def get_trees(self):
"""
Get the decision trees.
:return: A list of decision trees.
:rtype: `[Tree]`
"""
import json
from art.metrics.verification_decisions_trees import Box, Tree
booster_dump = self._model.get_booster().get_dump(dump_format="json")
trees = list()
for i_tree, tree_dump in enumerate(booster_dump):
box = Box()
if self._model.n_classes_ == 2:
class_label = -1
else:
class_label = i_tree % self._model.n_classes_
tree_json = json.loads(tree_dump)
trees.append(
Tree(class_id=class_label,
leaf_nodes=self._get_leaf_nodes(tree_json, i_tree,
class_label, box)))
return trees
def get_trees(self):
"""
Get the decision trees.
:return: A list of decision trees.
:rtype: `[Tree]`
"""
from art.metrics.verification_decisions_trees import Box, Tree
trees = list()
for i_tree, decision_tree_model in enumerate(self._model.estimators_):
box = Box()
# if num_classes == 2:
# class_label = -1
# else:
# class_label = i_tree % num_classes
decision_tree_classifier = ScikitlearnDecisionTreeClassifier(model=decision_tree_model)
for i_class in range(self._model.n_classes_):
class_label = i_class
# pylint: disable=W0212
trees.append(Tree(class_id=class_label,
leaf_nodes=decision_tree_classifier._get_leaf_nodes(0, i_tree, class_label, box)))
return trees
def get_trees(self) -> list:
"""
Get the decision trees.
:return: A list of decision trees.
"""
from art.metrics.verification_decisions_trees import Box, Tree
booster_dump = self._model.dump_model()["tree_info"]
trees = list()
for i_tree, tree_dump in enumerate(booster_dump):
box = Box()
# pylint: disable=W0212
if self._model._Booster__num_class == 2:
class_label = -1
else:
class_label = i_tree % self._model._Booster__num_class
trees.append(
Tree(
class_id=class_label,
leaf_nodes=self._get_leaf_nodes(
tree_dump["tree_structure"], i_tree, class_label, box),
))
return trees
def get_trees(self):
"""
Get the decision trees.
:return: A list of decision trees.
:rtype: `[Tree]`
"""
from art.metrics.verification_decisions_trees import Box, Tree
trees = list()
num_trees, num_classes = self._model.estimators_.shape
for i_tree in range(num_trees):
box = Box()
for i_class in range(num_classes):
decision_tree_classifier = ScikitlearnDecisionTreeRegressor(
model=self._model.estimators_[i_tree, i_class])
if num_classes == 2:
class_label = None
else:
class_label = i_class
# pylint: disable=W0212
trees.append(Tree(class_id=class_label,
leaf_nodes=decision_tree_classifier._get_leaf_nodes(0, i_tree, class_label, box)))
return trees