def get_output(self):
"""
Generates Output for RetinaNet
Returns
-------
Nyoka's Output object
"""
out_flds = []
out_flds.append(
pml.OutputField(name="predicted_LabelBoxScore",
dataType="string",
feature="predictedValue",
Extension=[
pml.Extension(extender="ADAPA",
name="format",
value="JSON")
]))
return pml.Output(OutputField=out_flds)
def get_segments_for_lgbc(model, derived_col_names, feature_names, target_name,
mining_imp_val, categoric_values, model_name):
"""
It returns all the segments of the LGB classifier.
Parameters
----------
model :
Contains LGB model object.
derived_col_names : List
Contains column names after preprocessing.
feature_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
model_name : string
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,
dataType=DATATYPE.DOUBLE,
feature=RESULT_FEATURE.PREDICTED_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
last_segment = pml.Segment(True_=pml.True_(),
id=2,
RegressionModel=reg_model)
segments.append(First_segment)
segments.append(last_segment)
else:
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)
oField = list()
for index in range(0, model.n_classes_):
inner_segment = []
for in_seg in range(index, len(main_key_value), model.n_classes_):
inner_segment.append(main_key_value[in_seg])
mining_schema_for_1st_segment = xgboostToPmml.mining_Field_For_First_Segment(
feature_names)
outputField = list()
outputField.append(
pml.OutputField(name='lgbValue(' + str(index) + ')',
optype=OPTYPE.CONTINUOUS,
feature=RESULT_FEATURE.PREDICTED_VALUE,
dataType=DATATYPE.FLOAT,
isFinalResult="true"))
out = pml.Output(OutputField=outputField)
oField.append('lgbValue(' + str(index) + ')')
segments_equal_to_estimators = generate_Segments_Equal_To_Estimators(
inner_segment, derived_col_names, feature_names)
segments_equal_to_class = xgboostToPmml.add_segmentation(
model, segments_equal_to_estimators,
mining_schema_for_1st_segment, out, index)
segments.append(segments_equal_to_class)
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.SOFTMAX
last_segment = pml.Segment(True_=pml.True_(),
id=model.n_classes_ + 1,
RegressionModel=reg_model)
segments.append(last_segment)
return segments
