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a-wozniakowski opened this issue Jul 30, 2020 · 2 comments · Fixed by #28701
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Support fit_params in stacking #18028

a-wozniakowski opened this issue Jul 30, 2020 · 2 comments · Fixed by #28701
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module:ensemble New Feature

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@a-wozniakowski
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Currently, there is no support for **fit_params in the fit method of _BaseStacking:

scikit-learn/sklearn/ensemble/_stacking.py

Line 110 in fd23727

def fit(self, X, y, sample_weight=None):

As introduced in issue #15953 for _MultiOutputEstimator, it seems natural to extend the utility to stacking. A proposed implementation in the base stacking class is as follows:

from ..utils.validation import _check_fit_params

def fit(self, X, y, sample_weight=None, **fit_params):
    # Right before predictions = Parallel...
    if fit_params:
        fit_params = _check_fit_params(X, fit_params)
    else:
        fit_params = (dict(sample_weight=sample_weight)
                      if sample_weight is not None
                      else None)
    # Then, utilize fit_params in the parallelized cross_val_predict

Subsequently, alter the fit methods for StackingClassifier and StackingRegressor such that they support **fit_params. If this is favorable, then I can write an implementation and start the pull request.
@jnothman
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jnothman commented Jul 30, 2020 via email

@a-wozniakowski
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@jnothman thanks for your reply. From an earlier issue, sample weights appear to be the underlying motivation for the design principle in FeatureUnion:

"I think fit_params is more or less just a more general way to implement sample_weights, with a slightly different API. I tried to implement sample_props once and it was mostly renaming fit_params (and sometimes moving it from __init__ to fit)"

Originally posted by @amueller in #7136 (comment)

With _MultiOutputEstimator and _BaseStacking there is also the motivation of early stopping (which does not apply to transformers). As the estimators in stacking may be heterogeneous, the name-based prefixing seems less error prone.

@adrinjalali adrinjalali mentioned this issue Oct 29, 2020
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@adrinjalali adrinjalali mentioned this issue Jun 24, 2021
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@adrinjalali adrinjalali mentioned this issue Aug 18, 2022
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@adrinjalali adrinjalali mentioned this issue Apr 26, 2023
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@StefanieSenger StefanieSenger mentioned this issue Mar 26, 2024
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FEA metadata routing for StackingClassifier and StackingRegressor StefanieSenger/scikit-learn
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@jnothman @cmarmo @a-wozniakowski