adding bagged estimator to have better results in terms of monotonicity #82
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Hey @juAlberge, thank you for this PR! Some early thoughts on this :)
Also, adding an example for the competing risk setting would be very nice
| self.bagging = bagging # number of models to train | ||
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| def fit(self, X, y, times=None): | ||
| self.models = [] |
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In "sklearn conventions", we set an underscore suffix to identify outputs attributes of the fitting process
Suggested change
| self.models = [] | |
| self.models_ = [] |
| survival_boost_params.pop("bagging") | ||
| for i in range(self.bagging): | ||
| model = SurvivalBoost( | ||
| random_state=self.random_state + i, **survival_boost_params |
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You need to handle the default case when self.random_state is None
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| class BaggedSurvivalBoost(BaseEstimator, ClassifierMixin): | ||
| def __init__( | ||
| # TODO: run a grid search on a few datasets to find good defaults. |
| self.models = [] | ||
| survival_boost_params = self.get_params() | ||
| survival_boost_params.pop("random_state") | ||
| survival_boost_params.pop("bagging") |
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Now that I'm thinking about this, what is your opinion on adding a bagging parameter to SurvivalBoost, instead of defining this new class? This would slightly complicate the internal methods of SurvivalBoost, but we would only have one class to deal with.
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new estimator:
BaggedSurvivalBoost
-> argument: bagging: to add the number of SurvivalBoost estimators to be created.
TODO: