#!/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
import logging
from abc import ABC, abstractmethod
from collections.abc import Iterable, Sequence
from dataclasses import dataclass
from logging import Logger
import numpy.typing as npt
import pandas as pd
from ax.core.base_trial import TrialStatus
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.map_data import MapData
from ax.core.objective import MultiObjective
from ax.modelbridge.map_torch import MapTorchModelBridge
from ax.modelbridge.modelbridge_utils import (
_unpack_observations,
observation_data_to_array,
observation_features_to_array,
)
from ax.modelbridge.registry import Cont_X_trans, Y_trans
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.map_unit_x import MapUnitX
from ax.models.torch_base import TorchModel
from ax.utils.common.base import Base
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from pyre_extensions import none_throws
logger: Logger = get_logger(__name__)
[docs]
@dataclass
class EarlyStoppingTrainingData:
"""Dataclass for keeping data arrays related to model training and
arm names together.
Args:
X: An `n x d'` array of training features. `d' = d + m`, where `d`
is the dimension of the design space and `m` are the number of map
keys. For the case of learning curves, `m = 1` since we have only
the number of steps as the map key.
Y: An `n x 1` array of training observations.
Yvar: An `n x 1` observed measurement noise.
arm_names: A list of length `n` of arm names. Useful for understanding
which data come from the same arm.
"""
X: npt.NDArray
Y: npt.NDArray
Yvar: npt.NDArray
arm_names: list[str | None]
[docs]
class BaseEarlyStoppingStrategy(ABC, Base):
"""Interface for heuristics that halt trials early, typically based on early
results from that trial."""
def __init__(
self,
metric_names: Iterable[str] | None = None,
seconds_between_polls: int = 300,
min_progression: float | None = None,
max_progression: float | None = None,
min_curves: int | None = None,
trial_indices_to_ignore: list[int] | None = None,
normalize_progressions: bool = False,
) -> None:
"""A BaseEarlyStoppingStrategy class.
Args:
metric_names: The names of the metrics the strategy will interact with.
If no metric names are provided the objective metric is assumed.
seconds_between_polls: How often to poll the early stopping metric to
evaluate whether or not the trial should be early 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`.
"""
if seconds_between_polls < 0:
raise ValueError("`seconds_between_polls may not be less than 0.")
self.metric_names = metric_names
self.seconds_between_polls = seconds_between_polls
self.min_progression = min_progression
self.max_progression = max_progression
self.min_curves = min_curves
self.trial_indices_to_ignore = trial_indices_to_ignore
self.normalize_progressions = normalize_progressions
[docs]
@abstractmethod
def should_stop_trials_early(
self,
trial_indices: set[int],
experiment: Experiment,
) -> dict[int, str | None]:
"""Decide whether to complete trials before evaluation is fully concluded.
Typical examples include stopping a machine learning model's training, or
halting the gathering of samples before some planned number are collected.
Args:
trial_indices: Indices of candidate trials to stop early.
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.
"""
pass # pragma: nocover
def _check_validity_and_get_data(
self, experiment: Experiment, metric_names: list[str]
) -> MapData | None:
"""Validity checks and returns the `MapData` used for early stopping that
is associated with `metric_names`. This function also handles normalizing
progressions.
"""
data = experiment.lookup_data()
if data.df.empty:
logger.info(
f"{self.__class__.__name__} received empty data. "
"Not stopping any trials."
)
return None
for metric_name in metric_names:
if metric_name not in set(data.df["metric_name"]):
logger.info(
f"{self.__class__.__name__} did not receive data from the "
f"objective metric `{metric_name}`. Not stopping any trials."
)
return None
if not isinstance(data, MapData):
logger.info(
f"{self.__class__.__name__} expects MapData, but the "
f"data attached to experiment is of type {type(data)}. "
"Not stopping any trials."
)
return None
data = checked_cast(MapData, data)
map_keys = data.map_keys
if len(list(map_keys)) > 1:
logger.info(
f"{self.__class__.__name__} expects MapData with a single "
"map key, but the data attached to the experiment has multiple: "
f"{data.map_keys}. Not stopping any trials."
)
return None
map_df = data.map_df
# keep only relevant metrics
map_df = map_df[map_df["metric_name"].isin(metric_names)].copy()
if self.normalize_progressions:
for map_key in map_keys:
values = map_df[map_key].astype(float)
map_df[map_key] = values / values.abs().max()
return MapData(
df=map_df,
map_key_infos=data.map_key_infos,
description=data.description,
)
@staticmethod
def _log_and_return_trial_ignored(
logger: logging.Logger, trial_index: int
) -> tuple[bool, str]:
"""Helper function for logging/constructing a reason when a trial
should be ignored."""
logger.info(
f"Trial {trial_index} should be ignored and not considered "
"for early stopping."
)
return False, "Specified as a trial to be ignored for early stopping."
@staticmethod
def _log_and_return_no_data(
logger: logging.Logger, trial_index: int, metric_name: str
) -> tuple[bool, str]:
"""Helper function for logging/constructing a reason when there is no data."""
logger.info(
f"There is not yet any data associated with trial {trial_index} and "
f"metric {metric_name}. Not early stopping this trial."
