
bool_pandas=False,bool_save=bool_save,savedir=savedir)
elif multiclassmethod == 'category':
ModelList=mlc.trainMulticlassCategoryModel([train_y,train_x],VegeTypes,varnames,params,runtime=runtime,bool_weight=bool_weight,\
bool_pandas=False,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mlc.testMulticlassCategoryModel(ModelList,[test_y,test_x],VegeTypes,varnames,params,runtime=runtime,\
bool_pandas=False,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
evalValues[runtime] = xgbf.Evaluate(test_Y, pred_Y, pred_pY,
baggingmetric)
print("Runtime: %d model done. Evaluation Value = %f" %
(runtime, evalValues[runtime]))
except:
print("Model not established!")
evalValues[runtime] = 0.0
weights = _calWeight(evalValues, baggingweightindex, baggingmetricthres)
evalFiledirto = savedirbase + os.sep + "Runtime_Model_Evaluation_Weights.csv"
init.writeArrayListToCSV([evalValues, weights], [baggingmetric, 'weight'],
evalFiledirto)
#Write Each Runtime Model Variables Names
selectvarnamesfiledir = savedirbase + os.sep + "Runtime_Model_Select_Variables.csv"
save = pd.DataFrame({})
for runtime in range(runtimes):
pdtmp = pd.DataFrame({
"SelectVarName_run" + str(runtime):
selectruntimesvarnames[runtime]
})
save = pd.concat([save, pdtmp], axis=1)
save.to_csv(selectvarnamesfiledir, index=False, header=True)
def _trainSingleclassBaggingModel(CPIDs,DataSet,vtname,params,baggingmetric,bool_gpu,n_gpus,n_parallels,selectruntimesvarnames,\
runtime,train_percent,single_thres,bool_balance,bool_strclass,labelHeaderName,bool_save,savedirbase):
#Assign task to worker
print("Training #%d model..." % runtime)
params_parallel = copy.deepcopy(params)
process_pid = os.getpid()
if len(CPIDs) < n_parallels:
CPIDs.append(process_pid)
process_pid_index = CPIDs.index(process_pid)
print("Worker #%d: PID = %d" % (process_pid_index, process_pid))
if bool_gpu:
params_parallel['gpu_id'] = process_pid_index % n_gpus
#Execute model training process
RuntimeDataSet=xgbf.trainingDataSet(DataSet,[vtname],selectruntimesvarnames[runtime],\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_binary=True)
[train_x, test_x, train_y,
test_y] = xgbf.splitTrainTestData(RuntimeDataSet,
train_percent,
bool_stratify=1)
if bool_balance:
if len(train_y.shape) > 1:
ratio = np.float(
np.sum(train_y[:, 0] == 0)) / np.sum(train_y[:, 0] == 1)
else:
ratio = np.float(np.sum(train_y[:] == 0)) / np.sum(train_y[:] == 1)
params_parallel['scale_pos_weight'] = ratio
model = xgbf.TrainModel(train_x, train_y, params_parallel)
savedir = savedirbase + os.sep + "runtime_" + str(runtime)
if not os.path.exists(savedir):
os.makedirs(savedir)
modelName = vtname + '_xgboost_singleclass_run' + str(runtime) + ".model"
modeldir = savedir + os.sep + modelName
model.save_model(modeldir)
[pred_Y, pred_pY] = xgbf.Predict(model,
test_x,
bool_binary=1,
threshold=single_thres)
evalValue = xgbf.Evaluate(test_y, pred_Y, pred_pY, baggingmetric)
print("Runtime: %d model training finished. Evaluation Value = %f\n" %
(runtime, evalValue))
return evalValue
def _trainMulticlassBaggingModel(CPIDs,DataSet,VegeTypes,varnames,params,multiclassmethod,bool_gpu,n_gpus,n_parallels,\
selectruntimesvarnames,runtime,train_percent,baggingmetric,bool_weight,bool_strclass,labelHeaderName,\
bool_save,savedirbase):
#Assign task to worker
