#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from logging import Logger
from typing import Dict, Iterable, List, Optional, Set, Tuple
import numpy as np
import pandas as pd
from ax.core.experiment import Experiment
from ax.early_stopping.strategies.base import BaseEarlyStoppingStrategy
from ax.early_stopping.utils import align_partial_results
from ax.exceptions.core import UnsupportedError
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import not_none
logger: Logger = get_logger(__name__)
[docs]class PercentileEarlyStoppingStrategy(BaseEarlyStoppingStrategy):
"""Implements the strategy of stopping a trial if its performance
falls below that of other trials at the same step."""
def __init__(
self,
metric_names: Optional[Iterable[str]] = None,
seconds_between_polls: int = 300,
percentile_threshold: float = 50.0,
min_progression: Optional[float] = 10,
max_progression: Optional[float] = None,
min_curves: Optional[int] = 5,
trial_indices_to_ignore: Optional[List[int]] = None,
normalize_progressions: bool = False,
) -> None:
"""Construct a PercentileEarlyStoppingStrategy instance.
Args:
metric_names: A (length-one) list of name of the metric to observe. If
None will default to the objective metric on the Experiment's
OptimizationConfig.
seconds_between_polls: How often to poll the early stopping metric to
evaluate whether or not the trial should be early stopped.
percentile_threshold: Falling below this threshold compared to other trials
at the same step will stop the run. Must be between 0.0 and 100.0.
e.g. if percentile_threshold=25.0, the bottom 25% of trials are stopped.
Note that "bottom" here is determined based on performance, not
absolute values; if `minimize` is False, then "bottom" actually refers
to the top trials in terms of metric value.
min_progression: Only stop trials if the latest progression value
(e.g. timestamp, epochs, training data used) is greater than this
threshold. Prevents stopping prematurely before enough data is gathered
to make a decision.
max_progression: Do not stop trials that have passed `max_progression`.
Useful if we prefer finishing a trial that are already near completion.
min_curves: Trials will not be stopped until a number of trials
`min_curves` have completed with curve data attached. That is, if
`min_curves` trials are completed but their curve data was not
successfully retrieved, further trials may not be early-stopped.
trial_indices_to_ignore: Trial indices that should not be early stopped.
normalize_progressions: Normalizes the progression column of the MapData df
by dividing by the max. If the values were originally in [0, `prog_max`]
(as we would expect), the transformed values will be in [0, 1]. Useful
for inferring the max progression and allows `min_progression` to be
specified in the transformed space. IMPORTANT: Typically, `min_curves`
should be > 0 to ensure that at least one trial has completed and that
we have a reliable approximation for `prog_max`.
"""
super().__init__(
metric_names=metric_names,
seconds_between_polls=seconds_between_polls,
trial_indices_to_ignore=trial_indices_to_ignore,
min_progression=min_progression,
max_progression=max_progression,
min_curves=min_curves,
normalize_progressions=normalize_progressions,
)
self.percentile_threshold = percentile_threshold
if metric_names is not None and len(list(metric_names)) > 1:
raise UnsupportedError(
"PercentileEarlyStoppingStrategy only supports a single metric. Use "
"LogicalEarlyStoppingStrategy to compose early stopping strategies "
"with multiple metrics."
)
[docs] def should_stop_trials_early(
self,
trial_indices: Set[int],
experiment: Experiment,
) -> Dict[int, Optional[str]]:
"""Stop a trial if its performance is in the bottom `percentile_threshold`
of the trials at the same step.
Args:
trial_indices: Indices of candidate trials to consider for early stopping.
experiment: Experiment that contains the trials and other contextual data.
Returns:
A dictionary mapping trial indices that should be early stopped to
(optional) messages with the associated reason. An empty dictionary
means no suggested updates to any trial's status.
"""
metric_name, minimize = self._default_objective_and_direction(
experiment=experiment
)
data = self._check_validity_and_get_data(
experiment=experiment, metric_names=[metric_name]
)
if data is None:
# don't stop any trials if we don't get data back
return {}
map_key = next(iter(data.map_keys))
df = data.map_df
# default checks on `min_progression` and `min_curves`; if not met, don't do
# early stopping at all and return {}
if not self.is_eligible_any(
trial_indices=trial_indices, experiment=experiment, df=df, map_key=map_key
):
return {}
try:
metric_to_aligned_means, _ = align_partial_results(
df=df,
progr_key=map_key,
metrics=[metric_name],
)
except Exception as e:
logger.warning(
f"Encountered exception while aligning data: {e}. "
"Not early stopping any trials."
)
return {}
aligned_means = metric_to_aligned_means[metric_name]
decisions = {
trial_index: self._should_stop_trial_early(
trial_index=trial_index,
experiment=experiment,
df=aligned_means,
df_raw=df,
map_key=map_key,
minimize=minimize,
)
for trial_index in trial_indices
}
return {
trial_index: reason
for trial_index, (should_stop, reason) in decisions.items()
if should_stop
}
def _should_stop_trial_early(
self,
trial_index: int,
experiment: Experiment,
df: pd.DataFrame,
df_raw: pd.DataFrame,
map_key: str,
minimize: bool,
) -> Tuple[bool, Optional[str]]:
"""Stop a trial if its performance is in the bottom `percentile_threshold`
of the trials at the same step.
Args:
trial_index: Indices of candidate trial to stop early.
experiment: Experiment that contains the trials and other contextual data.
df: Dataframe of partial results after applying interpolation,
filtered to objective metric.
df_raw: The original MapData dataframe (before interpolation).
map_key: Name of the column of the dataset that indicates progression.
minimize: Whether objective value is being minimized.
Returns:
A tuple `(should_stop, reason)`, where `should_stop` is `True` iff the
trial should be stopped, and `reason` is an (optional) string providing
information on why the trial should or should not be stopped.
"""
logger.info(f"Considering trial {trial_index} for early stopping.")
stopping_eligible, reason = self.is_eligible(
trial_index=trial_index, experiment=experiment, df=df_raw, map_key=map_key
)
if not stopping_eligible:
return False, reason
# dropna() here will exclude trials that have not made it to the
# last progression of the trial under consideration, and therefore
# can't be included in the comparison
df_trial = not_none(df[trial_index].dropna())
trial_last_prog = df_trial.index.max()
data_at_last_progression = df.loc[trial_last_prog].dropna()
logger.info(
"Early stopping objective at last progression is:\n"
f"{data_at_last_progression}."
)
# check for enough number of trials with data
if (
self.min_curves is not None
and len(data_at_last_progression) < self.min_curves # pyre-ignore[58]
):
return self._log_and_return_num_trials_with_data(
logger=logger,
trial_index=trial_index,
trial_last_progression=trial_last_prog,
num_trials_with_data=len(data_at_last_progression),
min_curves=self.min_curves, # pyre-ignore[6]
)
# percentile early stopping logic
percentile_threshold = (
100.0 - self.percentile_threshold if minimize else self.percentile_threshold
)
percentile_value = np.percentile(data_at_last_progression, percentile_threshold)
trial_objective_value = data_at_last_progression[trial_index]
should_early_stop = (
trial_objective_value > percentile_value
if minimize
else trial_objective_value < percentile_value
)
comp = "worse" if should_early_stop else "better"
reason = (
f"Trial objective value {trial_objective_value} is {comp} than "
f"{percentile_threshold:.1f}-th percentile ({percentile_value}) "
"across comparable trials."
)
logger.info(
f"Early stopping decision for {trial_index}: {should_early_stop}. "
f"Reason: {reason}"
)
return should_early_stop, reason
