#!/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.
from __future__ import annotations
from dataclasses import dataclass, field
from logging import Logger
from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple, Union
# Module-level import to avoid circular dependency b/w this file and
# generation_strategy.py
from ax import modelbridge
from ax.core.arm import Arm
from ax.core.base_trial import TrialStatus
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun
from ax.core.observation import ObservationFeatures
from ax.core.optimization_config import OptimizationConfig
from ax.core.search_space import SearchSpace
from ax.exceptions.core import UserInputError
from ax.exceptions.generation_strategy import GenerationStrategyRepeatedPoints
from ax.modelbridge.base import ModelBridge
from ax.modelbridge.cross_validation import BestModelSelector, CVDiagnostics, CVResult
from ax.modelbridge.model_spec import FactoryFunctionModelSpec, ModelSpec
from ax.modelbridge.registry import ModelRegistryBase
from ax.modelbridge.transition_criterion import (
MaxGenerationParallelism,
MaxTrials,
MinTrials,
TransitionCriterion,
TrialBasedCriterion,
)
from ax.utils.common.base import Base, SortableBase
from ax.utils.common.logger import get_logger
from ax.utils.common.serialization import SerializationMixin
from ax.utils.common.typeutils import not_none
logger: Logger = get_logger(__name__)
TModelFactory = Callable[..., ModelBridge]
CANNOT_SELECT_ONE_MODEL_MSG = """\
Base `GenerationNode` does not implement selection among fitted \
models, so exactly one `ModelSpec` must be specified when using \
`GenerationNode._pick_fitted_model_to_gen_from` (usually called \
by `GenerationNode.gen`.
"""
MAX_GEN_DRAWS = 5
MAX_GEN_DRAWS_EXCEEDED_MESSAGE = (
f"GenerationStrategy exceeded `MAX_GEN_DRAWS` of {MAX_GEN_DRAWS} while trying to "
"generate a unique parameterization. This indicates that the search space has "
"likely been fully explored, or that the sweep has converged."
)
[docs]class GenerationNode(SerializationMixin, SortableBase):
"""Base class for GenerationNode, capable of fitting one or more model specs under
the hood and generating candidates from them.
Args:
model_specs: A list of ModelSpecs to be selected from for generation in this
GenerationNode
should_deduplicate: Whether to deduplicate the parameters of proposed arms
against those of previous arms via rejection sampling. If this is True,
the GenerationStrategy will discard generator runs produced from the
GenerationNode that has `should_deduplicate=True` if they contain arms
already present on the experiment and replace them with new generator runs.
If no generator run with entirely unique arms could be produced in 5
attempts, a `GenerationStrategyRepeatedPoints` error will be raised, as we
assume that the optimization converged when the model can no longer suggest
unique arms.
node_name: A unique name for the GenerationNode. Used for storage purposes.
transition_criteria: List of TransitionCriterion, each of which describes a
condition that must be met before completing a GenerationNode. All `is_met`
must evaluateTrue for the GenerationStrategy to move on to the next
GenerationNode.
gen_unlimited_trials: If True the number of trials that can be generated from
this GenerationNode is unlimited.
Note for developers: by "model" here we really mean an Ax ModelBridge object, which
contains an Ax Model under the hood. We call it "model" here to simplify and focus
on explaining the logic of GenerationStep and GenerationStrategy.