print("Training #%d model..." % runtime)
params_parallel = copy.deepcopy(params)
process_pid = os.getpid()
if len(CPIDs) < n_parallels:
CPIDs.append(process_pid)
process_pid_index = CPIDs.index(process_pid)
print("Worker #%d: PID = %d" % (process_pid_index, process_pid))
if bool_gpu:
params_parallel['gpu_id'] = process_pid_index % n_gpus
#Execute model training process
savedir = savedirbase + os.sep + "runtime_" + str(runtime)
if not os.path.exists(savedir):
os.makedirs(savedir)
RuntimeDataSet=xgbf.trainingDataSet(DataSet,VegeTypes,selectruntimesvarnames[runtime],\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName)
[train_x, test_x, train_y,
test_y] = xgbf.splitTrainTestData(RuntimeDataSet,
train_percent,
bool_stratify=1)
if multiclassmethod == 'softmax':
ModelList=mlc.trainMulticlassSoftmaxModel([train_y,train_x],VegeTypes,varnames,params_parallel,runtime=runtime,\
bool_weight=bool_weight,bool_pandas=False,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mlc.testMulticlassSoftmaxModel(ModelList,[test_y,test_x],VegeTypes,varnames,params_parallel,\
runtime=runtime,bool_pandas=False,bool_save=bool_save,savedir=savedir)
elif multiclassmethod == 'category':
ModelList=mlc.trainMulticlassCategoryModel([train_y,train_x],VegeTypes,varnames,params_parallel,runtime=runtime,\
bool_weight=bool_weight,bool_pandas=False,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mlc.testMulticlassCategoryModel(ModelList,[test_y,test_x],VegeTypes,varnames,params_parallel,\
runtime=runtime,bool_pandas=False,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
evalValue = xgbf.Evaluate(test_Y, pred_Y, pred_pY, baggingmetric)
# evalValues[runtime]=xgbf.Evaluate(test_Y,pred_Y,pred_pY,access_method)
print("Runtime: %d model training finished. Evaluation Value = %f\n" %
(runtime, evalValue))
return evalValue
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
elif multiclassmethod == 'category':
ModelList=mlc.trainMulticlassCategoryModel(TrainDataSet,VegeTypes,varnames,params,bool_weight=bool_weight,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mlc.testMulticlassCategoryModel(ModelList,TestDataSet,VegeTypes,varnames,params,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
#%%
#Evaluate results
evalueFolder = dirto
xgbf.mlcEvalAndWriteResult(evalueFolder, pred_Y, pred_pY, test_Y)
#Plot confusion matrix
plotfiledirto = evalueFolder + os.sep + "conf_mat.png"
plot.plot_confusion_matrix(xgbf.Evaluate(test_Y,pred_Y,pred_pY,'confmat'),VegeTypes,title='Confusion Matrix',cmap=None,normalize=False,\
figsize=(8, 6),fontsize=11,labelsize=11,savedir=plotfiledirto)
#%%
#Predict mapping results
print("Predict region...")
nanDefault = -9999
[TiffList, Total] = init.generateVarialeTiffList(variableFolderdir,
varnames, postfix)
[MatX, Driver, GeoTransform, Proj, nrow,
ncol] = ptf.readTiffAsNumpy(TiffList)
multiclassFolderName = "Multiclass_XGBoost_" + multiclassmethod + "_Model"
savedir = root + os.sep + modelFolderName + os.sep + multiclassFolderName
if multiclassmethod == 'softmax':
pred_X = init.fomatMulticlassSoftmaxMatrix(MatX)
pred_pY = mlc.predictMulticlassSoftmaxModelCvted(ModelList,
pred_X,
def trainSingleclassBaggingModel(DataSet,vtname,varnames,params,baggingmetric='auc',baggingweightindex=1,\
baggingmetricthres=0.7,single_thres=0.5,varlabelweights=[-1],colsamplerate=0.7,\
