GBM: support hyperopt #2490
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GBM: support hyperopt #2490
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fixes a bug where small O(1) number of boosting rounds led to infinite loop
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justinxzhao
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Sep 15, 2022
| booster = self.train_step( | ||
| params, lgb_train, eval_sets, eval_names, booster, self.boosting_round_log_frequency, evals_result | ||
| ) | ||
| def check_progress_on_validation( |
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Is this function the same as the ECD trainer? If so, we should factor this out, like you've done with append_metrics.
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Not entirely, ECD trainer has additional logic here for reducing learning rate and increasing batch size
justinxzhao
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Runs evaluation after every
boosting_rounds_per_checkpointto populate training/validation metrics as required by hyperoptMimics as much as possible the ECD train loop logic