Improve model selection #848
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fverac
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Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
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Should we add a test that sklearn pipelines work correctly? |
Good point, I'll add several tests covering this functionality. |
fverac
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Feb 7, 2024
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
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fverac
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fverac
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Feb 14, 2024
fverac
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Feb 14, 2024
| scores.append(model.best_score) | ||
| self._all_scores = scores | ||
| self._best_score = np.max(scores) | ||
| self._best_model = self.models[np.argmax(scores)] | ||
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| else: | ||
| self._best_model.train(is_selecting, *args, **kwargs) | ||
| self._best_model.train(is_selecting, folds, *args, **kwargs) |
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is_selecting will be False here. Do we need to pass folds in this case?
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It's academic; if is_selecting is False then folds will be None anyway so we could pass folds or we could equivalently pass None.
But I think this is slightly more robust to changes in the upstream logic, since if for some reason we ever change that invariant then the logic here might not need to be changed.
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Fixes two-stage model selection used by IV models, so that we first select and train nuisances for y, t, etc., and then select and train the covariance model. Added central documentation for how to specify how to select models and simplifies the documentation for each estimator by pointing there.