"""
# Required options:
model_specs: List[ModelSpec]
# TODO: Move `should_deduplicate` to `ModelSpec` if possible, and make optional
should_deduplicate: bool
_node_name: str
_gen_unlimited_trials: bool = True
# Optional specifications
_model_spec_to_gen_from: Optional[ModelSpec] = None
# TODO: @mgarrard should this be a dict criterion_class name -> criterion mapping?
_transition_criteria: Optional[Sequence[TransitionCriterion]]
# [TODO] Handle experiment passing more eloquently by enforcing experiment
# attribute is set in generation strategies class
_generation_strategy: Optional[
modelbridge.generation_strategy.GenerationStrategy
] = None
def __init__(
self,
node_name: str,
model_specs: List[ModelSpec],
best_model_selector: Optional[BestModelSelector] = None,
should_deduplicate: bool = False,
transition_criteria: Optional[Sequence[TransitionCriterion]] = None,
gen_unlimited_trials: bool = True,
) -> None:
self._node_name = node_name
# While `GenerationNode` only handles a single `ModelSpec` in the `gen`
# and `_pick_fitted_model_to_gen_from` methods, we validate the
# length of `model_specs` in `_pick_fitted_model_to_gen_from` in order
# to not require all `GenerationNode` subclasses to override an `__init__`
# method to bypass that validation.
self.model_specs = model_specs
self.best_model_selector = best_model_selector
self.should_deduplicate = should_deduplicate
self._transition_criteria = transition_criteria
self._gen_unlimited_trials = gen_unlimited_trials
@property
def node_name(self) -> str:
"""Returns the unique name of this GenerationNode"""
return self._node_name
@property
def model_spec_to_gen_from(self) -> ModelSpec:
"""Returns the cached `_model_spec_to_gen_from` or gets it from
`_pick_fitted_model_to_gen_from` and then caches and returns it
"""
if self._model_spec_to_gen_from is not None:
return self._model_spec_to_gen_from
self._model_spec_to_gen_from = self._pick_fitted_model_to_gen_from()
return self._model_spec_to_gen_from
@property
def model_enum(self) -> ModelRegistryBase:
"""model_enum from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.model_enum
@property
def model_kwargs(self) -> Optional[Dict[str, Any]]:
"""model_kwargs from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.model_kwargs
@property
def model_gen_kwargs(self) -> Optional[Dict[str, Any]]:
"""model_gen_kwargs from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.model_gen_kwargs
@property
def model_cv_kwargs(self) -> Optional[Dict[str, Any]]:
"""model_cv_kwargs from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.model_cv_kwargs
@property
def fitted_model(self) -> ModelBridge:
"""fitted_model from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.fitted_model
@property
def fixed_features(self) -> Optional[ObservationFeatures]:
"""fixed_features from self.model_spec_to_gen_from for convenience"""
if len({model_spec.fixed_features for model_spec in self.model_specs}) == 1:
return self.model_specs[0].fixed_features
return self.model_spec_to_gen_from.fixed_features
@property
def cv_results(self) -> Optional[List[CVResult]]:
"""cv_results from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.cv_results
@property
def diagnostics(self) -> Optional[CVDiagnostics]:
"""diagnostics from self.model_spec_to_gen_from for convenience"""
return self.model_spec_to_gen_from.diagnostics
@property
def model_to_gen_from_name(self) -> Optional[str]:
"""Returns the name of the model that will be used for gen, if there is one.
Otherwise, returns None.
"""
return self.model_spec_to_gen_from.model_key
@property
def generation_strategy(self) -> modelbridge.generation_strategy.GenerationStrategy:
"""Returns a backpointer to the GenerationStrategy, useful for obtaining the
experiment associated with this GenerationStrategy"""
# TODO: @mgarrard remove this property once we make experiment a required
# arguement on GenerationStrategy
if self._generation_strategy is None:
raise ValueError(
"Generation strategy has not been initialized on this node."
)
return not_none(self._generation_strategy)
@property
def transition_criteria(self) -> Sequence[TransitionCriterion]:
"""Returns the sequence of TransitionCriteria that will be used to determine
if this GenerationNode is complete and should transition to the next node.
"""
return [] if self._transition_criteria is None else self._transition_criteria
@property
def experiment(self) -> Experiment:
"""Returns the experiment associated with this GenerationStrategy"""
return self.generation_strategy.experiment
@property
def gen_unlimited_trials(self) -> bool:
"""If True, this GenerationNode can generate unlimited trials."""
return self._gen_unlimited_trials
@property
def is_completed(self) -> bool:
"""Returns True if this GenerationNode is complete and should transition to
the next node.
