import copy
import logging
from typing import Dict, List, Optional
import numpy as np
from ray.tune.search.searcher import Searcher
from ray.tune.search.util import _set_search_properties_backwards_compatible
from ray.util import PublicAPI
logger = logging.getLogger(__name__)
TRIAL_INDEX = "__trial_index__"
"""str: A constant value representing the repeat index of the trial."""
def _warn_num_samples(searcher: Searcher, num_samples: int):
if isinstance(searcher, Repeater) and num_samples % searcher.repeat:
logger.warning(
"`num_samples` is now expected to be the total number of trials, "
"including the repeat trials. For example, set num_samples=15 if "
"you intend to obtain 3 search algorithm suggestions and repeat "
"each suggestion 5 times. Any leftover trials "
"(num_samples mod repeat) will be ignored."
)
class _TrialGroup:
"""Internal class for grouping trials of same parameters.
This is used when repeating trials for reducing training variance.
Args:
primary_trial_id: Trial ID of the "primary trial".
This trial is the one that the Searcher is aware of.
config: Suggested configuration shared across all trials
in the trial group.
max_trials: Max number of trials to execute within this group.
"""
def __init__(self, primary_trial_id: str, config: Dict, max_trials: int = 1):
assert type(config) is dict, "config is not a dict, got {}".format(config)
self.primary_trial_id = primary_trial_id
self.config = config
self._trials = {primary_trial_id: None}
self.max_trials = max_trials
def add(self, trial_id: str):
assert len(self._trials) < self.max_trials
self._trials.setdefault(trial_id, None)
def full(self) -> bool:
return len(self._trials) == self.max_trials
def report(self, trial_id: str, score: float):
assert trial_id in self._trials
if score is None:
raise ValueError("Internal Error: Score cannot be None.")
self._trials[trial_id] = score
def finished_reporting(self) -> bool:
return (
None not in self._trials.values() and len(self._trials) == self.max_trials
)
def scores(self) -> List[Optional[float]]:
return list(self._trials.values())
def count(self) -> int:
return len(self._trials)
[docs]@PublicAPI
class Repeater(Searcher):
"""A wrapper algorithm for repeating trials of same parameters.
Set tune.TuneConfig(num_samples=...) to be a multiple of `repeat`. For example,
set num_samples=15 if you intend to obtain 3 search algorithm suggestions
and repeat each suggestion 5 times. Any leftover trials
(num_samples mod repeat) will be ignored.
It is recommended that you do not run an early-stopping TrialScheduler
simultaneously.
Args:
searcher: Searcher object that the
Repeater will optimize. Note that the Searcher
will only see 1 trial among multiple repeated trials.
The result/metric passed to the Searcher upon
trial completion will be averaged among all repeats.
repeat: Number of times to generate a trial with a repeated
configuration. Defaults to 1.
set_index: Sets a tune.search.repeater.TRIAL_INDEX in
Trainable/Function config which corresponds to the index of the
repeated trial. This can be used for seeds. Defaults to True.
Example:
.. code-block:: python
from ray.tune.search import Repeater
search_alg = BayesOptSearch(...)
re_search_alg = Repeater(search_alg, repeat=10)
# Repeat 2 samples 10 times each.
tuner = tune.Tuner(
trainable,
tune_config=tune.TuneConfig(
search_alg=re_search_alg,
num_samples=20,
),
)
tuner.fit()
"""
def __init__(self, searcher: Searcher, repeat: int = 1, set_index: bool = True):
self.searcher = searcher
self.repeat = repeat
self._set_index = set_index
self._groups = []
self._trial_id_to_group = {}
self._current_group = None
super(Repeater, self).__init__(
metric=self.searcher.metric, mode=self.searcher.mode
)
def suggest(self, trial_id: str) -> Optional[Dict]:
if self._current_group is None or self._current_group.full():
config = self.searcher.suggest(trial_id)
if config is None:
return config
self._current_group = _TrialGroup(
trial_id, copy.deepcopy(config), max_trials=self.repeat
)
self._groups.append(self._current_group)
index_in_group = 0
else:
index_in_group = self._current_group.count()
self._current_group.add(trial_id)
config = self._current_group.config.copy()
if self._set_index:
config[TRIAL_INDEX] = index_in_group
self._trial_id_to_group[trial_id] = self._current_group
return config
[docs] def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, **kwargs):
"""Stores the score for and keeps track of a completed trial.
Stores the metric of a trial as nan if any of the following conditions
are met:
1. ``result`` is empty or not provided.
2. ``result`` is provided but no metric was provided.
"""
if trial_id not in self._trial_id_to_group:
logger.error(
"Trial {} not in group; cannot report score. "
"Seen trials: {}".format(trial_id, list(self._trial_id_to_group))
)
trial_group = self._trial_id_to_group[trial_id]
if not result or self.searcher.metric not in result:
score = np.nan
else:
score = result[self.searcher.metric]
trial_group.report(trial_id, score)
if trial_group.finished_reporting():
scores = trial_group.scores()
self.searcher.on_trial_complete(
trial_group.primary_trial_id,
result={self.searcher.metric: np.nanmean(scores)},
**kwargs
)
def get_state(self) -> Dict:
self_state = self.__dict__.copy()
del self_state["searcher"]
return self_state
def set_state(self, state: Dict):
self.__dict__.update(state)
def save(self, checkpoint_path: str):
self.searcher.save(checkpoint_path)
def restore(self, checkpoint_path: str):
self.searcher.restore(checkpoint_path)
def set_search_properties(
self, metric: Optional[str], mode: Optional[str], config: Dict, **spec
) -> bool:
return _set_search_properties_backwards_compatible(
self.searcher.set_search_properties, metric, mode, config, **spec
)