def get_equivalent_lale_op(sklearn_obj, fitted):
module_names = ["lale.lib.sklearn", "lale.lib.autoai_libs"]
from lale.operators import make_operator, TrainedIndividualOp
lale_wrapper_found = False
class_name = sklearn_obj.__class__.__name__
for module_name in module_names:
module = importlib.import_module(module_name)
try:
class_ = getattr(module, class_name)
lale_wrapper_found = True
break
except AttributeError:
continue
else:
class_ = make_operator(sklearn_obj, name=class_name)
if not fitted: #If fitted is False, we do not want to return a Trained operator.
lale_op = class_
else:
lale_op = TrainedIndividualOp(class_._name, class_._impl,
class_._schemas)
class_ = lale_op(**sklearn_obj.get_params())
if lale_wrapper_found:
class_._impl_instance()._wrapped_model = copy.deepcopy(sklearn_obj)
else: # If there is no lale wrapper, there is no _wrapped_model
class_._impl = copy.deepcopy(sklearn_obj)
return class_
def get_equivalent_lale_op(sklearn_obj, fitted):
# Validate that the sklearn_obj is a valid sklearn-compatible object
if sklearn_obj is None or not hasattr(sklearn_obj, "get_params"):
raise ValueError(
"The input pipeline has a step that is not scikit-learn compatible."
)
module_names = ["lale.lib.sklearn", "lale.lib.autoai_libs"]
from lale.operators import TrainedIndividualOp, make_operator
lale_wrapper_found = False
class_name = sklearn_obj.__class__.__name__
for module_name in module_names:
module = importlib.import_module(module_name)
try:
class_ = getattr(module, class_name)
lale_wrapper_found = True
break
except AttributeError:
continue
else:
class_ = make_operator(sklearn_obj, name=class_name)
if (
not fitted
): # If fitted is False, we do not want to return a Trained operator.
lale_op = class_
else:
lale_op = TrainedIndividualOp(class_._name, class_._impl,
class_._schemas)
orig_hyperparams = sklearn_obj.get_params()
higher_order = False
for hp_name, hp_val in orig_hyperparams.items():
higher_order = higher_order or hasattr(hp_val, "get_params")
if higher_order:
hyperparams = {}
for hp_name, hp_val in orig_hyperparams.items():
if hasattr(hp_val, "get_params"):
nested_op = get_equivalent_lale_op(hp_val, fitted)
hyperparams[hp_name] = nested_op
else:
hyperparams[hp_name] = hp_val
else:
hyperparams = orig_hyperparams
class_ = lale_op(**hyperparams)
if lale_wrapper_found:
wrapped_model = copy.deepcopy(sklearn_obj)
class_._impl_instance()._wrapped_model = wrapped_model
else: # If there is no lale wrapper, there is no _wrapped_model
class_._impl = copy.deepcopy(sklearn_obj)
return class_
def get_equivalent_lale_op(sklearn_obj, fitted):
module_names = ["lale.lib.sklearn", "lale.lib.autoai_libs"]
from lale.operators import make_operator, TrainedIndividualOp
lale_wrapper_found = False
class_name = sklearn_obj.__class__.__name__
for module_name in module_names:
module = importlib.import_module(module_name)
try:
class_ = getattr(module, class_name)
lale_wrapper_found = True
break
except AttributeError:
continue
else:
class_ = make_operator(sklearn_obj, name=class_name)
if not fitted: #If fitted is False, we do not want to return a Trained operator.
lale_op = class_
else:
lale_op = TrainedIndividualOp(class_._name, class_._impl,
class_._schemas)
orig_hyperparams = sklearn_obj.get_params()
higher_order = False
for hp_name, hp_val in orig_hyperparams.items():
higher_order = higher_order or hasattr(hp_val, 'get_params')
if higher_order:
hyperparams = {}
for hp_name, hp_val in orig_hyperparams.items():
if hasattr(hp_val, 'get_params'):
nested_op = get_equivalent_lale_op(hp_val, fitted)
hyperparams[hp_name] = nested_op
else:
hyperparams[hp_name] = hp_val
else:
hyperparams = orig_hyperparams
class_ = lale_op(**hyperparams)
if lale_wrapper_found:
wrapped_model = copy.deepcopy(sklearn_obj)
class_._impl_instance()._wrapped_model = wrapped_model
else: # If there is no lale wrapper, there is no _wrapped_model
class_._impl = copy.deepcopy(sklearn_obj)
return class_