"""
return self.should_transition_to_next_node(raise_data_required_error=False)[0]
@property
def _unique_id(self) -> str:
"""Returns a unique id for this GenerationNode"""
return self.node_name
@property
def _fitted_model(self) -> Optional[ModelBridge]:
"""Private property to return optional fitted_model from
self.model_spec_to_gen_from for convenience. If no model is fit,
will return None. If using the non-private `fitted_model` property,
and no model is fit, a UserInput error will be raised.
"""
return self.model_spec_to_gen_from._fitted_model
[docs] def fit(
self,
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
optimization_config: Optional[OptimizationConfig] = None,
**kwargs: Any,
) -> None:
"""Fits the specified models to the given experiment + data using
the model kwargs set on each corresponding model spec and the kwargs
passed to this method.
NOTE: Local kwargs take precedence over the ones stored in
``ModelSpec.model_kwargs``.
"""
self._model_spec_to_gen_from = None
for model_spec in self.model_specs:
model_spec.fit( # Stores the fitted model as `model_spec._fitted_model`
experiment=experiment,
data=data,
search_space=search_space,
optimization_config=optimization_config,
**kwargs,
)
[docs] def gen(
self,
n: Optional[int] = None,
pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None,
max_gen_draws_for_deduplication: int = MAX_GEN_DRAWS,
arms_by_signature_for_deduplication: Optional[Dict[str, Arm]] = None,
**model_gen_kwargs: Any,
) -> GeneratorRun:
"""Picks a fitted model, from which to generate candidates (via
``self._pick_fitted_model_to_gen_from``) and generates candidates
from it. Uses the ``model_gen_kwargs`` set on the selected ``ModelSpec``
alongside any kwargs passed in to this function (with local kwargs)
taking precedent.
Args:
n: Optional nteger representing how many arms should be in the generator
run produced by this method. When this is ``None``, ``n`` will be
determined by the ``ModelSpec`` that we are generating from.
pending_observations: A map from metric name to pending
observations for that metric, used by some models to avoid
resuggesting points that are currently being evaluated.
max_gen_draws_for_deduplication: TODO
model_gen_kwargs: Keyword arguments, passed through to ``ModelSpec.gen``;
these override any pre-specified in ``ModelSpec.model_gen_kwargs``.
NOTE: Models must have been fit prior to calling ``gen``.
NOTE: Some underlying models may ignore the ``n`` argument and produce a
model-determined number of arms. In that case this method will also output
a generator run with number of arms (that can differ from ``n``).
"""
model_spec = self.model_spec_to_gen_from
should_generate_run = True
generator_run = None
n_gen_draws = 0
# Keep generating until each of `generator_run.arms` is not a duplicate
# of a previous arm, if `should_deduplicate is True`
while should_generate_run:
generator_run = model_spec.gen(
# If `n` is not specified, ensure that the `None` value does not
# override the one set in `model_spec.model_gen_kwargs`.
n=model_spec.model_gen_kwargs.get("n")
if n is None and model_spec.model_gen_kwargs
else n,
# For `pending_observations`, prefer the input to this function, as
# `pending_observations` are dynamic throughout the experiment and thus
# unlikely to be specified in `model_spec.model_gen_kwargs`.
pending_observations=pending_observations,
**model_gen_kwargs,
)
should_generate_run = (
self.should_deduplicate
and arms_by_signature_for_deduplication
and any(
arm.signature in arms_by_signature_for_deduplication
for arm in generator_run.arms
)
)
n_gen_draws += 1
if should_generate_run:
if n_gen_draws > max_gen_draws_for_deduplication:
raise GenerationStrategyRepeatedPoints(
MAX_GEN_DRAWS_EXCEEDED_MESSAGE
)
else:
logger.info(
"The generator run produced duplicate arms. Re-running the "
"generation step in an attempt to deduplicate. Candidates "
f"produced in the last generator run: {generator_run.arms}."
