ART XGBoostClassifier Classifier Parameters Optimization Suggestions

[vincentliuheyang]

Is your feature request related to a problem? Please describe.
One thing I found interesting is that you did not implement fit model for tree models. Still, I am trying to implement the fit method on my own, so I imherit the XGBoostClassifier model to implement myself.

class XGBARTClassifier(XGBoostClassifier)

However, I found one thing that might be problematic is that if I am passing a unfitted xgboost.XGBClassifier, then the model is going to tell me

AttributeError: 'XGBClassifier' object has no attribute 'n_classes_'

I found the problem is caused by this method structure:

    def _get_nb_classes(self, nb_classes: Optional[int]) -> int:
        """
        Return the number of output classes.

        :return: Number of classes in the data.
        """
        from xgboost import Booster, XGBClassifier

        if isinstance(self._model, Booster):
            try:
                return int(len(self._model.get_dump(dump_format="json")) / self._model.n_estimators)  # type: ignore
            except AttributeError:
                if nb_classes is not None:
                    return nb_classes
                raise NotImplementedError(
                    "Number of classes cannot be determined automatically. "
                    + "Please manually set argument nb_classes in XGBoostClassifier."
                ) from AttributeError

        if isinstance(self._model, `XGBClassifier`):
            return self._model.n_classes_

        return -1

For XGBClassifier I passed in, if the model is unfitted, it does not have the attribute n_classes_. Then I cannot pass through the unfitted model to proceed.

Describe the solution you'd like
This issue might be minor, but I think if you could check if the XGBClassifier is fitted or not before return the n_classes_ would be better if I am going to pass in an unfitted model.

Thank you