#!/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 __future__ import annotations
import warnings
from collections import defaultdict, OrderedDict
from collections.abc import MutableMapping
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from logging import Logger
from typing import Optional, TYPE_CHECKING, Union
import numpy as np
from ax.core.arm import Arm
from ax.core.base_trial import BaseTrial
from ax.core.data import Data
from ax.core.generator_run import ArmWeight, GeneratorRun, GeneratorRunType
from ax.core.trial import immutable_once_run
from ax.core.types import (
TCandidateMetadata,
TEvaluationOutcome,
validate_evaluation_outcome,
)
from ax.exceptions.core import AxError, UnsupportedError, UserInputError
from ax.utils.common.base import SortableBase
from ax.utils.common.docutils import copy_doc
from ax.utils.common.equality import datetime_equals, equality_typechecker
from ax.utils.common.logger import _round_floats_for_logging, get_logger
from ax.utils.common.typeutils import checked_cast, not_none
logger: Logger = get_logger(__name__)
if TYPE_CHECKING:
# import as module to make sphinx-autodoc-typehints happy
from ax import core # noqa F401
BATCH_TRIAL_RAW_DATA_FORMAT_ERROR_MESSAGE = (
"Raw data must be a dict for batched trials."
)
[docs]class LifecycleStage(int, Enum):
EXPLORATION = 0
ITERATION = 1
BAKEOFF = 2
OFFLINE_OPTIMIZED = 3
EXPLORATION_CONCURRENT = 4
[docs]@dataclass
class AbandonedArm(SortableBase):
"""Class storing metadata of arm that has been abandoned within
a BatchTrial.
"""
name: str
time: datetime
reason: Optional[str] = None
@equality_typechecker
def __eq__(self, other: AbandonedArm) -> bool:
return (
self.name == other.name
and self.reason == other.reason
and datetime_equals(self.time, other.time)
)
@property
def _unique_id(self) -> str:
return self.name
[docs]@dataclass
class GeneratorRunStruct(SortableBase):
"""Stores GeneratorRun object as well as the weight with which it was added."""
generator_run: GeneratorRun
weight: float
@property
def _unique_id(self) -> str:
return self.generator_run._unique_id + ":" + str(self.weight)
[docs]class BatchTrial(BaseTrial):
"""Batched trial that has multiple attached arms, meant to be
*deployed and evaluated together*, and possibly arm weights, which are
a measure of how much of the total resources allocated to evaluating
a batch should go towards evaluating the specific arm. For instance,
for field experiments the weights could describe the fraction of the
total experiment population assigned to the different treatment arms.
Interpretation of the weights is defined in Runner.
NOTE: A `BatchTrial` is not just a trial with many arms; it is a trial,
for which it is important that the arms are evaluated simultaneously, e.g.
in an A/B test where the evaluation results are subject to nonstationarity.
For cases where multiple arms are evaluated separately and independently of
each other, use multiple `Trial` objects with a single arm each.
Args:
experiment: Experiment, to which this trial is attached
generator_run: GeneratorRun, associated with this trial. This can a
also be set later through `add_arm` or `add_generator_run`, but a
trial's associated generator run is immutable once set.
generator_runs: GeneratorRuns, associated with this trial. This can a
also be set later through `add_arm` or `add_generator_run`, but a
trial's associated generator run is immutable once set. This cannot
be combined with the `generator_run` argument.
trial_type: Type of this trial, if used in MultiTypeExperiment.
optimize_for_power: Whether to optimize the weights of arms in this
trial such that the experiment's power to detect effects of
certain size is as high as possible. Refer to documentation of
`BatchTrial.set_status_quo_and_optimize_power` for more detail.
ttl_seconds: If specified, trials will be considered failed after
this many seconds since the time the trial was ran, unless the
trial is completed before then. Meant to be used to detect
'dead' trials, for which the evaluation process might have
crashed etc., and which should be considered failed after
their 'time to live' has passed.
index: If specified, the trial's index will be set accordingly.