)
assert generator_run is not None, (
"The GeneratorRun is None which is an unexpected state of this"
" GenerationStrategy. This occured on GenerationNode: {self.node_name}."
)
generator_run._generation_node_name = self.node_name
return generator_run
# ------------------------- Model selection logic helpers. -------------------------
def _pick_fitted_model_to_gen_from(self) -> ModelSpec:
"""Select one model to generate from among the fitted models on this
generation node.
NOTE: In base ``GenerationNode`` class, this method does the following:
1. if this ``GenerationNode`` has an associated ``BestModelSelector``,
use it to select one model to generate from among the fitted models
on this generation node.
2. otherwise, ensure that this ``GenerationNode`` only contains one
`ModelSpec` and select it.
"""
if self.best_model_selector is None:
if len(self.model_specs) != 1:
raise NotImplementedError(CANNOT_SELECT_ONE_MODEL_MSG)
return self.model_specs[0]
for model_spec in self.model_specs:
model_spec.cross_validate()
best_model_index = not_none(self.best_model_selector).best_diagnostic(
diagnostics=[not_none(m.diagnostics) for m in self.model_specs],
)
return self.model_specs[best_model_index]
# ------------------------- Trial logic helpers. -------------------------
@property
def trials_from_node(self) -> Set[int]:
"""Returns a dictionary mapping a GenerationNode to the trials it generated.
Returns:
Set[int]: A set containing all the trials indices generated by this node.
"""
# TODO: @mgarrard simplify this method after generation_node_name added to
# BaseTrial
trials_from_node = set()
for _idx, trial in self.experiment.trials.items():
generator_runs_from_trial = trial.generator_runs
for gr in generator_runs_from_trial:
if (
gr._generation_node_name is not None
and gr._generation_node_name == self.node_name
):
trials_from_node.add(trial.index)
return trials_from_node
[docs] def should_transition_to_next_node(
self, raise_data_required_error: bool = True
) -> Tuple[bool, Optional[str]]:
"""Checks whether we should transition to the next node based on this node's
TransitionCriterion
Args:
raise_data_required_error: Whether to raise ``DataRequiredError`` in the
case detailed above. Not raising the error is useful if just looking to
check how many generator runs (to be made into trials) can be produced,
but not actually producing them yet.
Returns:
bool: Whether we should transition to the next node.
"""
if self.gen_unlimited_trials and len(self.transition_criteria) == 0:
return False, None
transition_blocking_criterion = [
criterion
for criterion in self.transition_criteria
if criterion.block_transition_if_unmet
]
all_transition_blocking_criteria_are_met = all(
transition_criterion.is_met(
self.experiment,
trials_from_node=self.trials_from_node,
)
for transition_criterion in transition_blocking_criterion
)
# Raise any necessary generation errors: for any met criterion,
# call its `block_continued_generation_error` method if not all
# transition-blocking criteria are met. The method might not raise an
# error, depending on its implementation on given criterion, so the error
# from the first met one that does block continued generation, will be raised.
if not all_transition_blocking_criteria_are_met:
for criterion in self.transition_criteria:
if (
criterion.is_met(
self.experiment, trials_from_node=self.trials_from_node
)
and raise_data_required_error
):
criterion.block_continued_generation_error(
node_name=self.node_name,
model_name=self.model_to_gen_from_name,
experiment=self.experiment,
trials_from_node=self.trials_from_node,
)
# Determine transition state
if (
len(transition_blocking_criterion) > 0
and all_transition_blocking_criteria_are_met
):
transition_nodes = [
criterion.transition_to
for criterion in transition_blocking_criterion
if criterion._transition_to is not None
]
if len(set(transition_nodes)) > 1:
# TODO: support intelligent selection between multiple transition nodes
raise NotImplementedError(
"Cannot currently select between multiple transition nodes."