train_percent=0.75,runtimes=300,bool_autolabel=True,varlabels=[],n_varlabels=5,bool_balance=True,\
bool_strclass=False,labelHeaderName="",bool_save=False,savedirbase=""):
ModelList = []
if bool_autolabel:
varlabels = vc.KMeansLabel(DataSet, varnames, n_varlabels=n_varlabels)
selectruntimesvarnames = _stratifiedRandomChoice_column(
varnames, varlabels, varlabelweights, colsamplerate, runtimes)
evalValues = np.zeros(runtimes)
for runtime in range(runtimes):
RuntimeDataSet=xgbf.trainingDataSet(DataSet,[vtname],selectruntimesvarnames[runtime],\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_binary=True)
[train_x, test_x, train_y,
test_y] = xgbf.splitTrainTestData(RuntimeDataSet,
train_percent,
bool_stratify=1)
if bool_balance:
if len(train_y.shape) > 1:
ratio = np.float(
np.sum(train_y[:, 0] == 0)) / np.sum(train_y[:, 0] == 1)
else:
ratio = np.float(
np.sum(train_y[:] == 0)) / np.sum(train_y[:] == 1)
params['scale_pos_weight'] = ratio
model = xgbf.TrainModel(train_x, train_y, params)
if bool_save:
savedir = savedirbase + os.sep + "runtime_" + str(runtime)
if not os.path.exists(savedir):
os.makedirs(savedir)
modelName = vtname + '_xgboost_singleclass_run' + str(
runtime) + ".model"
modeldir = savedir + os.sep + modelName
model.save_model(modeldir)
else:
ModelList.append(model)
[pred_Y, pred_pY] = xgbf.Predict(model,
test_x,
bool_binary=1,
threshold=single_thres)
evalValues[runtime] = xgbf.Evaluate(test_y, pred_Y, pred_pY,
baggingmetric)
print("Runtime: %d model done. Evaluation Value = %f" %
(runtime, evalValues[runtime]))
baggingweights = _calWeight(evalValues, runtimes, baggingweightindex,
baggingmetricthres)
if bool_save:
#Save Weights
evalweightsFileName = vtname + "_Runtime_Evaluation_Weight.csv"
evalweightsFiledirto = savedirbase + os.sep + evalweightsFileName
evalweightsarray = np.zeros([runtimes, 2])
evalweightsarray[:, 0] = evalValues
evalweightsarray[:, 1] = baggingweights
evalweightarrayname = [baggingmetric, 'weight']
init.writeArrayToCSV(evalweightsarray, evalweightarrayname,
evalweightsFiledirto)
#Save Used Parameters
selectvarnamesfiledir = savedirbase + os.sep + vtname + "_Runtime_Model_Select_Variables.csv"
save = pd.DataFrame({})
for runtime in range(runtimes):
pdtmp = pd.DataFrame({
"SelectVarName_run" + str(runtime):
selectruntimesvarnames[runtime]
})
save = pd.concat([save, pdtmp], axis=1)
save.to_csv(selectvarnamesfiledir, index=False, header=True)
return []
else:
return [ModelList, selectruntimesvarnames, baggingweights]
init.getListFromPandas(HierRelationsFiledir,labelHeaderName_L))
#%%
#Produce merged predicted test set
pred_Y = hmap.predictHierUpMapping(baseMapTestResult, VegeTypes1,
VegeTypes2, HierRelations)
test_Y = hmap.predictHierUpMapping(realTestY, VegeTypes1, VegeTypes2,
HierRelations)
#Evaluate
EvalueFolder = dirto
xgbf.mlcEvalAndWriteResult(EvalueFolder, pred_Y, np.zeros_like(pred_Y),
test_Y)
#Plot confusion matrix
plotfiledirto = EvalueFolder + os.sep + "conf_mat.png"
chsize = 5
plot.plot_confusion_matrix(xgbf.Evaluate(test_Y,pred_Y,np.zeros_like(pred_Y),'confmat'),VegeTypes1,title='Confusion Matrix',cmap=None,normalize=False,\
figsize=(8, 6),fontsize=chsize,labelsize=chsize,savedir=plotfiledirto)
#%%
#Produce merged map
print("Predict region...")
