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Name of the model
Returns
-------
regrs_models :
Returns all the segments of the LGB model.
"""
segments = list()
if model.n_classes_ == 2:
main_key_value = []
lgb_dump = model.booster_.dump_model()
for i in range(len(lgb_dump['tree_info'])):
tree = lgb_dump['tree_info'][i]['tree_structure']
main_key_value.append(tree)
mining_schema_for_1st_segment = xgboostToPmml.mining_Field_For_First_Segment(feature_names)
outputField = list()
outputField.append(pml.OutputField(name="lgbValue", optype=OPTYPE.CONTINUOUS.value, dataType=DATATYPE.DOUBLE.value,
feature=RESULT_FEATURE.PREDICTED_VALUE.value, isFinalResult="false"))
out = pml.Output(OutputField=outputField)
oField=list()
oField.append("lgbValue")
segments_equal_to_estimators = generate_Segments_Equal_To_Estimators(main_key_value, derived_col_names,
feature_names)
First_segment = xgboostToPmml.add_segmentation(model,segments_equal_to_estimators, mining_schema_for_1st_segment, out, 1)
reg_model = sklToPmml.get_regrs_models(model, oField, oField, target_name, mining_imp_val, categoric_values,model_name)[0]
reg_model.normalizationMethod = REGRESSION_NORMALIZATION_METHOD.LOGISTIC.value
last_segment = pml.Segment(True_=pml.True_(), id=2,
RegressionModel=reg_model)
segments.append(First_segment)
segments.append(last_segment)