)
return False, "No data available to make an early stopping decision."
@staticmethod
def _log_and_return_progression_range(
logger: logging.Logger,
trial_index: int,
trial_last_progression: float,
min_progression: float | None,
max_progression: float | None,
metric_name: str,
) -> tuple[bool, str]:
"""Helper function for logging/constructing a reason when min progression
is not yet reached."""
reason = (
f"Most recent progression ({trial_last_progression}) that is available for "
f"metric {metric_name} falls out of the "
f"min/max_progression range ({min_progression}, {max_progression})."
)
logger.info(
f"Trial {trial_index}'s m{reason[1:]} Not early stopping this trial."
)
return False, reason
@staticmethod
def _log_and_return_completed_trials(
logger: logging.Logger, num_completed: int, min_curves: float
) -> tuple[bool, str]:
"""Helper function for logging/constructing a reason when min number of
completed trials is not yet reached."""
logger.info(
f"The number of completed trials ({num_completed}) is less than "
"the minimum number of curves needed for early stopping "
f"({min_curves}). Not early stopping."
)
reason = (
f"Need {min_curves} completed trials, but only {num_completed} "
"completed trials so far."
)
return False, reason
@staticmethod
def _log_and_return_num_trials_with_data(
logger: logging.Logger,
trial_index: int,
trial_last_progression: float,
num_trials_with_data: int,
min_curves: int,
) -> tuple[bool, str]:
"""Helper function for logging/constructing a reason when min number of
trials with data is not yet reached."""
logger.info(
f"The number of trials with data ({num_trials_with_data}) "
f"at trial {trial_index}'s last progression ({trial_last_progression}) "
"is less than the specified minimum number for early stopping "
f"({min_curves}). Not early stopping."
)
reason = (
f"Number of trials with data ({num_trials_with_data}) at "
f"last progression ({trial_last_progression}) is less than the "
f"specified minimum number for early stopping ({min_curves})."
)
return False, reason
[docs]
def is_eligible_any(
self,
trial_indices: set[int],
experiment: Experiment,
df: pd.DataFrame,
map_key: str | None = None,
) -> bool:
"""Perform a series of default checks for a set of trials `trial_indices` and
determine if at least one of them is eligible for further stopping logic:
1. Check that at least `self.min_curves` trials have completed`
2. Check that at least one trial has reached `self.min_progression`
Returns a boolean indicating if all checks are passed.
This is useful for some situations where if no trials are eligible for stopping,
then we can skip costly steps, such as model fitting, that occur before
individual trials are considered for stopping.
"""
# check that there are sufficient completed trials
num_completed = len(experiment.trial_indices_by_status[TrialStatus.COMPLETED])
if self.min_curves is not None and num_completed < self.min_curves:
self._log_and_return_completed_trials(
logger=logger,
num_completed=num_completed,
min_curves=none_throws(self.min_curves),
)
return False
# check that at least one trial has reached `self.min_progression`
df_trials = df[df["trial_index"].isin(trial_indices)].dropna(subset=["mean"])
any_last_prog = 0
if not df_trials[map_key].empty:
any_last_prog = df_trials[map_key].max()
logger.info(
f"Last progression of any candidate for trial stopping is {any_last_prog}."
)
if self.min_progression is not None and any_last_prog < self.min_progression:
logger.info(
f"No trials have reached {self.min_progression}. "
"Not stopping any trials."
)
return False
return True
[docs]
def is_eligible(
self,
trial_index: int,
experiment: Experiment,
df: pd.DataFrame,
map_key: str,
) -> tuple[bool, str | None]:
"""Perform a series of default checks for a specific trial `trial_index` and
determines whether it is eligible for further stopping logic:
1. Check for ignored indices based on `self.trial_indices_to_ignore`
2. Check that `df` contains data for the trial `trial_index`
3. Check that the trial has reached `self.min_progression`
4. Check that the trial hasn't surpassed `self.max_progression`
Returns two elements: a boolean indicating if all checks are passed and a
str indicating the reason that early stopping is not applied (None if all
checks pass).
Args:
trial_index: The index of the trial to check.
experiment: The experiment containing the trial.
df: A dataframe containing the time-dependent metrics for the trial.
NOTE: `df` should only contain data with `metric_name` fields that are
associated with the early stopping strategy. This is usually done
automatically in `_check_validity_and_get_data`. `is_eligible` might
otherwise return False even though the trial is eligible, if there are
secondary tracking metrics that are in `df` but shouldn't be considered
in the early stopping decision.
map_key: The name of the column containing the progression (e.g. time).
Returns:
A tuple of two elements: a boolean indicating if the trial is eligible and
an optional string indicating any reason for ineligiblity.