This should generally not be specified, as in the index will be
automatically determined based on the number of existing trials.
This is only used for the purpose of loading from storage.
lifecycle_stage: The stage of the experiment lifecycle that this
trial represents
"""
def __init__(
self,
experiment: core.experiment.Experiment,
generator_run: Optional[GeneratorRun] = None,
generator_runs: Optional[list[GeneratorRun]] = None,
trial_type: Optional[str] = None,
optimize_for_power: Optional[bool] = False,
ttl_seconds: Optional[int] = None,
index: Optional[int] = None,
lifecycle_stage: Optional[LifecycleStage] = None,
) -> None:
super().__init__(
experiment=experiment,
trial_type=trial_type,
ttl_seconds=ttl_seconds,
index=index,
)
self._arms_by_name: dict[str, Arm] = {}
self._generator_run_structs: list[GeneratorRunStruct] = []
self._abandoned_arms_metadata: dict[str, AbandonedArm] = {}
self._status_quo: Optional[Arm] = None
self._status_quo_weight_override: Optional[float] = None
if generator_run is not None:
if generator_runs is not None:
raise UnsupportedError(
"Cannot specify both `generator_run` and `generator_runs`."
)
self.add_generator_run(generator_run=generator_run)
elif generator_runs is not None:
for gr in generator_runs:
self.add_generator_run(generator_run=gr)
self.optimize_for_power = optimize_for_power
status_quo = experiment.status_quo
if optimize_for_power:
if status_quo is None:
raise ValueError(
"Can only optimize for power if experiment has a status quo."
)
self.set_status_quo_and_optimize_power(status_quo=status_quo)
else:
# Set the status quo for tracking purposes
# It will not be included in arm_weights
self._status_quo = status_quo
# Trial status quos are stored in the DB as a generator run
# with one arm; thus we need to store two `db_id` values
# for this object instead of one
self._status_quo_generator_run_db_id: Optional[int] = None
self._status_quo_arm_db_id: Optional[int] = None
self._lifecycle_stage = lifecycle_stage
@property
def experiment(self) -> core.experiment.Experiment:
"""The experiment this batch belongs to."""
return self._experiment
@property
def index(self) -> int:
"""The index of this batch within the experiment's batch list."""
return self._index
@property
def generator_run_structs(self) -> list[GeneratorRunStruct]:
"""List of generator run structs attached to this trial.
Struct holds generator_run object and the weight with which it was added.
"""
return self._generator_run_structs
@property
def arm_weights(self) -> MutableMapping[Arm, float]:
"""The set of arms and associated weights for the trial.
These are constructed by merging the arms and weights from
each generator run that is attached to the trial.
"""
arm_weights = OrderedDict()
if len(self._generator_run_structs) == 0 and self.status_quo is None:
return arm_weights
for struct in self._generator_run_structs:
multiplier = struct.weight
for arm, weight in struct.generator_run.arm_weights.items():
scaled_weight = weight * multiplier
if arm in arm_weights:
arm_weights[arm] += scaled_weight
else:
arm_weights[arm] = scaled_weight
if self.status_quo is not None and self._status_quo_weight_override is not None:
# If override is specified, this is the weight the status quo gets,
# regardless of whether it appeared in any generator runs.
# If no override is specified, status quo does not appear in arm_weights.
arm_weights[self.status_quo] = self._status_quo_weight_override
return arm_weights
@property
def lifecycle_stage(self) -> Optional[LifecycleStage]:
return self._lifecycle_stage
@arm_weights.setter
def arm_weights(self, arm_weights: MutableMapping[Arm, float]) -> None:
raise NotImplementedError("Use `trial.add_arms_and_weights`")
@immutable_once_run
def add_arm(self, arm: Arm, weight: float = 1.0) -> BatchTrial:
"""Add a arm to the trial.
Args:
arm: The arm to be added.
weight: The weight with which this arm should be added.
Returns:
The trial instance.
