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[MRG] FIX use a stump as base estimator in RUSBoostClassifier #545

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merged 7 commits into from
Jun 11, 2019
Merged

[MRG] FIX use a stump as base estimator in RUSBoostClassifier #545

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0685603
Use a stump as base estimator in RUSBoostClassifier
Feb 15, 2019
cd4e0bd
Call super _validate_estimator to set the default estimator
Feb 15, 2019
810e1e9
make pep8speaks happy
Feb 15, 2019
02c3f1d
Fix test
Feb 23, 2019
fa8008b
Merge remote-tracking branch 'origin/master' into rusboost_default_es…
glemaitre Jun 11, 2019
b960ec7
iter
glemaitre Jun 11, 2019
e7b4356
PEP8
glemaitre Jun 11, 2019
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Use a stump as base estimator in RUSBoostClassifier
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chkoar committed Feb 15, 2019
commit 068560369ff8c79cab9ba7c8275a510f24142733
9 changes: 5 additions & 4 deletions imblearn/ensemble/_weight_boosting.py
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Expand Up @@ -30,10 +30,11 @@ class RUSBoostClassifier(AdaBoostClassifier):

Parameters
----------
base_estimator : object, optional (default=DecisionTreeClassifier)
base_estimator : object, optional (default=None)
The base estimator from which the boosted ensemble is built.
Support for sample weighting is required, as well as proper `classes_`
and `n_classes_` attributes.
Support for sample weighting is required, as well as proper
``classes_`` and ``n_classes_`` attributes. If ``None``, then
the base estimator is ``DecisionTreeClassifier(max_depth=1)``

n_estimators : integer, optional (default=50)
The maximum number of estimators at which boosting is terminated.
Expand Down Expand Up @@ -151,7 +152,7 @@ def fit(self, X, y, sample_weight=None):
super(RUSBoostClassifier, self).fit(X, y, sample_weight)
return self

def _validate_estimator(self, default=DecisionTreeClassifier()):
def _validate_estimator(self, default=DecisionTreeClassifier(max_depth=1)):
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I think that you are right. We should actually call the parent class then:
https://github.com/scikit-learn/scikit-learn/blob/7389dba/sklearn/ensemble/weight_boosting.py#L414

"""Check the estimator and the n_estimator attribute, set the
`base_estimator_` attribute."""
if not isinstance(self.n_estimators, (numbers.Integral, np.integer)):
Expand Down