def get_estimator(self, idx):
"""Extract a single estimator tree from the forest.
:param int idx: The index of the tree to extract.
"""
check_is_fitted(self)
if not self.enable_tree_details:
raise ValueError(
"enable_tree_details must be True prior to training")
return GRFTreeRegressor.from_forest(self, idx=idx)
def estimators_(self):
try:
check_is_fitted(self)
except NotFittedError:
raise AttributeError(
f"{self.__class__.__name__} object has no attribute 'estimators_'"
) from None
if not self.enable_tree_details:
raise ValueError(
"enable_tree_details must be True prior to training")
return [
GRFTreeRegressor.from_forest(self, idx=idx)
for idx in range(self.n_estimators)
]
def test_from_forest(self, boston_X, boston_y):
forest = GRFForestRegressor()
forest.fit(boston_X, boston_y)
tree = GRFTreeRegressor.from_forest(forest=forest, idx=0)
tree.predict(boston_X)