"""
# check for ignored indices
if (
self.trial_indices_to_ignore is not None
and trial_index in self.trial_indices_to_ignore
):
return self._log_and_return_trial_ignored(
logger=logger, trial_index=trial_index
)
# Check eligibility of each metric.
for metric_name, metric_df in df.groupby("metric_name"):
# check for no data
df_trial = metric_df[metric_df["trial_index"] == trial_index]
df_trial = df_trial.dropna(subset=["mean"])
if df_trial.empty:
return self._log_and_return_no_data(
logger=logger, trial_index=trial_index, metric_name=metric_name
)
# check for min/max progression
trial_last_prog = df_trial[map_key].max()
logger.info(
f"Last progression of Trial {trial_index} is {trial_last_prog}."
)
if (
self.min_progression is not None
and trial_last_prog < self.min_progression
) or (
self.max_progression is not None
and trial_last_prog > self.max_progression
):
return self._log_and_return_progression_range(
logger=logger,
trial_index=trial_index,
trial_last_progression=trial_last_prog,
min_progression=self.min_progression,
max_progression=self.max_progression,
metric_name=metric_name,
)
return True, None
def _default_objective_and_direction(
self, experiment: Experiment
) -> tuple[str, bool]:
if self.metric_names is None:
optimization_config = none_throws(experiment.optimization_config)
objective = optimization_config.objective
# if multi-objective optimization, infer as first objective
# although it is recommended to specify a metric name(s) explicitly.
if isinstance(objective, MultiObjective):
objective = objective.objectives[0]
metric_name = objective.metric.name
minimize = objective.minimize
else:
metric_name = list(self.metric_names)[0]
minimize = experiment.metrics[metric_name].lower_is_better or False
return metric_name, minimize
[docs]
class ModelBasedEarlyStoppingStrategy(BaseEarlyStoppingStrategy):
"""A base class for model based early stopping strategies. Includes
a helper function for processing MapData into arrays."""
def __init__(
self,
metric_names: Iterable[str] | None = None,
seconds_between_polls: int = 300,
min_progression: float | None = None,
max_progression: float | None = None,
min_curves: int | None = None,
trial_indices_to_ignore: list[int] | None = None,
normalize_progressions: bool = False,
min_progression_modeling: float | None = None,
) -> None:
"""A ModelBasedEarlyStoppingStrategy class.
Args:
metric_names: The names of the metrics the strategy will interact with.
If no metric names are provided the objective metric is assumed.
seconds_between_polls: How often to poll the early stopping metric to
evaluate whether or not the trial should be early 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`.
min_progression_modeling: If set, this will exclude progressions that are
below this threshold from being used or modeling. Useful when early data
is not reliable or excessively noisy.
"""
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.min_progression_modeling = min_progression_modeling
def _check_validity_and_get_data(
self, experiment: Experiment, metric_names: list[str]
) -> MapData | None:
"""Validity checks and returns the `MapData` used for early stopping that
is associated with `metric_names`. This function also handles normalizing
progressions.
"""
map_data = super()._check_validity_and_get_data(
experiment=experiment, metric_names=metric_names
)
if map_data is not None and self.min_progression_modeling is not None:
map_df = map_data.map_df
map_df = map_df[
map_df[map_data.map_keys[0]] >= self.min_progression_modeling
]
map_data = MapData(
df=map_df,
map_key_infos=map_data.map_key_infos,
description=map_data.description,
)
return map_data
[docs]
def get_training_data(
self,
experiment: Experiment,
map_data: MapData,
max_training_size: int | None = None,
outcomes: Sequence[str] | None = None,
parameters: list[str] | None = None,
) -> EarlyStoppingTrainingData:
"""Processes the raw (untransformed) training data into arrays for
use in modeling. The trailing dimensions of `X` are the map keys, in
their originally specified order from `map_data`.
Args:
experiment: Experiment that contains the data.
map_data: The MapData from the experiment, as can be obtained by
via `_check_validity_and_get_data`.
max_training_size: Subsample the learning curve to keep the total
number of data points less than this threshold. Passed to
MapData's `subsample` method as `limit_rows_per_metric`.
Returns:
An `EarlyStoppingTrainingData` that contains training data arrays X, Y,
and Yvar + a list of arm names.
"""
if max_training_size is not None:
map_data = map_data.subsample(
map_key=map_data.map_keys[0],
limit_rows_per_metric=max_training_size,
)
if outcomes is None:
# default to the default objective
metric_name, _ = self._default_objective_and_direction(
experiment=experiment
)
outcomes = [metric_name]
if parameters is None:
parameters = list(experiment.search_space.parameters.keys())
parameters = parameters + list(map_data.map_keys)
# helper Ax model to make use of its transform functionality
transform_model = get_transform_helper_model(
experiment=experiment, data=map_data
)
observations_raw = transform_model.get_training_data()
observations, _ = transform_model._transform_data(
observations=observations_raw,
search_space=transform_model._model_space,
transforms=transform_model._raw_transforms,
transform_configs=None,
)
obs_features, obs_data, arm_names = _unpack_observations(observations)
X = observation_features_to_array(parameters=parameters, obsf=obs_features)
Y, Yvar = observation_data_to_array(
outcomes=list(outcomes), observation_data=obs_data
)
return EarlyStoppingTrainingData(X=X, Y=Y, Yvar=Yvar, arm_names=arm_names)
