#!/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 Any, Dict, Iterable, List, Optional, Set, Tuple
import pandas as pd
from ax.core.experiment import Experiment
from ax.early_stopping.strategies.base import BaseEarlyStoppingStrategy
from ax.exceptions.core import UnsupportedError
from ax.utils.common.logger import get_logger
logger: Logger = get_logger(__name__)
[docs]class ThresholdEarlyStoppingStrategy(BaseEarlyStoppingStrategy):
"""Implements the strategy of stopping a trial if its performance
doesn't reach a pre-specified threshold by a certain progression."""
def __init__(
self,
metric_names: Optional[Iterable[str]] = None,
seconds_between_polls: int = 300,
metric_threshold: float = 0.2,
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 ThresholdEarlyStoppingStrategy 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.
metric_threshold: The metric threshold that a trial needs to reach by
min_progression in order not to be stopped.
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,
min_progression=min_progression,
max_progression=max_progression,
min_curves=min_curves,
trial_indices_to_ignore=trial_indices_to_ignore,
normalize_progressions=normalize_progressions,
)
self.metric_threshold = metric_threshold
if metric_names is not None and len(list(metric_names)) > 1:
raise UnsupportedError(
"ThresholdEarlyStoppingStrategy 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,
**kwargs: Dict[str, Any],
) -> Dict[int, Optional[str]]:
"""Stop a trial if its performance doesn't reach a pre-specified threshold
by `min_progression`.
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 {}
df_objective = df[df["metric_name"] == metric_name]
decisions = {
trial_index: self._should_stop_trial_early(
trial_index=trial_index,
experiment=experiment,
df=df_objective,
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,
map_key: str,
minimize: bool,
) -> Tuple[bool, Optional[str]]:
"""Stop a trial if its performance doesn't reach a pre-specified threshold
by `min_progression`.
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 for the objective metric.
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, map_key=map_key
)
if not stopping_eligible:
return False, reason
# threshold early stopping logic
df_trial = df[df["trial_index"] == trial_index].dropna(subset=["mean"])
trial_last_prog = df_trial[map_key].max()
data_last_prog = df_trial[df_trial[map_key] == trial_last_prog]["mean"].iloc[0]
logger.info(
"Early stopping objective at last progression is:\n" f"{data_last_prog}."
)
should_early_stop = (
data_last_prog > self.metric_threshold
if minimize
else data_last_prog < self.metric_threshold
)
comp = "worse" if should_early_stop else "better"
reason = (
f"Trial objective value {data_last_prog} is {comp} than "
f"the metric threshold {self.metric_threshold:}."
)
logger.info(
f"Early stopping decision for {trial_index}: {should_early_stop}. "
f"Reason: {reason}"
)
return should_early_stop, reason
