def inputPMML(self):
""""""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(pmmlNaiveBayesModel)[0]
self._existingTests = self._model.getChildrenOfType(pmmlNaiveBayes)
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
seg={}
if (self.__mode=='Update'):
for t in self._existingTests:
for e in t.getChildrenOfType(pmmlSegments)[0].getChildrenOfType(pmmlExplicitSegment):
seg[e.getAttribute('field')]=e.getAttribute('value')
segment=Producer.tupelize(seg)
if segment in self._baseDict.keys():
testdistributions=t.getChildrenOfType(pmmlNaiveBayes)[0].getChildrenOfType(pmmlNaiveBayesInputs)
try:
test=testdistributions[0]
test.makeAttribute("sample","0")
vctable={}
for vc in test.getChildrenOfType(pmmlValueCount):
vctable[vc.getAttribute('value')]=float(vc.getAttribute('count'))
self._baseDict[segment]=[0,vctable,False]
except:
pass
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
# input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(pmmlBaselineModel)[0]
self._existingTests = self._model.getChildrenOfType(pmmlTestDistributions)
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
# It looks like this will only work for explicit segments
seg = {}
if self.__mode == "Update":
for t in self._existingTests:
for e in t.getChildrenOfType(pmmlSegments)[0].getChildrenOfType(pmmlExplicitSegment):
seg[e.getAttribute("field")] = e.getAttribute("value")
segment = Producer.tupelize(seg)
if segment in self._baseDict.keys():
testdistributions = t.getChildrenOfType(pmmlBaseline)[0].getChildrenOfType(pmmlDiscreteDistribution)
try:
test = testdistributions[0]
test.makeAttribute("sample", "0")
vctable = {}
for vc in test.getChildrenOfType(pmmlValueCount):
vctable[vc.getAttribute("value")] = float(vc.getAttribute("count"))
self._baseDict[segment] = [0, vctable, False]
except:
pass
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(self._modelType)[0]
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(self._modelType)[0]
self._localTransformations = self._model.getChildrenOfType(
pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
def inputPMML(self, pmmlModelClass):
if self._timer:
self._timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
try:
reader.parse(str(self.__input))
except:
print str(self.__input)
self._pmml = reader.root
# Specific elements of regression models:
self._model = self._pmml.getChildrenOfType(pmmlModelClass)[0]
# Container of local transformations (if any)
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(pmmlNaiveBayesModel)[0]
#self._existingTests = self._model.getChildrenOfType(pmmlNaiveBayes)
self._inputs = self._model.getChildrenOfType(pmmlBayesInputs)[0]
self._output = self._model.getChildrenOfType(pmmlBayesOutput)[0]
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
#It looks like this will only work for explicit segments
"""
def inputPMML(self, pmmlModelClass):
if self._timer:
self._timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
try:
reader.parse(str(self.__input))
except:
print str(self.__input)
self._pmml = reader.root
# Specific elements of regression models:
self._model = self._pmml.getChildrenOfType(pmmlModelClass)[0]
# Container of local transformations (if any)
self._localTransformations = self._model.getChildrenOfType(
pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(pmmlNaiveBayesModel)[0]
#self._existingTests = self._model.getChildrenOfType(pmmlNaiveBayes)
self._inputs = self._model.getChildrenOfType(pmmlBayesInputs)[0]
self._output = self._model.getChildrenOfType(pmmlBayesOutput)[0]
self._localTransformations = self._model.getChildrenOfType(
pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
#It looks like this will only work for explicit segments
"""
def inputPMML(self):
""""""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(pmmlBaselineModel)[0]
self._existingTests = self._model.getChildrenOfType(
pmmlTestDistributions)
self._localTransformations = self._model.getChildrenOfType(
pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
seg = {}
if (self.__mode == 'Update'):
for t in self._existingTests:
for e in t.getChildrenOfType(pmmlSegments)[
0].getChildrenOfType(pmmlExplicitSegment):
seg[e.getAttribute('field')] = e.getAttribute('value')
segment = Producer.tupelize(seg)
if segment in self._baseDict.keys():
testdistributions = t.getChildrenOfType(pmmlBaseline)[
0].getChildrenOfType(pmmlDiscreteDistribution)
try:
test = testdistributions[0]
test.makeAttribute("sample", "0")
vctable = {}
for vc in test.getChildrenOfType(pmmlValueCount):
vctable[vc.getAttribute('value')] = float(
vc.getAttribute('count'))
self._baseDict[segment] = [0, vctable, False]
except:
pass
def inputPMML(self):
"""This function reads in a pmml file and saves the things we are going to be reusing when we collect stats."""
if self.__timer:
self.__timer.output("Inputting model")
#input the portion of the model that is completed
reader = pmmlReader()
reader.parse(str(self.__input))
self._pmml = reader.root
self._model = self._pmml.getChildrenOfType(self._modelType)[0]
self._localTransformations = self._model.getChildrenOfType(pmmlLocalTransformations)
if len(self._localTransformations) > 0:
self._localTransformations = self._localTransformations[0]
else:
self._localTransformations = None
self.fields = []
for clusteringField in self._model.getChildrenOfType(pmmlClusteringField):
self.fields.append(clusteringField.getAttributes()["field"])
try:
self.numberOfClusters = int(self._model.getAttributes()['numberOfClusters'])
except TypeError:
raise ValueError("The numberOfClusters must be specified in ClusteringModel as an integer.")
self.clusterNames = []
self.clusters = []
for cluster in self._model.getChildrenOfType(pmmlCluster):
try:
self.clusterNames.append(cluster.getAttributes()['name'])
except TypeError:
raise ValueError("Each cluster must have a 'name' attribute.")
self.clusters.append(cluster.getChildrenOfType(pmmlArray)[0].array_values)
if len(self.clusterNames) != self.numberOfClusters:
raise ValueError("Number of Clusters in ClusteringModel (%d) does not match numberOfClusters attribute (%d)." % (len(self.clusterNames), self.numberOfClusters))