nanDefault = -9999
[baseMapLayer, Driver, GeoTransform, Proj, nrow,
ncol] = ptf.readTiffAsNumpy([baseMapLayerFiledir])
baseMapLayer = baseMapLayer[:, :, 0].astype(np.int32)
pred_Y = hmap.predictHierUpMapping(baseMapLayer, VegeTypes1, VegeTypes2,
HierRelations)
#Write mapping results
Filename2 = "Merging_Mapping_result" + postfix
def evalFeature(CPIDs,evaluate_feature,TrainDataSet,ValidDataSet,VegeTypes,feature_names,multiclassmethod,params,evalue_method,\
bool_cv,cv_num,skf_split,bool_gpu,n_gpus,n_parallels,bool_weight,bool_strclass,labelHeaderName,bool_save,savedir):
print("Trying to evalute feature: %s"%evaluate_feature)
params_parallel=copy.deepcopy(params)
process_pid=os.getpid()
if len(CPIDs)<n_parallels:
CPIDs.append(process_pid)
process_pid_index=CPIDs.index(process_pid)
print("Worker #%d: PID = %d"%(process_pid_index,process_pid))
if bool_gpu:
params_parallel['gpu_id']=process_pid_index%n_gpus
if bool_cv==1:
[Y,X]=xgbf.trainingDataSet(TrainDataSet,VegeTypes,feature_names,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName)
if not bool_strclass:
class_labels=init.mergeCategories(Y)
else:
class_labels=Y
pred_Y_cv=np.zeros(len(class_labels)*cv_num,dtype=np.int32)
pred_pY_cv=np.zeros(len(class_labels)*cv_num)
test_Y_cv=np.zeros(len(class_labels)*cv_num,dtype=np.int32)
last_cv_idx=0
current_cv_idx=0
for cv_i in range(cv_num):
skf=StratifiedKFold(n_splits=skf_split,shuffle=True)
cv_j=0
for train, test in skf.split(X,class_labels):
train_x=X[train]
train_y=Y[train]
test_x=X[test]
test_y=Y[test]
if multiclassmethod=='softmax':
ModelList=mtc.trainMulticlassSoftmaxModel([train_y,train_x],VegeTypes,feature_names,params_parallel,bool_weight=bool_weight,bool_pandas=False,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassSoftmaxModel(ModelList,[test_y,test_x],VegeTypes,feature_names,params_parallel,bool_pandas=False,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
elif multiclassmethod=='category':
ModelList=mtc.trainMulticlassCategoryModel([train_y,train_x],VegeTypes,feature_names,params_parallel,bool_weight=bool_weight,bool_pandas=False,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassCategoryModel(ModelList,[test_y,test_x],VegeTypes,feature_names,params_parallel,bool_pandas=False,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
current_cv_idx=len(test_Y)+last_cv_idx
pred_Y_cv[last_cv_idx:current_cv_idx]=pred_Y
# pred_pY_cv[last_cv_idx:current_cv_idx]=pred_pY
test_Y_cv[last_cv_idx:current_cv_idx]=test_Y
last_cv_idx=current_cv_idx
# evalues_runtime[cv_i,cv_j]=xgbf.Evaluate(test_Y,pred_Y,pred_pY,evalue_method)
cv_j=cv_j+1
evalue=xgbf.Evaluate(test_Y_cv,pred_Y_cv,pred_pY_cv,evalue_method)
else:
if multiclassmethod=='softmax':
ModelList=mtc.trainMulticlassSoftmaxModel(TrainDataSet,VegeTypes,feature_names,params_parallel,bool_weight=bool_weight,bool_pandas=True,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassSoftmaxModel(ModelList,ValidDataSet,VegeTypes,feature_names,params_parallel,bool_pandas=True,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
elif multiclassmethod=='category':
ModelList=mtc.trainMulticlassCategoryModel(TrainDataSet,VegeTypes,feature_names,params_parallel,bool_weight=bool_weight,bool_pandas=True,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassCategoryModel(ModelList,ValidDataSet,VegeTypes,feature_names,params_parallel,bool_pandas=True,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
evalue=xgbf.Evaluate(test_Y,pred_Y,pred_pY,evalue_method)
print("Feature: %s partial evalue = %f\n"%(evaluate_feature,evalue))
return evalue
def _estabModelAndPred(TrainDataSet,ValidDataSet,VegeTypes,feature_names,multiclassmethod,params,evalue_method,EvalueFolder,variableFolderdir,postfix,\
bool_predictmap,bool_weight,bool_strclass,labelHeaderName,bool_save,savedir):
num_class=len(VegeTypes)
#Establish Training Model
if multiclassmethod=='softmax':
ModelList=mtc.trainMulticlassSoftmaxModel(TrainDataSet,VegeTypes,feature_names,params,bool_weight=bool_weight,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassSoftmaxModel(ModelList,ValidDataSet,VegeTypes,feature_names,params,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
elif multiclassmethod=='category':
ModelList=mtc.trainMulticlassCategoryModel(TrainDataSet,VegeTypes,feature_names,params,bool_weight=bool_weight,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY,test_Y]=mtc.testMulticlassCategoryModel(ModelList,ValidDataSet,VegeTypes,feature_names,params,\
bool_strclass=bool_strclass,labelHeaderName=labelHeaderName,bool_save=bool_save,savedir=savedir)
else:
print("Invalid Multiclass Method Input!")
