def import_from_sklearn_pipeline(sklearn_pipeline):
#For all pipeline steps, identify equivalent lale wrappers if present,
#if not, call make operator on sklearn classes and create a lale pipeline.
def get_equivalent_lale_op(sklearn_obj):
module_name = "lale.lib.sklearn"
from sklearn.base import clone
from lale.operators import make_operator
class_name = sklearn_obj.__class__.__name__
module = importlib.import_module(module_name)
try:
class_ = getattr(module, class_name)
except AttributeError:
class_ = make_operator(sklearn_obj, name=class_name)
class_ = class_(**sklearn_obj.get_params())
class_._impl._sklearn_model = copy.deepcopy(sklearn_obj)
return class_
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.base import BaseEstimator
from lale.operators import make_pipeline, make_union
if isinstance(sklearn_pipeline, Pipeline):
nested_pipeline_steps = sklearn_pipeline.named_steps.values()
nested_pipeline_lale_objects = [import_from_sklearn_pipeline(nested_pipeline_step) for nested_pipeline_step in nested_pipeline_steps]
lale_op_obj = make_pipeline(*nested_pipeline_lale_objects)
elif isinstance(sklearn_pipeline, FeatureUnion):
transformer_list = sklearn_pipeline.transformer_list
concat_predecessors = [import_from_sklearn_pipeline(transformer[1]) for transformer in transformer_list]
lale_op_obj = make_union(*concat_predecessors)
else:
lale_op_obj = get_equivalent_lale_op(sklearn_pipeline)
return lale_op_obj
def test_pca_nys_lr(self):
from lale.operators import make_union
nys = Nystroem(n_components=15)
pca = PCA(n_components=10)
lr = LogisticRegression(random_state=42)
trainable = make_union(nys, pca) >> lr
digits = sklearn.datasets.load_digits()
trained = trainable.fit(digits.data, digits.target)
predicted = trained.predict(digits.data)
def import_from_sklearn_pipeline(sklearn_pipeline, fitted=True):
#For all pipeline steps, identify equivalent lale wrappers if present,
#if not, call make operator on sklearn classes and create a lale pipeline.
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_
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.base import BaseEstimator
from lale.operators import make_pipeline, make_union
if isinstance(sklearn_pipeline, Pipeline):
nested_pipeline_steps = sklearn_pipeline.named_steps.values()
nested_pipeline_lale_objects = [
import_from_sklearn_pipeline(nested_pipeline_step, fitted=fitted)
for nested_pipeline_step in nested_pipeline_steps
]
lale_op_obj = make_pipeline(*nested_pipeline_lale_objects)
elif isinstance(sklearn_pipeline, FeatureUnion):
transformer_list = sklearn_pipeline.transformer_list
concat_predecessors = [
import_from_sklearn_pipeline(transformer[1], fitted=fitted)
for transformer in transformer_list
]
lale_op_obj = make_union(*concat_predecessors)
else:
lale_op_obj = get_equivalent_lale_op(sklearn_pipeline, fitted=fitted)
return lale_op_obj
def test_concat_with_hyperopt2(self):
from lale.operators import make_pipeline, make_union
from lale.lib.lale import Hyperopt
pca = PCA(n_components=3)
nys = Nystroem(n_components=10)
concat = ConcatFeatures()
lr = LogisticRegression(random_state=42, C=0.1)
trainable = make_pipeline(make_union(pca, nys), lr)
clf = Hyperopt(estimator=trainable, max_evals=2)
from sklearn.datasets import load_iris
iris_data = load_iris()
clf.fit(iris_data.data, iris_data.target)
clf.predict(iris_data.data)
def import_from_sklearn_pipeline(sklearn_pipeline, fitted=True):
#For all pipeline steps, identify equivalent lale wrappers if present,
#if not, call make operator on sklearn classes and create a lale pipeline.
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_
from sklearn.base import BaseEstimator
from sklearn.pipeline import FeatureUnion, Pipeline
from lale.operators import make_pipeline, make_union
if isinstance(sklearn_pipeline, Pipeline):
nested_pipeline_steps = sklearn_pipeline.named_steps.values()
nested_pipeline_lale_objects = [
import_from_sklearn_pipeline(nested_pipeline_step, fitted=fitted)
for nested_pipeline_step in nested_pipeline_steps
]
lale_op_obj = make_pipeline(*nested_pipeline_lale_objects)
elif isinstance(sklearn_pipeline, FeatureUnion):
transformer_list = sklearn_pipeline.transformer_list
concat_predecessors = [
import_from_sklearn_pipeline(transformer[1], fitted=fitted)
for transformer in transformer_list
]
lale_op_obj = make_union(*concat_predecessors)
else:
lale_op_obj = get_equivalent_lale_op(sklearn_pipeline, fitted=fitted)
return lale_op_obj