)
return True, transition_nodes[0]
return False, None
[docs] def generator_run_limit(self, supress_generation_errors: bool = True) -> int:
"""How many generator runs can this generation strategy generate right now,
assuming each one of them becomes its own trial. Only considers
`transition_criteria` that are TrialBasedCriterion.
Returns:
- the number of generator runs that can currently be produced, with -1
meaning unlimited generator runs,
"""
# TODO: @mgarrard Should we consider returning `None` if there is no limit?
# TODO:@mgarrard Should we instead have `raise_generation_error`? The name
# of this method doesn't suggest that it would raise errors by default, since
# it's just finding out the limit according to the name. I know we want the
# errors in some cases, so we could call the flag `raise_error_if_cannot_gen` or
# something like that : )
trial_based_gen_blocking_criteria = [
criterion
for criterion in self.transition_criteria
if criterion.block_gen_if_met and isinstance(criterion, TrialBasedCriterion)
]
gen_blocking_criterion_delta_from_threshold = [
criterion.num_till_threshold(
experiment=self.experiment, trials_from_node=self.trials_from_node
)
for criterion in trial_based_gen_blocking_criteria
]
# Raise any necessary generation errors: for any met criterion,
# call its `block_continued_generation_error` method The method might not
# raise an error, depending on its implementation on given criterion, so the
# error from the first met one that does block continued generation, will be
# raised.
if not supress_generation_errors:
for criterion in trial_based_gen_blocking_criteria:
# TODO[mgarrard]: Raise a group of all the errors, from each gen-
# blocking transition criterion.
if criterion.is_met(
self.experiment, trials_from_node=self.trials_from_node
):
criterion.block_continued_generation_error(
node_name=self.node_name,
model_name=self.model_to_gen_from_name,
experiment=self.experiment,
trials_from_node=self.trials_from_node,
)
if len(gen_blocking_criterion_delta_from_threshold) == 0:
if not self.gen_unlimited_trials:
logger.warning(
"Even though this node is not flagged for generation of unlimited "
"trials, there are no generation blocking criterion, therefore, "
"unlimited trials will be generated."
)
return -1
return min(gen_blocking_criterion_delta_from_threshold)
def __repr__(self) -> str:
"String representation of this GenerationNode"
# add model specs
str_rep = f"{self.__class__.__name__}(model_specs="
model_spec_str = str(self.model_specs).replace("\n", " ").replace("\t", "")
str_rep += model_spec_str
# add node name, gen_unlimited_trials, and transition_criteria
str_rep += f", node_name={self.node_name}"
str_rep += f", gen_unlimited_trials={str(self.gen_unlimited_trials)}"
str_rep += f", transition_criteria={str(self.transition_criteria)}"
return f"{str_rep})"
[docs]@dataclass
class GenerationStep(GenerationNode, SortableBase):
"""One step in the generation strategy, corresponds to a single model.
Describes the model, how many trials will be generated with this model, what
minimum number of observations is required to proceed to the next model, etc.
NOTE: Model can be specified either from the model registry
(`ax.modelbridge.registry.Models` or using a callable model constructor. Only
models from the registry can be saved, and thus optimization can only be
resumed if interrupted when using models from the registry.
Args:
model: A member of `Models` enum or a callable returning an instance of
`ModelBridge` with an instantiated underlying `Model`. Refer to
`ax/modelbridge/factory.py` for examples of such callables.
num_trials: How many trials to generate with the model from this step.
If set to -1, trials will continue to be generated from this model
as long as `generation_strategy.gen` is called (available only for
the last of the generation steps).
min_trials_observed: How many trials must be completed before the
generation strategy can proceed to the next step. Defaults to 0.
If `num_trials` of a given step have been generated but `min_trials_
observed` have not been completed, a call to `generation_strategy.gen`
will fail with a `DataRequiredError`.
max_parallelism: How many trials generated in the course of this step are
allowed to be run (i.e. have `trial.status` of `RUNNING`) simultaneously.
