def xgboost_to_pmml(pipeline, col_names, target_name, pmml_f_name='from_xgboost.pmml',model_name=None,description=None):
"""
Exports xgboost model 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_xgboost.pmml')
model_name : string (optional)
Name of the model
description : string (optional)
Description for the model
Returns
-------
Generates the PMML object and exports it 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,
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 xgboost_to_pmml(pipeline,
col_names,
target_name,
pmml_f_name='from_xgboost.pmml'):
"""
Exports xgboost 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_xgboost.pmml')
Returns
-------
Returns a pmml file
"""
try:
model = pipeline.steps[-1][1]
except:
raise TypeError(
"Exporter expects pipeleine_instance and not an estimator_instance"
)
else:
if isinstance(col_names, np.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)
pmml = pml.PMML(
version=sklToPmml.get_version(),
Header=sklToPmml.get_header(),
MiningBuildTask=sklToPmml.get_mining_buildtask(pipeline),
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 lgb_to_pmml(pipeline,
col_names,
target_name,
pmml_f_name='from_lgbm.pmml'):
"""
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')
Returns
-------
Returns a pmml file
"""
try:
model = pipeline.steps[-1][1]
except:
raise TypeError(
"Exporter expects pipeleine_instance and not an estimator_instance"
)
else:
if isinstance(col_names, np.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)
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)
pmml = pml.PMML(version=sklToPmml.get_version(),
Header=sklToPmml.get_header(),
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)
read_file = open(pmml_f_name, 'r')
lines = read_file.readlines()
lines.insert(
1,
'<!--(Comment generated by ADAPA) PMML processed by ADAPA (Version : 4.3)-->\n'
)
read_file.close()
read_file = open(pmml_f_name, 'w')
read_file.writelines(lines)
read_file.close()