Dealing with empty trees in Random Forests about pyxai HOT 4 CLOSED

elif "leaf" in tree_JSON:
            # Special case when the tree is just a leaf, this append when no split is realized by the solver, but the weight have to be taken into account
            return LeafNode(tree_JSON["leaf"])

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from pyxai import Learning

iris = load_iris()
features = iris.data
rf = RandomForestClassifier(n_estimators=300, max_depth=8, min_samples_leaf=0.5,
                            min_samples_split=0.3, min_weight_fraction_leaf=0.1, criterion='entropy',
                            random_state=2023)
rf.fit(features, iris.target)

px_learner, px_model = Learning.import_models(rf, feature_names=['sepal_length', 'sepal_width',
                                                                 'petal_length', 'petal_width'])

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-5a023b46d641> in <module>
     12 rf.fit(features, iris.target)
     13
---> 14 px_learner, px_model = Learning.import_models(rf, feature_names=['sepal_length', 'sepal_width',
     15                                                                  'petal_length', 'petal_width'])
     16

/opt/conda/envs/pyxai/lib/python3.9/site-packages/pyxai/Learning.py in import_models(models, feature_names)
    103         for l in learner_information: l.set_feature_names(feature_names)
    104
--> 105     result_output = learner.convert_model(evaluation_output, learner_information)
    106
    107     Tools.verbose("---------------   Explainer   ----------------")

/opt/conda/envs/pyxai/lib/python3.9/site-packages/pyxai/sources/learning/learner.py in convert_model(self, output, learner_information)
    264                 return self.to_DT_CLS(self.learner_information)
    265             elif output == EvaluationOutput.RF:
--> 266                 return self.to_RF_CLS(self.learner_information)
    267             elif output == EvaluationOutput.BT:
    268                 return self.to_BT_CLS(self.learner_information)

/opt/conda/envs/pyxai/lib/python3.9/site-packages/pyxai/sources/learning/scikitlearn.py in to_RF_CLS(self, learner_information)
    103             for sk_tree in random_forest:
    104                 sk_raw_tree = sk_tree.tree_
--> 105                 decision_trees.append(self.classifier_to_DT(sk_tree, sk_raw_tree, id_solver_results))
    106             random_forests.append(
    107                 RandomForest(decision_trees, n_classes=len(sk_tree.classes_), learner_information=self.learner_information[id_solver_results]))

/opt/conda/envs/pyxai/lib/python3.9/site-packages/pyxai/sources/learning/scikitlearn.py in classifier_to_DT(self, sk_tree, sk_raw_tree, id_solver_results)
    140                 nodes[i].right = nodes[id_right] if id_right in nodes else LeafNode(numpy.argmax(sk_raw_tree.value[id_right][0]))
    141
--> 142         root = nodes[0] if 0 in nodes else DecisionNode(1, 0, sk_raw_tree.value[0][0])
    143         return DecisionTree(sk_tree.n_features_in_, root, sk_tree.classes_, id_solver_results=id_solver_results,
    144                             learner_information=self.learner_information[id_solver_results])

TypeError: __init__() takes 2 positional arguments but 4 were given

scikit-learn                       1.3.2
numpy                              1.20.3

sk_raw_tree.feature
Out[1]: array([-2], dtype=int64)
sk_raw_tree.value
Out[2]: array([[[49., 51., 50.]]])
sk_raw_tree.children_left
Out[3]: array([-1], dtype=int64)
sk_raw_tree.children_right
Out[4]: array([-1], dtype=int64)
sk_raw_tree.n_leaves
Out[5]: 1
sk_raw_tree.node_count
Out[6]: 1