If `max_parallelism` trials from this step are already running, a call
to `generation_strategy.gen` will fail with a `MaxParallelismReached
Exception`, indicating that more trials need to be completed before
generating and running next trials.
use_update: DEPRECATED.
enforce_num_trials: Whether to enforce that only `num_trials` are generated
from the given step. If False and `num_trials` have been generated, but
`min_trials_observed` have not been completed, `generation_strategy.gen`
will continue generating trials from the current step, exceeding `num_
trials` for it. Allows to avoid `DataRequiredError`, but delays
proceeding to next generation step.
model_kwargs: Dictionary of kwargs to pass into the model constructor on
instantiation. E.g. if `model` is `Models.SOBOL`, kwargs will be applied
as `Models.SOBOL(**model_kwargs)`; if `model` is `get_sobol`, `get_sobol(
**model_kwargs)`. NOTE: if generation strategy is interrupted and
resumed from a stored snapshot and its last used model has state saved on
its generator runs, `model_kwargs` is updated with the state dict of the
model, retrieved from the last generator run of this generation strategy.
model_gen_kwargs: Each call to `generation_strategy.gen` performs a call to the
step's model's `gen` under the hood; `model_gen_kwargs` will be passed to
the model's `gen` like so: `model.gen(**model_gen_kwargs)`.
completion_criteria: List of TransitionCriterion. All `is_met` must evaluate
True for the GenerationStrategy to move on to the next Step
index: Index of this generation step, for use internally in `Generation
Strategy`. Do not assign as it will be reassigned when instantiating
`GenerationStrategy` with a list of its steps.
should_deduplicate: Whether to deduplicate the parameters of proposed arms
against those of previous arms via rejection sampling. If this is True,
the generation strategy will discard generator runs produced from the
generation step that has `should_deduplicate=True` if they contain arms
already present on the experiment and replace them with new generator runs.
If no generator run with entirely unique arms could be produced in 5
attempts, a `GenerationStrategyRepeatedPoints` error will be raised, as we
assume that the optimization converged when the model can no longer suggest
unique arms.
Note for developers: by "model" here we really mean an Ax ModelBridge object, which
contains an Ax Model under the hood. We call it "model" here to simplify and focus
on explaining the logic of GenerationStep and GenerationStrategy.
"""
# Required options:
model: Union[ModelRegistryBase, Callable[..., ModelBridge]]
num_trials: int
# Optional model specifications:
# Kwargs to pass into the Models constructor (or factory function).
model_kwargs: Optional[Dict[str, Any]] = None
# Kwargs to pass into the Model's `.gen` function.
model_gen_kwargs: Optional[Dict[str, Any]] = None
# Optional specifications for use in generation strategy:
completion_criteria: Sequence[TransitionCriterion] = field(default_factory=list)
min_trials_observed: int = 0
max_parallelism: Optional[int] = None
use_update: bool = False
enforce_num_trials: bool = True
# Whether the generation strategy should deduplicate the suggested arms against
# the arms already present on the experiment. If this is `True`
# on a given generation step, during that step the generation
# strategy will discard a generator run that contains an arm
# already present on the experiment and produce a new generator
# run instead before returning it from `gen` or `_gen_multiple`.
should_deduplicate: bool = False
index: int = -1 # Index of this step, set internally.
# Optional model name. Defaults to `model_spec.model_key`.
model_name: str = field(default_factory=str)
def __post_init__(self) -> None:
if self.use_update:
raise DeprecationWarning("`GenerationStep.use_update` is deprecated.")
if (
self.enforce_num_trials
and (self.num_trials >= 0)
and (self.min_trials_observed > self.num_trials)
):
raise UserInputError(
"`GenerationStep` received `min_trials_observed > num_trials` "
f"(`min_trials_observed = {self.min_trials_observed}`, `num_trials = "
f"{self.num_trials}`), making completion of this step impossible. "
"Please alter inputs so that `min_trials_observed <= num_trials`."
