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10082b9262
* remove extra comma * exclusive bound * log file name * add cost to space * dataset_format * add load_openml_dataset test * docstr * revise test format * simplify restore * order categories * openml server exception in test * process space * add warning * log format * reduce n_cpu * nested space * hierarchical search space for CFO * non hierarchical for bs * unflatten hierarchical config * connection error * random sample * config signature * check ray version * preprocess numpy array * catboost preprocess * time budget * seed, verbose, hpo_method * test cfocat * shallow copy in flatten_dict prevent lgbm model duplication * match estimator name * quantize and log * test qloguniform and qrandint * test qlograndint * thread.running Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Qingyun Wu <qingyunwu@Qingyuns-MacBook-Pro-2.local>
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
'''!
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* Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved.
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* Licensed under the MIT License. See LICENSE file in the
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* project root for license information.
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'''
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from typing import Optional
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# try:
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# from ray import __version__ as ray_version
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# assert ray_version >= '1.0.0'
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# from ray.tune.trial import Trial
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# except (ImportError, AssertionError):
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from .trial import Trial
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import logging
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logger = logging.getLogger(__name__)
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class Nologger():
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'''Logger without logging
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'''
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def on_result(self, result):
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pass
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class SimpleTrial(Trial):
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'''A simple trial class
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'''
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def __init__(self, config, trial_id=None):
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self.trial_id = Trial.generate_id() if trial_id is None else trial_id
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self.config = config or {}
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self.status = Trial.PENDING
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self.start_time = None
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self.last_result = {}
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self.last_update_time = -float("inf")
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self.custom_trial_name = None
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self.trainable_name = "trainable"
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self.experiment_tag = "exp"
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self.verbose = False
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self.result_logger = Nologger()
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self.metric_analysis = {}
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self.n_steps = [5, 10]
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self.metric_n_steps = {}
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class BaseTrialRunner:
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"""Implementation of a simple trial runner
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Note that the caller usually should not mutate trial state directly.
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"""
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def __init__(self,
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search_alg=None, scheduler=None,
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metric: Optional[str] = None,
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mode: Optional[str] = 'min'):
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self._search_alg = search_alg
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self._scheduler_alg = scheduler
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self._trials = []
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self._metric = metric
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self._mode = mode
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def get_trials(self):
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"""Returns the list of trials managed by this TrialRunner.
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Note that the caller usually should not mutate trial state directly.
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"""
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return self._trials
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def add_trial(self, trial):
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"""Adds a new trial to this TrialRunner.
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Trials may be added at any time.
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Args:
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trial (Trial): Trial to queue.
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"""
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self._trials.append(trial)
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if self._scheduler_alg:
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self._scheduler_alg.on_trial_add(self, trial)
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def process_trial_result(self, trial, result):
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trial.update_last_result(result)
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self._search_alg.on_trial_result(trial.trial_id, result)
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if self._scheduler_alg:
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decision = self._scheduler_alg.on_trial_result(self, trial, result)
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if decision == "STOP":
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trial.set_status(Trial.TERMINATED)
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elif decision == "PAUSE":
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trial.set_status(Trial.PAUSED)
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def stop_trial(self, trial):
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"""Stops trial.
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"""
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if trial.status not in [Trial.ERROR, Trial.TERMINATED]:
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if self._scheduler_alg:
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self._scheduler_alg.on_trial_complete(
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self, trial.trial_id, trial.last_result)
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self._search_alg.on_trial_complete(trial.trial_id, trial.last_result)
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trial.set_status(Trial.TERMINATED)
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elif self._scheduler_alg:
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self._scheduler_alg.on_trial_remove(self, trial)
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class SequentialTrialRunner(BaseTrialRunner):
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"""Implementation of the sequential trial runner
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"""
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def step(self) -> Trial:
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"""Runs one step of the trial event loop.
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Callers should typically run this method repeatedly in a loop. They
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may inspect or modify the runner's state in between calls to step().
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returns a Trial to run
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"""
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trial_id = Trial.generate_id()
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config = self._search_alg.suggest(trial_id)
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if config:
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trial = SimpleTrial(config, trial_id)
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self.add_trial(trial)
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trial.set_status(Trial.RUNNING)
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else:
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trial = None
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self.running_trial = trial
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return trial
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