#!/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
import inspect
import logging
from typing import Iterable, List, Optional, Tuple
from ax.core.arm import Arm
from ax.core.base_trial import BaseTrial
from ax.core.batch_trial import BatchTrial
from ax.core.experiment import Experiment
from ax.core.formatting_utils import data_and_evaluations_from_raw_data
from ax.core.trial import Trial
from ax.core.types import (
TEvaluationFunction,
TEvaluationOutcome,
TModelPredictArm,
TParameterization,
)
from ax.core.utils import get_pending_observation_features
from ax.exceptions.constants import CHOLESKY_ERROR_ANNOTATION
from ax.exceptions.core import SearchSpaceExhausted, UserInputError
from ax.modelbridge.base import ModelBridge
from ax.modelbridge.dispatch_utils import choose_generation_strategy
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.modelbridge.registry import Models
from ax.service.utils.best_point import (
get_best_parameters_from_model_predictions,
get_best_raw_objective_point,
)
from ax.service.utils.instantiation import InstantiationBase, TParameterRepresentation
from ax.utils.common.executils import retry_on_exception
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import not_none
logger: logging.Logger = get_logger(__name__)
[docs]class OptimizationLoop:
"""Managed optimization loop, in which Ax oversees deployment of trials and
gathering data."""
def __init__(
self,
experiment: Experiment,
evaluation_function: TEvaluationFunction,
total_trials: int = 20,
arms_per_trial: int = 1,
random_seed: Optional[int] = None,
wait_time: int = 0,
run_async: bool = False, # TODO[Lena],
generation_strategy: Optional[GenerationStrategy] = None,
) -> None:
assert not run_async, "OptimizationLoop does not yet support async."
self.wait_time = wait_time
self.total_trials = total_trials
self.arms_per_trial = arms_per_trial
self.random_seed = random_seed
self.evaluation_function: TEvaluationFunction = evaluation_function
assert len(experiment.trials) == 0, (
"Optimization Loop should not be initialized with an experiment "
"that has trials already."
)
self.experiment = experiment
if generation_strategy is None:
# pyre-fixme[4]: Attribute must be annotated.
self.generation_strategy = choose_generation_strategy(
search_space=experiment.search_space,
use_batch_trials=self.arms_per_trial > 1,
random_seed=self.random_seed,
experiment=experiment,
)
else:
self.generation_strategy = generation_strategy
self.current_trial = 0
[docs] @staticmethod
def with_evaluation_function(
parameters: List[TParameterRepresentation],
evaluation_function: TEvaluationFunction,
experiment_name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
total_trials: int = 20,
arms_per_trial: int = 1,
wait_time: int = 0,
random_seed: Optional[int] = None,
generation_strategy: Optional[GenerationStrategy] = None,
) -> "OptimizationLoop":
"""Constructs a synchronous `OptimizationLoop` using an evaluation
function."""
experiment = InstantiationBase.make_experiment(
name=experiment_name,
parameters=parameters,
objectives={objective_name: "minimize" if minimize else "maximize"}
if objective_name
else None,
parameter_constraints=parameter_constraints,
outcome_constraints=outcome_constraints,
)
return OptimizationLoop(
experiment=experiment,
total_trials=total_trials,
arms_per_trial=arms_per_trial,
random_seed=random_seed,
wait_time=wait_time,
generation_strategy=generation_strategy,
evaluation_function=evaluation_function,
)
[docs] @classmethod
def with_runners_and_metrics(
cls,
parameters: List[TParameterRepresentation],
path_to_runner: str,
paths_to_metrics: List[str],
experiment_name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
total_trials: int = 20,
arms_per_trial: int = 1,
wait_time: int = 0,
random_seed: Optional[int] = None,
) -> "OptimizationLoop":
"""Constructs an asynchronous `OptimizationLoop` using Ax runners and
metrics."""
# NOTE: Could use `Scheduler` to implement this if needed.
raise NotImplementedError
def _call_evaluation_function(
self, parameterization: TParameterization, weight: Optional[float] = None
) -> TEvaluationOutcome:
signature = inspect.signature(self.evaluation_function)
num_evaluation_function_params = len(signature.parameters.items())
if num_evaluation_function_params == 1:
# pyre-ignore [20]: Can't run instance checks on subscripted generics.
evaluation = self.evaluation_function(parameterization)
elif num_evaluation_function_params == 2:
# pyre-ignore [19]: Can't run instance checks on subscripted generics.
evaluation = self.evaluation_function(parameterization, weight)
else:
raise UserInputError(
"Evaluation function must take either one parameter "
"(parameterization) or two parameters (parameterization and weight)."
