Known issues

Pickle serialization/deserialization in models using CatBoost steps

When trying to use a model loaded from a pickle file and that contains CatBoost steps, you might see the following error:

>>> model = joblib.load("model.pkl")
>>> model.predict(data)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/venv/lib/python3.7/site-packages/baikal/_core/", line 470, in predict
    X_norm, [], outputs, allow_unused_inputs=True, follow_targets=False
  File "/venv/lib/python3.7/site-packages/baikal/_core/", line 191, in _get_required_nodes
    required_nodes |= backtrack(output)
  File "/venv/lib/python3.7/site-packages/baikal/_core/", line 176, in backtrack
    parent_node = output.node
  File "/venv/lib/python3.7/site-packages/baikal/_core/", line 44, in node
    return self.step._nodes[self.port]
AttributeError: 'CatBoostClassifierStep' object has no attribute '_nodes'

This is because CatBoost estimators (CatBoostClassifier, CatBoostRegressor) implement their own __getstate__ and __setstate__ methods and, if they are not overridden appropriately, they won’t include Step-specific attributes in the pickled result. The solution to this problem is to override the __getstate__ and __setstate__ methods to include the missing attributes as follows:

class CatBoostClassifierStep(Step, CatBoostClassifier):
    def __init__(self, *args, name=None, n_outputs=1, **kwargs):
        super().__init__(*args, name=name, n_outputs=n_outputs, **kwargs)

    def __getstate__(self):
        state = super().__getstate__()
        state["_name"] = self._name
        state["_nodes"] = self._nodes
        state["_n_outputs"] = self._n_outputs
        return state

    def __setstate__(self, state):
        self._name = state.pop("_name")
        self._nodes = state.pop("_nodes")
        self._n_outputs = state.pop("_n_outputs")