def reconstruct(pmml, *args):
mining_model = pmml.get_MiningModel()[0]
targets = mining_model.get_Targets()
model = EnsembleModel()
model.isRescaleRequired(val=True)
if targets:
model.set_target_transformation(targets)
fields, classes = model.get_data_information(pmml)
if args:
fields = args[0]
segmentations = mining_model.get_Segmentation()
model.set_multiple_model_method(segmentations.get_multipleModelMethod())
segments = segmentations.get_Segment()
tree_models = model.get_tree_models(segments)
tree_objs = model.get_tree_objects(tree_models, fields, classes)
model.set_classes(classes)
model.set_tree_objs(tree_objs)
model.n_features_ = len(fields)
extension_value = pmml.get_MiningBuildTask().get_Extension()[0].get_value()
if 'XGBRegressor' in extension_value:
mod = XGBRegressor()
mod.rescaleConstant = model.get_rescale_Constant(targets)
elif 'GradientBoostingRegressor' in extension_value:
mod = GradientBoostingRegressor()
mod.rescaleFactor = model.get_rescale_Factor(targets)
mod.rescaleConstant = model.get_rescale_Constant(targets)
elif 'RandomForestClassifier' in extension_value:
mod = RandomForestClassifier()
elif 'RandomForestRegressor' in extension_value:
mod = RandomForestRegressor()
elif 'LGBMRegressor' in extension_value:
mod = LGBMRegressor()
model.isRescaleRequired(val=False)
elif 'LGBMClassifier' in extension_value:
mod = LGBMClassifier()
elif 'XGBClassifier' in extension_value:
mod = XGBClassifier()
elif 'GradientBoostingClassifier' in extension_value:
mod = GradientBoostingClassifier()
mod.normalizationMethod = model.normalizationMethod
mod.predict = model.predict
mod.multiple_model_method = model.multiple_model_method
mod.trees = model.trees
mod.transformedOutputs = model.transformedOutputs
mod.classes = model.classes
return mod
def reconstruct(pmml, *args):
mining_model = pmml.get_MiningModel()[0]
targets = mining_model.get_Targets()
model = EnsembleModel()
model.isRescaleRequired(val=True)
if targets:
model.set_target_transformation(targets)
fields, classes = model.get_data_information(pmml)
if args:
fields = args[0]
segmentations = mining_model.get_Segmentation()
model.set_multiple_model_method(segmentations.get_multipleModelMethod())
segments = segmentations.get_Segment()
tree_models = model.get_tree_models(segments)
tree_objs = model.get_tree_objects(tree_models, fields, classes)
model.set_classes(classes)
model.set_tree_objs(tree_objs)
model.n_features_ = len(fields)
#extension_value=pmml.get_MiningBuildTask().get_Extension()[0].get_value()
# Vinay's logic
for ext in range(len(pmml.get_MiningBuildTask().get_Extension())):
if ('Sequential' not in pmml.get_MiningBuildTask().get_Extension()
[ext].get_value()) and (
pmml.get_MiningBuildTask().get_Extension()[ext].name
== 'modelObject'):
extension_value = pmml.get_MiningBuildTask().get_Extension(
)[ext].get_value()
# Vinay's logic ends
if 'XGBRegressor' in extension_value:
mod = XGBRegressor()
mod.rescaleConstant = model.get_rescale_Constant(targets)
if 'GradientBoostingRegressor' in extension_value:
mod = GradientBoostingRegressor()
mod.rescaleFactor = model.get_rescale_Factor(targets)
mod.rescaleConstant = model.get_rescale_Constant(targets)
elif 'RandomForestClassifier' in extension_value:
mod = RandomForestClassifier()
elif 'RandomForestRegressor' in extension_value:
mod = RandomForestRegressor()
elif 'ExtraTreesClassifier' in extension_value:
mod = ExtraTreesClassifier()
elif 'ExtraTreesRegressor' in extension_value:
mod = ExtraTreesRegressor()
elif 'LGBMRegressor' in extension_value:
mod = LGBMRegressor()
model.isRescaleRequired(val=False)
elif 'LGBMClassifier' in extension_value:
mod = LGBMClassifier()
elif 'XGBClassifier' in extension_value:
mod = XGBClassifier()
elif 'GradientBoostingClassifier' in extension_value:
mod = GradientBoostingClassifier()
mod.normalizationMethod = model.normalizationMethod
elif 'IsolationForest' in extension_value:
normalizedScore = mining_model.Output.OutputField[
1].Apply.get_Constant()[0].get_valueOf_()
threshold = mining_model.Output.OutputField[3].Apply.get_Constant(
)[0].get_valueOf_()
model.normalizedScore = normalizedScore
model.threshold = threshold
mod = IsolationForest()
if 'IsolationForest' in extension_value:
mod.predict = model.predict_anomaly
else:
mod.predict = model.predict
mod.multiple_model_method = model.multiple_model_method
mod.trees = model.trees
mod.transformedOutputs = model.transformedOutputs
mod.classes = model.classes
return mod