"""
return self.add_arms_and_weights(arms=[arm], weights=[weight])
@immutable_once_run
def add_arms_and_weights(
self,
arms: list[Arm],
weights: Optional[list[float]] = None,
multiplier: float = 1.0,
) -> BatchTrial:
"""Add arms and weights to the trial.
Args:
arms: The arms to be added.
weights: The weights associated with the arms.
multiplier: The multiplier applied to input weights before merging with
the current set of arms and weights.
Returns:
The trial instance.
"""
return self.add_generator_run(
generator_run=GeneratorRun(
arms=arms, weights=weights, type=GeneratorRunType.MANUAL.name
),
multiplier=multiplier,
)
@immutable_once_run
def add_generator_run(
self, generator_run: GeneratorRun, multiplier: float = 1.0
) -> BatchTrial:
"""Add a generator run to the trial.
The arms and weights from the generator run will be merged with
the existing arms and weights on the trial, and the generator run
object will be linked to the trial for tracking.
Args:
generator_run: The generator run to be added.
multiplier: The multiplier applied to input weights before merging with
the current set of arms and weights.
Returns:
The trial instance.
"""
# First validate generator run arms
for arm in generator_run.arms:
self.experiment.search_space.check_types(arm.parameters, raise_error=True)
# Clone arms to avoid mutating existing state
generator_run._arm_weight_table = OrderedDict(
{
arm_sig: ArmWeight(arm_weight.arm.clone(), arm_weight.weight)
for arm_sig, arm_weight in generator_run._arm_weight_table.items()
}
)
# Add names to arms
# For those not yet added to this experiment, create a new name
# Else, use the name of the existing arm
for arm in generator_run.arms:
self._check_existing_and_name_arm(arm)
self._generator_run_structs.append(
GeneratorRunStruct(generator_run=generator_run, weight=multiplier)
)
generator_run.index = len(self._generator_run_structs) - 1
if self.status_quo is not None and self.optimize_for_power:
self.set_status_quo_and_optimize_power(status_quo=not_none(self.status_quo))
if generator_run._generation_step_index is not None:
self._set_generation_step_index(
generation_step_index=generator_run._generation_step_index
)
self._refresh_arms_by_name()
return self
@property
def status_quo(self) -> Optional[Arm]:
"""The control arm for this batch."""
return self._status_quo
@status_quo.setter
def status_quo(self, status_quo: Optional[Arm]) -> None:
raise NotImplementedError(
"Use `set_status_quo_with_weight` or "
"`set_status_quo_and_optimize_power` "
"to set the status quo arm."
)
[docs] def unset_status_quo(self) -> None:
"""Set the status quo to None."""
self._status_quo = None
self._status_quo_weight_override = None
self._refresh_arms_by_name()
@immutable_once_run
def set_status_quo_with_weight(
self, status_quo: Arm, weight: Optional[float]
) -> BatchTrial:
"""Sets status quo arm with given weight. This weight *overrides* any
weight the status quo has from generator runs attached to this batch.
Thus, this function is not the same as using add_arm, which will
result in the weight being additive over all generator runs.
"""
# Assign a name to this arm if none exists
if weight is not None:
if weight <= 0.0:
raise ValueError("Status quo weight must be positive.")
if status_quo is None:
raise ValueError("Cannot set weight because status quo is not defined.")
if status_quo is not None:
self.experiment.search_space.check_types(
status_quo.parameters, raise_error=True
)
self.experiment._name_and_store_arm_if_not_exists(
arm=status_quo,
proposed_name="status_quo_" + str(self.index),
replace=True,
)
self._status_quo = status_quo.clone() if status_quo is not None else None
self._status_quo_weight_override = weight
self._refresh_arms_by_name()
return self
@immutable_once_run
def set_status_quo_and_optimize_power(self, status_quo: Arm) -> BatchTrial:
"""Adds a status quo arm to the batch and optimizes for power.
NOTE: this optimization based on the arms that are currently attached
to the batch. If you add more arms later, you should re-run this function.
