def _nyoka_mining_schema(
self,
mining_schema: models.MiningSchema) -> nyoka_pmml.MiningSchema:
return nyoka_pmml.MiningSchema(MiningField=[
nyoka_pmml.MiningField(name=f.name, usageType=f.usageType.name)
for f in mining_schema.miningFields
])
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 nodes in json format.
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_())
mining_field_for_innner_segments = col_names
m_flds = []
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=MINING_FUNCTION.REGRESSION,
modelName="DecisionTreeModel",
missingValueStrategy="none",
noTrueChildStrategy="returnLastPrediction",
splitCharacteristic=TREE_SPLIT_CHARACTERISTIC.MULTI,
Node=main_node,
MiningSchema=pml.MiningSchema(MiningField=m_flds)))))
return segments_equal_to_estimators
