feature_importances for Pipeline including XGBoost don't work

[paulotrefosco]

Description of the bug
For a machine using only XGBoostClassifier (e.g. xgbm) I can extract feature_importances(xgbm), but for a pipeline machine like one hot encoder |> XGBoostClassifier it seems to exist a bug.
I've tried to have some checks like reports_feature_importances(xgbm.model.xg_boost_classifier) (results in true) but both codes below lead to errors:

feature_importances(xgbm) >> Results nothing

feature_importances(xgbm.model.xg_boost_classifier) >> Error Message:

MethodError: no method matching feature_importances(::MLJXGBoostInterface.XGBoostClassifier)

Closest candidates are:
  feature_importances(::Union{MLJXGBoostInterface.XGBoostAbstractClassifier, MLJXGBoostInterface.XGBoostAbstractRegressor}, ::Any, ::Any)
   @ MLJXGBoostInterface C:\Users\User\.julia\packages\MLJXGBoostInterface\uFARS\src\MLJXGBoostInterface.jl:121
  feature_importances(::Machine)
   @ MLJBase C:\Users\User\.julia\packages\MLJBase\eCnWm\src\machines.jl:910
  feature_importances(::Union{MLJDecisionTreeInterface.AdaBoostStumpClassifier, MLJDecisionTreeInterface.DecisionTreeClassifier, MLJDecisionTreeInterface.DecisionTreeRegressor, MLJDecisionTreeInterface.RandomForestClassifier, MLJDecisionTreeInterface.RandomForestRegressor}, ::Any, ::Any)
   @ MLJDecisionTreeInterface C:\Users\User\.julia\packages\MLJDecisionTreeInterface\kPIDf\src\MLJDecisionTreeInterface.jl:483


Stacktrace:
 [1] top-level scope
   @ In[32]:1

To Reproduce

XGBC = @load XGBoostClassifier
xgb = XGBC()
ohe = OneHotEncoder()

xgb_pipe = ohe |> xgb

y, X = unpack(df, ==(:y_label), col->true)

train, test = partition(1:length(y), 0.7, shuffle=true)

xgbm = machine(xgb_pipe, X, y)
fit!(xgbm, rows=train, verbosity=0)

feature_importances(xgbm.model.xg_boost_classifier)

Versions
julia\environments\v1.9
MLJ v0.20.0

Any idea if it's a bug or if I should write something different in the code?
Thanks a lot for the support!

[ablaom]

No this is something between a missing feature and a bug, depending on your point-of-view. You're not doing anything wrong that I can see.

[ablaom]

closed by JuliaAI/MLJBase.jl#963