If you want the optimization to happen automatically,
set batch.optimize_for_power = True.
This function will maximize power across the multiple pair-wise
comparisons of existing arms against the status_quo.
Specifically, this function assigns sqrt(sum_weights) weight to the
status quo, where sum_weights is the sum of the weights of the existing
arms, excluding the status quo. This will be optimal in terms of
statistical power in the case where:
1) status quo is the only arm to compare against,
2) all other arms are of equal interest.
"""
status_quo_is_only_arm = len(self.arms) == 1 and self._status_quo is not None
if len(self.arms) == 0 or status_quo_is_only_arm:
# If status quo is the only arm, just set its weight to 1
# Can't use logic below, because it will choose 0
self.set_status_quo_with_weight(status_quo=status_quo, weight=1)
return self
# arm_weights should always have at least one arm now
arm_weights = not_none(self.arm_weights)
sum_weights = sum(w for arm, w in arm_weights.items() if arm != status_quo)
optimal_status_quo_weight_override = np.sqrt(sum_weights)
self.set_status_quo_with_weight(
status_quo=status_quo, weight=optimal_status_quo_weight_override
)
return self
@property
def arms(self) -> list[Arm]:
"""All arms contained in the trial."""
arm_weights = self.arm_weights
return [] if arm_weights is None else list(arm_weights.keys())
@property
def weights(self) -> list[float]:
"""Weights corresponding to arms contained in the trial."""
arm_weights = self.arm_weights
return [] if arm_weights is None else list(arm_weights.values())
@property
def arms_by_name(self) -> dict[str, Arm]:
"""Map from arm name to object for all arms in trial."""
return self._arms_by_name
def _refresh_arms_by_name(self) -> None:
self._arms_by_name = {}
for arm in self.arms:
if not arm.has_name:
raise ValueError("Arms attached to a trial must have a name.")
self._arms_by_name[arm.name] = arm
@property
def abandoned_arms(self) -> list[Arm]:
"""List of arms that have been abandoned within this trial."""
return [
self.arms_by_name[arm.name]
for arm in self._abandoned_arms_metadata.values()
]
@property
def abandoned_arm_names(self) -> set[str]:
"""Set of names of arms that have been abandoned within this trial."""
return set(self._abandoned_arms_metadata.keys())
@property
def in_design_arms(self) -> list[Arm]:
return [
arm
for arm in self.arms
if self.experiment.search_space.check_membership(arm.parameters)
]
# pyre-ignore[6]: T77111662.
@copy_doc(BaseTrial.generator_runs)
@property
def generator_runs(self) -> list[GeneratorRun]:
return [grs.generator_run for grs in self.generator_run_structs]
@property
def abandoned_arms_metadata(self) -> list[AbandonedArm]:
return list(self._abandoned_arms_metadata.values())
@property
def is_factorial(self) -> bool:
"""Return true if the trial's arms are a factorial design with
no linked factors.
"""
# To match the model behavior, this should probably actually be pulled
# from exp.parameters. However, that seems rather ugly when this function
# intuitively should just depend on the arms.
sufficient_factors = all(len(arm.parameters or []) >= 2 for arm in self.arms)
if not sufficient_factors:
return False
param_levels: defaultdict[str, dict[Union[str, float], int]] = defaultdict(dict)
for arm in self.arms:
for param_name, param_value in arm.parameters.items():
param_levels[param_name][not_none(param_value)] = 1
param_cardinality = 1
for param_values in param_levels.values():
param_cardinality *= len(param_values)
return len(self.arms) == param_cardinality
[docs] def run(self) -> BatchTrial:
return checked_cast(BatchTrial, super().run())
[docs] def normalized_arm_weights(
self, total: float = 1, trunc_digits: Optional[int] = None
) -> MutableMapping[Arm, float]:
"""Returns arms with a new set of weights normalized
to the given total.
This method is useful for many runners where we need to normalize weights
to a certain total without mutating the weights attached to a trial.
