scikit-lego: [BUG] Stacking classifier cannot use Thresholder function - no .predict_proba

Description:

I’m able to use the thresholder on sklearn’s voting classifer, but not on the stacking classifier. It throws this error, which I believe is in error. StackingClassifier does have predict_proba. Maybe I’m missunderstanding the use case, but this seems to fit.

ValueError: The Thresholder meta model only works on classifcation models with .predict_proba.

Code for reproduction (using the sklearn sample data for StackingClassifier):

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import StackingClassifier

X, y = load_iris(return_X_y=True)
estimators = [
    ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
    ('svr', make_pipeline(StandardScaler(), LinearSVC(random_state=42)))]
clf = StackingClassifier(    estimators=estimators, final_estimator=LogisticRegression())
clf.fit(X, y)

a = Thresholder(clf, threshold=0.2)
a.fit(X, y)
a.predict(X)

Full trace:

ValueError                                Traceback (most recent call last)
<ipython-input-26-1b89dbfa16b8> in <module>
     16 
     17 a = Thresholder(clf, threshold=0.2)
---> 18 a.fit(X_train_std, np.ceil(y_train[targets[2]]))
     19 a.predict(X_train_std)

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\sklego\meta\thresholder.py in fit(self, X, y, sample_weight)
     54         self.estimator_ = clone(self.model)
     55         if not isinstance(self.estimator_, ProbabilisticClassifier):
---> 56             raise ValueError(
     57                 "The Thresholder meta model only works on classifcation models with .predict_proba."
     58             )

ValueError: The Thresholder meta model only works on classifcation models with .predict_proba.