Source code for ax.early_stopping.strategies.threshold

#!/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 collections.abc import Iterable
from logging import Logger

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: Iterable[str] | None = None, seconds_between_polls: int = 300, metric_threshold: float = 0.2, min_progression: float | None = 10, max_progression: float | None = None, min_curves: int | None = 5, trial_indices_to_ignore: list[int] | None = 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 worse 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, ) -> dict[int, str | None]: """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, str | None]: """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