def get_segments_for_xgbr(model, derived_col_names, feature_names, target_name, mining_imp_val,categorical_values):
"""
It returns all the Segments element of the model
Parameters
----------
model :
Contains Xgboost model object.
derived_col_names : List
Contains column names after preprocessing.
feature_names : List
Contains list of feature/column names.
target_name : List
Name of the Target column.
mining_imp_val : tuple
Contains the mining_attributes,mining_strategy, mining_impute_value
categoric_values : tuple
Contains Categorical attribute names and its values
Returns
-------
segment :
Get the Segmentation element which contains inner segments.
"""
segments = list()
get_nodes_in_json_format = []
for i in range(model.n_estimators):
get_nodes_in_json_format.append(json.loads(model._Booster.get_dump(dump_format='json')[i]))
segmentation = pml.Segmentation(multipleModelMethod="sum",
Segment=generate_Segments_Equal_To_Estimators(get_nodes_in_json_format, derived_col_names,
feature_names))
return segmentation
def add_segmentation(model,segments_equal_to_estimators,mining_schema_for_1st_segment,out,id):
"""
It returns the First Segments for a binary classifier and returns number of Segments equls to number of values
target class for multiclass classifier
Parameters
----------
model:
Contains Xgboost model object.
segments_equal_to_estimators: List
Contains List Segements equals to the number of the estimators of the model.
mining_schema_for_1st_segment:
Contains Mining Schema for the First Segment
out:
Contains the Output element
id: Integer
Index of the Segements
Returns:
-------
segments_equal_to_estimators:
Returns list of segments equal to number of estimator of the model
"""
segmentation = pml.Segmentation(multipleModelMethod="sum", Segment=segments_equal_to_estimators)
mining_model = pml.MiningModel(functionName='regression', modelName="MiningModel", MiningSchema=mining_schema_for_1st_segment,
Output=out, Segmentation=segmentation)
if model.n_classes_==2:
First_segment = pml.Segment(True_=pml.True_(), id=id, MiningModel=mining_model)
return First_segment
else:
segments_equal_to_class = pml.Segment(True_=pml.True_(), id=id + 1, MiningModel=mining_model)
return segments_equal_to_class
def get_outer_segmentation(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values):
"""
It returns the Segmentation element of the model.
Parameters
----------
model :
Contains Xgboost model object.
derived_col_names : List
Contains column names after preprocessing.
col_names : List
Contains list of feature/column names.
target_name : String
Name of the Target column.
mining_imp_val : tuple
Contains the mining_attributes,mining_strategy, mining_impute_value
categoric_values : tuple
Contains Categorical attribute names and its values
Returns
-------
segmentation :
Get the outer most Segmentation of an xgboost model
"""
if 'XGBRegressor' in str(model.__class__):
segmentation=get_segments(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values)
else:
segmentation = pml.Segmentation(
multipleModelMethod=get_multiple_model_method(model),
Segment=get_segments(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values)
)
return segmentation