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model_history -> save_best_model_per_estimator #283
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sonichi
commented
Nov 15, 2021
•
edited
edited
- if save_best_model_per_estimator is False and retrain_final is True, unfit the model after evaluation in HPO.
- retrain if using ray.
- update ITER_HP in config after a trial is finished.
- change prophet logging level.
- example and notebook update.
- allow settings to be passed to AutoML constructor. Are you planning to add multi-output-regression capability to FLAML #192 Is multi-tasking allowed? #277 can pass the auotml setting to the constructor instead of requiring a derived class.
- remove model_history.
- checkpoint bug fix.
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User need to specify save_best_model_per_estimator in every test; redundant?
Not every test. Only if you need to save the best model per estimator. |
the states can best_trial.get_best_checkpoint return a non-empty value. | ||
""" | ||
import torch | ||
def evaluate(self, eval_dataset=None, ignore_keys=None, metric_key_prefix="eval"): |
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add typing?
flaml/automl.py
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return self._model_history | ||
self._settings = settings | ||
settings["time_budget"] = settings.get("time_budget", 60) | ||
settings["task"] = settings.get("task", "regression") |
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The default task is regression? Used to be classification.
flaml/automl.py
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ensemble=None, | ||
eval_method=None, | ||
log_type=None, | ||
save_best_model_per_estimator=None, |
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Is it necessary to change 'model_history' to save_best_model_per_estimator
? Won't be compatible with code using older versions.
flaml/automl.py
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self._n_concurrent_trials = n_concurrent_trials | ||
self._early_stop = early_stop | ||
self._use_ray = use_ray or n_concurrent_trials > 1 | ||
self._state.save_best_model_per_estimator = save_best_model_per_estimator | ||
# use the following condition if we have an estimation of average_trial_time and average_trial_overhead |
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Move these two commented lines to line #1861