RuleFitClassifier(tree_generator = GradientBoostingClassifier()) not working as per documentation

[Manuelhrokr]

Hi,

When using RuleFitClassifier(tree_generator = GradientBoostingClassifier()) with a GradientBoostingClassifier() object fitted and optimized separately via Scikitlearn API, it returns the next error when fitting RuleFitClassifier(tree_generator = GradientBoostingClassifier()):

ValueError: n_estimators=1 must be larger or equal to estimators_.shape[0]=100 when warm_start==True

When inspecting whats inside RuleFitClassifier(tree_generator = GradientBoostingClassifier()) after fitting the model, the GradientBoostingClassifier() is completely modified to other parameters different than those optimized before fitting RuleFitClassifier(), i.e., GradientBoostingClassifier(max_leaf_nodes=4, n_estimators=1, random_state=0, warm_start=True). Not sure why these parameters (from the GradientBoostingClassifier()) are changed inside the RuleFitClassifier() object.

If RuleFitClassifier(tree_generator = None), everything works well.

As per documentation:

tree_generator : Optional: this object will be used as provided to generate the rules. This will override almost all the other properties above. Must be GradientBoostingRegressor(), GradientBoostingClassifier(), or RandomForestRegressor()

• Which are those properties from RuleFitClassifier() that are override if tree_generator=GradientBoostingClassifier()?
• Why does this behavior occurs?

Here is the closest solution I found in Issue #34, however the behavior is not clear.

Any help will be highly appreciated.

Many thanks!