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Added an info reminding user that if no time_budget and no max_iter is specified, then effectively zero-shot AutoML is used (#850)

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* Added an info reminding user that if no time_budget and no max_iter is specified, then effectively zero-shot AutoML is used

* moved message to line 2818

* Update flaml/automl/automl.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/automl/automl.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
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Jing Dong 2022-12-19 01:49:00 +08:00 committed by GitHub
parent 2b203ca694
commit b2d51b648c
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flaml/automl/automl.py
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@ -2566,6 +2566,7 @@ class AutoML(BaseEstimator):
self._use_ray = use_ray or n_concurrent_trials > 1 self._use_ray = use_ray or n_concurrent_trials > 1
# use the following condition if we have an estimation of average_trial_time and average_trial_overhead # use the following condition if we have an estimation of average_trial_time and average_trial_overhead
# self._use_ray = use_ray or n_concurrent_trials > ( average_trail_time + average_trial_overhead) / (average_trial_time) # self._use_ray = use_ray or n_concurrent_trials > ( average_trail_time + average_trial_overhead) / (average_trial_time)
if self._use_ray is not False: if self._use_ray is not False:
import ray import ray
@ -2816,6 +2817,7 @@ class AutoML(BaseEstimator):
logger.warning( logger.warning(
"No search budget is provided via time_budget or max_iter." "No search budget is provided via time_budget or max_iter."
" Training only one model per estimator." " Training only one model per estimator."
" Zero-shot AutoML is used for certain tasks and estimators."
" To tune hyperparameters for each estimator," " To tune hyperparameters for each estimator,"
" please provide budget either via time_budget or max_iter." " please provide budget either via time_budget or max_iter."
) )