Args:
total: The total weight to which to normalize.
Default is 1, in which case arm weights
can be interpreted as probabilities.
trunc_digits: The number of digits to keep. If the
resulting total weight is not equal to `total`, re-allocate
weight in such a way to maintain relative weights as best as
possible.
Returns:
Mapping from arms to the new set of weights.
"""
weights = np.array(self.weights)
if trunc_digits is not None:
atomic_weight = 10**-trunc_digits
# pyre-fixme[16]: `float` has no attribute `astype`.
int_weights = (
(total / atomic_weight) * (weights / np.sum(weights))
).astype(int)
n_leftover = int(total / atomic_weight) - np.sum(int_weights)
int_weights[:n_leftover] += 1
weights = int_weights * atomic_weight
else:
weights = weights * (total / np.sum(weights))
return OrderedDict(zip(self.arms, weights))
[docs] def mark_arm_abandoned(
self, arm_name: str, reason: Optional[str] = None
) -> BatchTrial:
"""Mark a arm abandoned.
Usually done after deployment when one arm causes issues but
user wants to continue running other arms in the batch.
NOTE: Abandoned arms are considered to be 'pending points' in
experiment after their abandonment to avoid Ax models suggesting
the same arm again as a new candidate. Abandoned arms are also
excluded from model training data unless ``fit_abandoned``
option is specified to model bridge.
Args:
arm_name: The name of the arm to abandon.
reason: The reason for abandoning the arm.
Returns:
The batch instance.
"""
if arm_name not in self.arms_by_name:
raise ValueError("Arm must be contained in batch.")
abandoned_arm = AbandonedArm(name=arm_name, time=datetime.now(), reason=reason)
self._abandoned_arms_metadata[arm_name] = abandoned_arm
return self
[docs] def clone(self) -> BatchTrial:
"""Clone the trial and attach it to the current experiment."""
warnings.warn(
"clone() method is getting deprecated. Please use clone_to() instead.",
DeprecationWarning,
stacklevel=3,
)
return self.clone_to(include_sq=False)
[docs] def clone_to(
self,
experiment: Optional[core.experiment.Experiment] = None,
include_sq: bool = True,
) -> BatchTrial:
"""Clone the trial and attach it to a specified experiment.
If None provided, attach it to the current experiment.
Args:
experiment: The experiment to which the cloned trial will belong.
If unspecified, uses the current experiment.
include_sq: Whether to include status quo in the cloned trial.
Returns:
A new instance of the trial.
"""
use_old_experiment = experiment is None
experiment = self._experiment if experiment is None else experiment
new_trial = experiment.new_batch_trial(
trial_type=self._trial_type, ttl_seconds=self._ttl_seconds
)
for struct in self._generator_run_structs:
if use_old_experiment:
# don't clone gen run in case we are attaching cloned trial to
# the same experiment
new_trial.add_generator_run(struct.generator_run, struct.weight)
else:
new_trial.add_generator_run(struct.generator_run.clone(), struct.weight)
if (self._status_quo is not None) and include_sq:
sq_weight = self._status_quo_weight_override
new_trial.set_status_quo_with_weight(
self._status_quo.clone(),
weight=sq_weight,
)
self._update_trial_attrs_on_clone(new_trial=new_trial)
return new_trial
[docs] def attach_batch_trial_data(
self,
raw_data: dict[str, TEvaluationOutcome],
sample_sizes: Optional[dict[str, int]] = None,
metadata: Optional[dict[str, Union[str, int]]] = None,
) -> None:
"""Attaches data to the trial
Args:
raw_data: Map from arm name to metric outcomes.
sample_sizes: Dict from arm name to sample size.
metadata: Additional metadata to track about this run.
importantly the start_date and end_date
complete_trial: Whether to mark trial as complete after
attaching data. Defaults to False.
