def generate_Segments_Equal_To_Estimators(val, derived_col_names, col_names):
"""
It returns number of Segments equal to the estimator of the model.
Parameters
----------
val: List
Contains a list of well structured node for binary classification/inner segments for multi-class classification
derived_col_names: List
Contains column names after preprocessing.
col_names: List
Contains list of feature/column names.
Returns:
-------
segments_equal_to_estimators:
Returns list of segments equal to number of estimator of the model
"""
segments_equal_to_estimators = []
for i in range(len(val)):
main_node = pml.Node(True_=pml.True_())
m_flds = []
mining_field_for_innner_segments = col_names
create_node(val[i], main_node, derived_col_names)
for name in mining_field_for_innner_segments:
m_flds.append(pml.MiningField(name=name))
segments_equal_to_estimators.append((pml.Segment(id=i + 1, True_=pml.True_(),
TreeModel=pml.TreeModel(functionName="regression",
modelName="DecisionTreeModel",
missingValueStrategy="none",
noTrueChildStrategy="returnLastPrediction",
splitCharacteristic="multiSplit",
Node=main_node,
MiningSchema=pml.MiningSchema(
MiningField=m_flds)))))
return segments_equal_to_estimators
def create_right_node(obj,derived_col_names):
nd = pml.Node()
nd.set_SimplePredicate(
pml.SimplePredicate(field=replace_name_with_derivedColumnNames(obj['split'], derived_col_names), operator='greaterOrEqual', value=obj['split_condition']))
create_node(obj['children'][1], nd, derived_col_names)
return nd