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make default verbose level > 0 when using ray (#272)

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* make default verbose level > 0 when using ray

* default hpo method when using ray

* bug fix: == -> =
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Chi Wang 2021-11-04 22:06:19 -07:00 committed by GitHub
parent 549a0dfb53
commit fc32eca24b
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 4 additions and 4 deletions
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8
flaml/automl.py
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@ -1578,7 +1578,6 @@ class AutoML:
) )
logger.info("List of ML learners in AutoML Run: {}".format(estimator_list)) logger.info("List of ML learners in AutoML Run: {}".format(estimator_list))
self.estimator_list = estimator_list self.estimator_list = estimator_list
self._hpo_method = hpo_method or ("cfo" if n_concurrent_trials == 1 else "bs")
self._state.time_budget = time_budget or 1e10 self._state.time_budget = time_budget or 1e10
self._active_estimators = estimator_list.copy() self._active_estimators = estimator_list.copy()
self._ensemble = ensemble self._ensemble = ensemble
@ -1592,7 +1591,8 @@ class AutoML:
self._state.n_jobs = n_jobs self._state.n_jobs = n_jobs
self._n_concurrent_trials = n_concurrent_trials self._n_concurrent_trials = n_concurrent_trials
self._early_stop = early_stop self._early_stop = early_stop
self._use_ray = use_ray or self._n_concurrent_trials > 1 self._use_ray = use_ray or n_concurrent_trials > 1
self._hpo_method = hpo_method or ("bs" if self._use_ray else "cfo")
if log_file_name: if log_file_name:
with training_log_writer(log_file_name, append_log) as save_helper: with training_log_writer(log_file_name, append_log) as save_helper:
self._training_log = save_helper self._training_log = save_helper
@ -1709,7 +1709,7 @@ class AutoML:
resources_per_trial=resources_per_trial, resources_per_trial=resources_per_trial,
time_budget_s=self._state.time_budget, time_budget_s=self._state.time_budget,
num_samples=self._max_iter, num_samples=self._max_iter,
verbose=max(self.verbose - 3, 0), verbose=max(self.verbose - 2, 0),
raise_on_failed_trial=False, raise_on_failed_trial=False,
) )
# logger.info([trial.last_result for trial in analysis.trials]) # logger.info([trial.last_result for trial in analysis.trials])
@ -2297,7 +2297,7 @@ class AutoML:
speed = delta_loss / delta_time speed = delta_loss / delta_time
if speed: if speed:
estimated_cost = max(2 * gap / speed, estimated_cost) estimated_cost = max(2 * gap / speed, estimated_cost)
estimated_cost == estimated_cost or 1e-9 estimated_cost = estimated_cost or 1e-9
inv.append(1 / estimated_cost) inv.append(1 / estimated_cost)
else: else:
estimated_cost = self._eci[i] estimated_cost = self._eci[i]