Error when using preprocessing per case in model selection #145
Description
Hello,
I followed this notebook to create an ensemble.
First I tune the base learners with a preprocessing pipeline:
preprocessing = {'sc': [StandardScaler()]}
# Fit the base learners
evl.fit(
x_train, y_train,
estimators=base_learners,
param_dicts=param_dicts,
preprocessing=preprocessing,
n_iter=1
)
In the notebook, the model selection is done like this:
in_layer_proba = SuperLearner(model_selection=True).add(base_learners,
proba=True)
in_layer_class = SuperLearner(model_selection=True).add(base_learners,
proba=False)
This works fine, but I think the preprocessing part is missing here (the standard scaler), is it not? So I did the following:
in_layer_proba = SuperLearner(model_selection=True).add(
estimators_per_case, preprocessing_cases, proba=True
)
in_layer_class = SuperLearner(model_selection=True).add(
estimators_per_case, preprocessing_cases, proba=False
)
preprocess = {'proba': [('layer-1', in_layer_proba)],
'class': [('layer-1', in_layer_class)]}
evl.fit(
x_train, y_train,
meta_learners,
param_dicts=param_dicts,
preprocessing=preprocess,
n_iter=1
)
And I get the following error. I am not sure if I am doing something wrong or if it is a bug?