#Write Test Results
YArray=np.zeros([len(test_Y),2])
YArray[:,0]=test_Y
YArray[:,1]=pred_Y
YFiledirto=EvalueFolder+os.sep+"Best_Feature_Real_and_Predicted_Results.csv"
init.writeArrayToCSV(YArray,['real','predict'],YFiledirto)
#Evaluate Model and Write Result
evalArray=np.zeros([1,2])
evalArray[0,0]=xgbf.Evaluate(test_Y,pred_Y,pred_pY,'accuracy')
evalArray[0,1]=xgbf.Evaluate(test_Y,pred_Y,pred_pY,'kappa')
evalFiledirto=EvalueFolder+os.sep+"Best_Feature_Model_Evaluation_ValidDataSet.csv"
init.writeArrayToCSV(evalArray,['accuracy','kappa'],evalFiledirto)
#Find XGBoost Feature Scores
featureScoreFiledirto=EvalueFolder+os.sep+"Feature_Scores.csv"
model=ModelList[0]
feature_scores=model.get_fscore()
[feature_names,fscores]=locateFeatureScores(feature_names,feature_scores)
init.writeArrayListToCSV([feature_names,fscores],['VariableName','FeatureScore'],featureScoreFiledirto)
if bool_predictmap:
#Predict Mapping Results
print("Predict region...")
nanDefault=-9999
[TiffList,Total]=init.generateVarialeTiffList(variableFolderdir,feature_names,postfix)
[MatX,Driver,GeoTransform,Proj,nrow,ncol]=ptf.readTiffAsNumpy(TiffList)
BestFeatureProductFolder=EvalueFolder+os.sep+"Best_Features_Mapping_Results"
if multiclassmethod=='softmax':
pred_X=init.fomatMulticlassSoftmaxMatrix(MatX)
pred_pY=mtc.predictMulticlassSoftmaxModelCvted(ModelList,pred_X,params,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY]=init.reshapeMulticlassMatrix(pred_pY,nrow,ncol,num_class,bool_onearray=False)
elif multiclassmethod=='category':
pred_X=init.formatMulticlassCategoryMatrix(MatX,num_class)
pred_pY=mtc.predictMulticlassCategoryModelCvted(ModelList,pred_X,params,bool_save=bool_save,savedir=savedir)
[pred_Y,pred_pY]=init.reshapeMulticlassMatrix(pred_pY,nrow,ncol,num_class,bool_onearray=True)
for i in range(len(VegeTypes)):
vtname=VegeTypes[i]
ProductFolder=BestFeatureProductFolder+os.sep+vtname
if not os.path.exists(ProductFolder):
os.makedirs(ProductFolder)
Filename1=vtname+"_xgboost_"+multiclassmethod+postfix
ProductFiledirto1=ProductFolder+os.sep+Filename1
ptf.writeNumpyToTiff(pred_pY[:,:,i],Driver,GeoTransform,Proj,nrow,ncol,nanDefault,ProductFiledirto1,datatype='Float32')
Filename2="VegeMap_XGBoost_multiclass_"+multiclassmethod+postfix
ProductFolder=BestFeatureProductFolder
ProductFiledirto2=ProductFolder+os.sep+Filename2
ptf.writeNumpyToTiff(pred_Y,Driver,GeoTransform,Proj,nrow,ncol,nanDefault,ProductFiledirto2,datatype='Int16')
return fscores