def lightgbm_parser(scope, model, inputs, custom_parsers=None):
if hasattr(model, "fit"):
raise TypeError( # pragma: no cover
"This converter does not apply on type '{}'."
"".format(type(model)))
if len(inputs) == 1:
wrapped = WrappedBooster(model)
objective = wrapped.get_objective()
if objective.startswith('binary'):
wrapped = WrappedLightGbmBoosterClassifier(wrapped)
return _parse_sklearn_classifier(
scope, wrapped, inputs, custom_parsers=custom_parsers)
if objective.startswith('multiclass'):
wrapped = WrappedLightGbmBoosterClassifier(wrapped)
return _parse_sklearn_classifier(
scope, wrapped, inputs, custom_parsers=custom_parsers)
if objective.startswith('regression'): # pragma: no cover
return _parse_sklearn_simple_model(
scope, wrapped, inputs, custom_parsers=custom_parsers)
raise NotImplementedError( # pragma: no cover
"Objective '{}' is not implemented yet.".format(objective))
# Multiple columns
this_operator = scope.declare_local_operator('LightGBMConcat')
this_operator.raw_operator = model
this_operator.inputs = inputs
var = scope.declare_local_variable(
'Xlgbm', inputs[0].type.__class__([None, None]))
this_operator.outputs.append(var)
return lightgbm_parser(scope, model, this_operator.outputs,
custom_parsers=custom_parsers)
def custom_parser(scope, model, inputs, custom_parsers=None):
if custom_parsers is not None and model in custom_parsers:
return custom_parsers[model](
scope, model, inputs, custom_parsers=custom_parsers)
if not all(isinstance(i, (numbers.Real, bool, np.bool_))
for i in model.classes_):
raise NotImplementedError(
"Current converter does not support string labels.")
return _parse_sklearn_classifier(scope, model, inputs)
def _custom_parser_xgboost(scope, model, inputs, custom_parsers=None):
"""
Custom parser for *XGBClassifier* and *LGBMClassifier*.
"""
if custom_parsers is not None and model in custom_parsers:
return custom_parsers[model](
scope, model, inputs, custom_parsers=custom_parsers)
if not all(isinstance(i, (numbers.Real, bool, numpy.bool_))
for i in model.classes_):
raise NotImplementedError( # pragma: no cover
"Current converter does not support string labels.")
try:
from skl2onnx._parse import _parse_sklearn_classifier
except ImportError as e:
import skl2onnx
raise ImportError(
"Hidden API has changed in module 'skl2onnx=={}', "
"installation path is '{}'.".format(
skl2onnx.__version__, skl2onnx.__file__)) from e
return _parse_sklearn_classifier(scope, model, inputs)
