from __future__ import absolute_import
import sys, os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
sys.path.append(BASE_DIR)
import PMML44 as pml
import skl_to_pmml as sklToPmml
import xgboost_to_pmml as xgboostToPmml
import json
import pre_process as pp
from datetime import datetime
from enums import *
def lgb_to_pmml(pipeline, col_names, target_name, pmml_f_name='from_lgbm.pmml',model_name=None, description=None):
"""
Exports LGBM pipeline object into pmml
Parameters
----------
pipeline :
Contains an instance of Pipeline with preprocessing and final estimator
col_names : List
Contains list of feature/column names.
target_name : String
Name of the target column.
pmml_f_name : String
Name of the pmml file. (Default='from_lgbm.pmml')
model_name : string (optional)
Name of the model
description : string (optional)
Description of the model
Returns
-------
Exports the generated PMML object to `pmml_f_name`
"""
try:
model = pipeline.steps[-1][1]
except:
raise TypeError("Exporter expects pipeleine_instance and not an estimator_instance")
else:
if col_names.__class__.__name__ == "ndarray":
col_names = col_names.tolist()
ppln_sans_predictor = pipeline.steps[:-1]
trfm_dict_kwargs = dict()
derived_col_names = col_names
categoric_values = tuple()
mining_imp_val = tuple()
if ppln_sans_predictor:
pml_pp = pp.get_preprocess_val(ppln_sans_predictor, col_names, model)
trfm_dict_kwargs['TransformationDictionary'] = pml_pp['trfm_dict']
derived_col_names = pml_pp['derived_col_names']
col_names = pml_pp['preprocessed_col_names']
categoric_values = pml_pp['categorical_feat_values']
mining_imp_val = pml_pp['mining_imp_values']
PMML_kwargs = get_PMML_kwargs(model,
derived_col_names,
col_names,
target_name,
mining_imp_val,
categoric_values,
model_name)
pmml = pml.PMML(
version=PMML_SCHEMA.VERSION.value,
Header=sklToPmml.get_header(description),
DataDictionary=sklToPmml.get_data_dictionary(model, col_names, target_name, categoric_values),
**trfm_dict_kwargs,
**PMML_kwargs
)
pmml.export(outfile=open(pmml_f_name, "w"), level=0)
def get_PMML_kwargs(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name):
"""
It returns all the pmml elements.
Parameters
----------
model :
Contains LGB 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
model_name : string
Name of the model
Returns
-------
algo_kwargs : Dictionary
Get the PMML model argument based on LGB model object
"""
algo_kwargs = {'MiningModel': get_ensemble_models(model,
derived_col_names,
col_names,
target_name,
mining_imp_val,
categoric_values,
model_name)}
return algo_kwargs
def get_ensemble_models(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name):
"""
It returns the Mining Model element of the model
Parameters
----------
model :
Contains LGB 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
model_name : string
Name of the model
Returns
-------
Returns the MiningModel for the given LGB model
"""
model_kwargs = sklToPmml.get_model_kwargs(model, col_names, target_name, mining_imp_val,categoric_values)
mining_models = list()
mining_models.append(pml.MiningModel(
modelName=model_name if model_name else "LightGBModel",
Segmentation=get_outer_segmentation(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name),
**model_kwargs
))
return mining_models
def get_outer_segmentation(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name):
"""
It returns the Segmentation element of the model.
Parameters
----------
model :
Contains LGB 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
model_name : string
Name of the model
Returns
-------
segmentation :
Get the outer most Segmentation of an LGB model
"""
if 'LGBMRegressor' in str(model.__class__):
segmentation=get_segments(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name)
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,model_name)
)
return segmentation
def get_segments(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name):
"""
It returns the Segment element of the model.
Parameters
----------
model :
Contains LGB 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
model_name : string
Name of the model
Returns
-------
segment :
Get the Segments for the Segmentation element.
"""
segments = None
if 'LGBMClassifier' in str(model.__class__):
segments=get_segments_for_lgbc(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name)
elif 'LGBMRegressor' in str(model.__class__):
segments=get_segments_for_lgbr(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values)
return segments
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.value,
modelName="DecisionTreeModel",
missingValueStrategy="none",
noTrueChildStrategy="returnLastPrediction",
splitCharacteristic=TREE_SPLIT_CHARACTERISTIC.MULTI.value,
Node=main_node,
MiningSchema=pml.MiningSchema(
MiningField=m_flds)))))
return segments_equal_to_estimators
def get_segments_for_lgbr(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 LGB 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()
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)
segmentation = pml.Segmentation(multipleModelMethod=MULTIPLE_MODEL_METHOD.SUM.value,
Segment=generate_Segments_Equal_To_Estimators(main_key_value, derived_col_names,
feature_names))
return segmentation
def create_node(obj, main_node,derived_col_names):
"""
It creates nodes for the internal Decision Trees.
Parameters
----------
obj : Json
Contains nodes in json format.
main_node :
Contains node build with Nyoka class.
derived_col_names : List
Contains column names after preprocessing.
"""
def create_left_node(obj,derived_col_names):
nd = pml.Node()
nd.set_SimplePredicate(
pml.SimplePredicate(field=xgboostToPmml.replace_name_with_derivedColumnNames(derived_col_names[int(obj['split_feature'])],\
derived_col_names), operator=SIMPLE_PREDICATE_OPERATOR.LESS_OR_EQUAL.value, value="{:.16f}".format(obj['threshold'])))
create_node(obj['left_child'], nd, derived_col_names)
return nd
def create_right_node(obj,derived_col_names):
nd = pml.Node()
nd.set_SimplePredicate(
pml.SimplePredicate(field=xgboostToPmml.replace_name_with_derivedColumnNames(derived_col_names[int(obj['split_feature'])],\
derived_col_names), operator=SIMPLE_PREDICATE_OPERATOR.GREATER_THAN.value, value="{:.16f}".format(obj['threshold'])))
create_node(obj['right_child'], nd, derived_col_names)
return nd
if 'leaf_index' in obj:
main_node.set_score(obj['leaf_value'])
else:
main_node.add_Node(create_left_node(obj,derived_col_names))
main_node.add_Node(create_right_node(obj,derived_col_names))
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.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)
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.value,
feature=RESULT_FEATURE.PREDICTED_VALUE.value, dataType=DATATYPE.FLOAT.value, 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.value
last_segment = pml.Segment(True_=pml.True_(), id=model.n_classes_ + 1,
RegressionModel=reg_model)
segments.append(last_segment)
return segments
def get_multiple_model_method(model):
"""
It returns the type of multiple model method for MiningModels.
Parameters
----------
model :
Contains LGB model object
Returns
-------
The multiple model method for a MiningModel.
"""
if 'LGBMClassifier' in str(model.__class__):
return MULTIPLE_MODEL_METHOD.MODEL_CHAIN.value
else:
return MULTIPLE_MODEL_METHOD.SUM.value