"""
# Validate type of raw_data
if not isinstance(raw_data, dict):
raise ValueError(BATCH_TRIAL_RAW_DATA_FORMAT_ERROR_MESSAGE)
for key, value in raw_data.items():
if not isinstance(key, str):
raise ValueError(BATCH_TRIAL_RAW_DATA_FORMAT_ERROR_MESSAGE)
try:
validate_evaluation_outcome(outcome=value)
except TypeError:
raise ValueError(BATCH_TRIAL_RAW_DATA_FORMAT_ERROR_MESSAGE)
# Format the data to save.
not_trial_arm_names = set(raw_data.keys()) - set(self.arms_by_name.keys())
if not_trial_arm_names:
raise UserInputError(
f"Arms {not_trial_arm_names} are not part of trial #{self.index}."
)
evaluations, data = self._make_evaluations_and_data(
raw_data=raw_data, metadata=metadata, sample_sizes=sample_sizes
)
self._validate_batch_trial_data(data=data)
self._run_metadata = self._run_metadata if metadata is None else metadata
self.experiment.attach_data(data)
data_for_logging = _round_floats_for_logging(item=evaluations)
logger.info(
f"Updated trial {self.index} with data: "
f"{_round_floats_for_logging(item=data_for_logging)}."
)
def __repr__(self) -> str:
return (
"BatchTrial("
f"experiment_name='{self._experiment._name}', "
f"index={self._index}, "
f"status={self._status})"
)
def _get_candidate_metadata_from_all_generator_runs(
self,
) -> dict[str, TCandidateMetadata]:
"""Retrieves combined candidate metadata from all generator runs on this
batch trial in the form of { arm name -> candidate metadata} mapping.
NOTE: this does not handle the case of the same arm appearing in multiple
generator runs in the same trial: metadata from only one of the generator
runs containing the arm will be retrieved.
"""
cand_metadata = {}
for gr_struct in self._generator_run_structs:
gr = gr_struct.generator_run
if gr.candidate_metadata_by_arm_signature:
gr_cand_metadata = gr.candidate_metadata_by_arm_signature
warn = False
for arm in gr.arms:
if arm.name in cand_metadata:
warn = True
if gr_cand_metadata:
# Reformat the mapping to be by arm name, since arm signature
# is not stored in Ax data.
cand_metadata[arm.name] = gr_cand_metadata.get(arm.signature)
if warn:
logger.warning(
"The same arm appears in multiple generator runs in batch "
f"{self.index}. Candidate metadata will only contain metadata "
"for one of those generator runs, and the candidate metadata "
"for the arm from another generator run will not be propagated."
)
return cand_metadata
def _get_candidate_metadata(self, arm_name: str) -> TCandidateMetadata:
"""Retrieves candidate metadata for a specific arm."""
try:
arm = self.arms_by_name[arm_name]
except KeyError:
raise ValueError(
f"Arm by name {arm_name} is not part of trial #{self.index}."
)
for gr_struct in self._generator_run_structs:
gr = gr_struct.generator_run
if gr and gr.candidate_metadata_by_arm_signature and arm in gr.arms:
return not_none(gr.candidate_metadata_by_arm_signature).get(
arm.signature
)
return None
def _validate_batch_trial_data(self, data: Data) -> None:
"""Utility function to validate batch data before further processing."""
if (
self.status_quo
and not_none(self.status_quo).name in self.arms_by_name
and not_none(self.status_quo).name not in data.df["arm_name"].values
):
raise AxError(
f"Trial #{self.index} was completed with data that did "
"not contain status quo observations, but the trial has "
"status quo set and therefore data for it is required."
)
for metric_name in data.df["metric_name"].values:
if metric_name not in self.experiment.metrics:
logger.info(
f"Data was logged for metric {metric_name} that was not yet "
"tracked on the experiment. Please specify `tracking_metric_"
"names` argument in AxClient.create_experiment to add tracking "
"metrics to the experiment. Without those, all data users "
"specify is still attached to the experiment, but will not be "
"fetched in `experiment.fetch_data()`, but you can still use "
"`experiment.lookup_data_for_trial` to get all attached data."
)
