def test_shallow_planned_nested_indiv_operator(self):
from lale.lib.sklearn import BaggingClassifier, DecisionTreeClassifier
clf = BaggingClassifier(base_estimator=DecisionTreeClassifier())
params = clf.get_params(deep=False)
filtered_params = self.remove_lale_params(params)
assert filtered_params["bootstrap"]
def test_deep_planned_nested_indiv_operator(self):
from lale.lib.sklearn import BaggingClassifier, DecisionTreeClassifier
dtc = DecisionTreeClassifier()
clf = BaggingClassifier(base_estimator=dtc)
params = clf.get_params(deep=True)
filtered_params = self.remove_lale_params(params)
# expected = LogisticRegression.get_defaults()
base = filtered_params["base_estimator"]
base_params = self.remove_lale_params(base.get_params(deep=True))
nested_base_params = nest_HPparams("base_estimator", base_params)
self.assertDictEqual(
{
k: v
for k, v in filtered_params.items()
if k.startswith("base_estimator__")
and not k.startswith("base_estimator___lale")
},
nested_base_params,
)
def test_deep_grammar(self):
from lale.grammar import Grammar
from lale.lib.sklearn import BaggingClassifier, DecisionTreeClassifier
from lale.lib.sklearn import KNeighborsClassifier as KNN
from lale.lib.sklearn import LogisticRegression as LR
from lale.lib.sklearn import StandardScaler as Scaler
dtc = DecisionTreeClassifier()
clf = BaggingClassifier(base_estimator=dtc)
params = clf.get_params(deep=True)
filtered_params = self.remove_lale_params(params)
g = Grammar()
g.start = g.estimator
g.estimator = (NoOp | g.transformer) >> g.prim_est
g.transformer = (NoOp | g.transformer) >> g.prim_tfm
g.prim_est = LR | KNN
g.prim_tfm = PCA | Scaler
params = g.get_params(deep=True)
filtered_params = self.remove_lale_params(params)
assert filtered_params["start__name"] == "estimator"
assert filtered_params["prim_est__LogisticRegression__penalty"] == "l2"