#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import List, Optional, Tuple
from ax.core.experiment import Experiment
from ax.core.search_space import SearchSpace
from ax.core.simple_experiment import SimpleExperiment, TEvaluationFunction
from ax.core.types import TModelPredictArm, TParameterization
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.modelbridge_utils import get_pending_observation_features
from ax.service.utils.best_point import (
get_best_from_model_predictions,
get_best_raw_objective_point,
)
from ax.service.utils.instantiation import (
TParameterRepresentation,
constraint_from_str,
outcome_constraint_from_str,
parameter_from_json,
)
from ax.utils.common.logger import get_logger
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,
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
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:
self.generation_strategy = choose_generation_strategy(
search_space=experiment.search_space,
arms_per_trial=self.arms_per_trial,
random_seed=self.random_seed,
)
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."""
exp_parameters = [parameter_from_json(p) for p in parameters]
parameter_map = {p.name: p for p in exp_parameters}
experiment = SimpleExperiment(
name=experiment_name,
search_space=SearchSpace(
parameters=exp_parameters,
parameter_constraints=None
if parameter_constraints is None
else [
constraint_from_str(c, parameter_map) for c in parameter_constraints
],
),
objective_name=objective_name,
evaluation_function=evaluation_function,
minimize=minimize,
outcome_constraints=[
outcome_constraint_from_str(c) for c in (outcome_constraints or [])
],
)
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,
)
[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."""
# TODO[drfreund], T42401002
raise NotImplementedError # pragma: no cover
[docs] def run_trial(self) -> None:
"""Run a single step of the optimization plan."""
if self.current_trial >= self.total_trials:
raise ValueError(f"Optimization is complete, cannot run another trial.")
logger.info(f"Running optimization trial {self.current_trial + 1}...")
arms_per_trial = self.arms_per_trial
if arms_per_trial == 1:
trial = self.experiment.new_trial(
generator_run=self.generation_strategy.gen(
experiment=self.experiment,
pending_observations=get_pending_observation_features(
experiment=self.experiment
),
)
)
elif arms_per_trial > 1:
trial = self.experiment.new_batch_trial(
generator_run=self.generation_strategy.gen(
experiment=self.experiment, n=arms_per_trial
)
)
else: # pragma: no cover
raise ValueError(f"Invalid number of arms per trial: {arms_per_trial}")
trial.fetch_data()
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):
self.run_trial()
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_from_model_predictions(experiment=self.experiment)
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()