)
if not isinstance(self.model, ModelRegistryBase):
if not callable(self.model):
raise UserInputError(
"`model` in generation step must be either a `ModelRegistryBase` "
"enum subclass entry or a callable factory function returning a "
"model bridge instance."
)
model_spec = FactoryFunctionModelSpec(
factory_function=self.model,
model_kwargs=self.model_kwargs,
model_gen_kwargs=self.model_gen_kwargs,
)
else:
model_spec = ModelSpec(
model_enum=self.model,
model_kwargs=self.model_kwargs,
model_gen_kwargs=self.model_gen_kwargs,
)
if self.model_name == "":
try:
self.model_name = model_spec.model_key
except TypeError:
# Factory functions may not always have a model key defined.
self.model_name = f"Unknown {model_spec.__class__.__name__}"
# Create transition criteria for this step. MaximumTrialsInStatus can be used
# to ensure that requirements related to num_trials and unlimited trials
# are met. MinimumTrialsInStatus can be used enforce the min_trials_observed
# requirement, and override MaxTrials if enforce flag is set to true. We set
# `transition_to` is set in `GenerationStrategy` constructor,
# because only then is the order of the generation steps actually known.
transition_criteria = []
if self.num_trials != -1:
gen_unlimited_trials = False
transition_criteria.append(
MaxTrials(
threshold=self.num_trials,
not_in_statuses=[TrialStatus.FAILED, TrialStatus.ABANDONED],
block_gen_if_met=self.enforce_num_trials,
block_transition_if_unmet=True,
)
)
else:
gen_unlimited_trials = True
if self.min_trials_observed > 0:
transition_criteria.append(
MinTrials(
only_in_statuses=[
TrialStatus.COMPLETED,
TrialStatus.EARLY_STOPPED,
],
threshold=self.min_trials_observed,
block_gen_if_met=False,
block_transition_if_unmet=True,
)
)
if self.max_parallelism is not None:
transition_criteria.append(
MaxGenerationParallelism(
threshold=self.max_parallelism,
only_in_statuses=[TrialStatus.RUNNING],
block_gen_if_met=True,
block_transition_if_unmet=False,
transition_to=None,
)
)
if len(self.completion_criteria) > 0:
gen_unlimited_trials = False
transition_criteria += self.completion_criteria
super().__init__(
node_name=f"GenerationStep_{str(self.index)}",
model_specs=[model_spec],
should_deduplicate=self.should_deduplicate,
transition_criteria=transition_criteria,
gen_unlimited_trials=gen_unlimited_trials,
)
@property
def model_spec(self) -> ModelSpec:
"""Returns the first model_spec from the model_specs attribute."""
return self.model_specs[0]
@property
def _unique_id(self) -> str:
"""Returns the unique ID of this generation step, which is the index."""
return str(self.index)
[docs] def gen(
self,
n: Optional[int] = None,
pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None,
max_gen_draws_for_deduplication: int = MAX_GEN_DRAWS,
arms_by_signature_for_deduplication: Optional[Dict[str, Arm]] = None,
**model_gen_kwargs: Any,
) -> GeneratorRun:
gr = super().gen(
n=n,
pending_observations=pending_observations,
max_gen_draws_for_deduplication=max_gen_draws_for_deduplication,
arms_by_signature_for_deduplication=arms_by_signature_for_deduplication,
**model_gen_kwargs,
)
gr._generation_step_index = self.index
return gr
def __eq__(self, other: Base) -> bool:
# We need to override `__eq__` to make sure we inherit the one from
# the base class and not the one from dataclasses library, since we
# want to be comparing equality of generation steps in the same way
# as we compare equality of other Ax objects (and we want all the
# same special-casing to apply).
return SortableBase.__eq__(self, other=other)