)
return evaluation
def _get_new_trial(self) -> BaseTrial:
if self.arms_per_trial == 1:
return self.experiment.new_trial(
generator_run=self.generation_strategy.gen(
experiment=self.experiment,
pending_observations=get_pending_observation_features(
experiment=self.experiment
),
)
)
elif self.arms_per_trial > 1:
return self.experiment.new_batch_trial(
generator_run=self.generation_strategy.gen(
experiment=self.experiment, n=self.arms_per_trial
)
)
else:
raise UserInputError(
f"Invalid number of arms per trial: {self.arms_per_trial}"
)
def _get_weights_by_arm(
self, trial: BaseTrial
) -> Iterable[Tuple[Arm, Optional[float]]]:
if isinstance(trial, Trial):
if trial.arm is not None:
return [(not_none(trial.arm), None)]
return []
elif isinstance(trial, BatchTrial):
return trial.normalized_arm_weights().items()
else:
raise UserInputError(f"Invalid trial type: {type(trial)}")
[docs] @retry_on_exception(
logger=logger,
exception_types=(RuntimeError,),
suppress_all_errors=False,
wrap_error_message_in=CHOLESKY_ERROR_ANNOTATION,
)
def run_trial(self) -> None:
"""Run a single step of the optimization plan."""
if self.current_trial >= self.total_trials:
raise ValueError("Optimization is complete, cannot run another trial.")
logger.info(f"Running optimization trial {self.current_trial + 1}...")
trial = self._get_new_trial()
trial.mark_running(no_runner_required=True)
_, data = data_and_evaluations_from_raw_data(
raw_data={
arm.name: self._call_evaluation_function(arm.parameters, weight)
for arm, weight in self._get_weights_by_arm(trial)
},
trial_index=self.current_trial,
sample_sizes={},
data_type=self.experiment.default_data_type,
metric_names=not_none(
self.experiment.optimization_config
).objective.metric_names,
)
self.experiment.attach_data(data=data)
trial.mark_completed()
self.current_trial += 1
[docs] def full_run(self) -> OptimizationLoop:
"""Runs full optimization loop as defined in the provided optimization
plan."""
num_steps = self.total_trials
logger.info(f"Started full optimization with {num_steps} steps.")
for _ in range(num_steps):
try:
self.run_trial()
except SearchSpaceExhausted as err:
logger.info(
f"Stopped optimization as the search space is exhaused. Message "
f"from generation strategy: {err}."
)
return self
except Exception:
logger.exception("Encountered exception during optimization: ")
return self
return self
[docs] def get_best_point(self) -> Tuple[TParameterization, Optional[TModelPredictArm]]:
"""Obtains the best point encountered in the course
of this optimization."""
# Find latest trial which has a generator_run attached and get its predictions
model_predictions = get_best_parameters_from_model_predictions(
experiment=self.experiment, models_enum=Models
)
if model_predictions is not None:
return model_predictions
# Could not find through model, default to using raw objective.
parameterization, values = get_best_raw_objective_point(
experiment=self.experiment
)
# For values, grab just the means to conform to TModelPredictArm format.
return (
parameterization,
(
{k: v[0] for k, v in values.items()}, # v[0] is mean
{k: {k: v[1] * v[1]} for k, v in values.items()}, # v[1] is sem
),
)
[docs] def get_current_model(self) -> Optional[ModelBridge]:
"""Obtain the most recently used model in optimization."""
return self.generation_strategy.model
[docs]def optimize(
parameters: List[TParameterRepresentation],
evaluation_function: TEvaluationFunction,
experiment_name: Optional[str] = None,
objective_name: Optional[str] = None,
minimize: bool = False,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
total_trials: int = 20,
arms_per_trial: int = 1,
random_seed: Optional[int] = None,
generation_strategy: Optional[GenerationStrategy] = None,
) -> Tuple[
TParameterization, Optional[TModelPredictArm], Experiment, Optional[ModelBridge]
]:
"""Construct and run a full optimization loop."""
loop = OptimizationLoop.with_evaluation_function(
parameters=parameters,
objective_name=objective_name,
evaluation_function=evaluation_function,
experiment_name=experiment_name,
minimize=minimize,
parameter_constraints=parameter_constraints,
outcome_constraints=outcome_constraints,
total_trials=total_trials,
arms_per_trial=arms_per_trial,
random_seed=random_seed,
generation_strategy=generation_strategy,
)
loop.full_run()
parameterization, values = loop.get_best_point()
return parameterization, values, loop.experiment, loop.get_current_model()
