#!/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 pickle
from typing import Any, Dict, Type
from ax.benchmark.benchmark_problem import BenchmarkProblem, SimpleBenchmarkProblem
from ax.core import ObservationFeatures
from ax.core.arm import Arm
from ax.core.batch_trial import BatchTrial
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun
from ax.core.map_data import MapData
from ax.core.metric import Metric
from ax.core.multi_type_experiment import MultiTypeExperiment
from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
from ax.core.optimization_config import (
MultiObjectiveOptimizationConfig,
OptimizationConfig,
)
from ax.core.outcome_constraint import OutcomeConstraint
from ax.core.parameter import ChoiceParameter, FixedParameter, RangeParameter
from ax.core.parameter_constraint import (
OrderConstraint,
ParameterConstraint,
SumConstraint,
)
from ax.core.search_space import SearchSpace
from ax.core.simple_experiment import SimpleExperiment
from ax.core.trial import Trial
from ax.early_stopping.strategies import (
PercentileEarlyStoppingStrategy,
)
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import _encode_callables_as_references
from ax.modelbridge.transforms.base import Transform
from ax.models.torch.botorch_modular.model import BoTorchModel
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.runners.synthetic import SyntheticRunner
from ax.storage.botorch_modular_registry import CLASS_TO_REGISTRY
from ax.storage.transform_registry import TRANSFORM_REGISTRY
from ax.utils.common.serialization import serialize_init_args
[docs]def experiment_to_dict(experiment: Experiment) -> Dict[str, Any]:
"""Convert Ax experiment to a dictionary."""
return {
"__type": experiment.__class__.__name__,
"name": experiment._name,
"description": experiment.description,
"experiment_type": experiment.experiment_type,
"search_space": experiment.search_space,
"optimization_config": experiment.optimization_config,
"tracking_metrics": list(experiment._tracking_metrics.values()),
"runner": experiment.runner,
"status_quo": experiment.status_quo,
"time_created": experiment.time_created,
"trials": experiment.trials,
"is_test": experiment.is_test,
"data_by_trial": experiment.data_by_trial,
"properties": experiment._properties,
"default_data_type": experiment._default_data_type,
}
[docs]def simple_experiment_to_dict(experiment: SimpleExperiment) -> Dict[str, Any]:
"""Convert AE simple experiment to a dictionary."""
return experiment_to_dict(experiment)
[docs]def multi_type_experiment_to_dict(experiment: MultiTypeExperiment) -> Dict[str, Any]:
"""Convert AE multitype experiment to a dictionary."""
multi_type_dict = {
"default_trial_type": experiment._default_trial_type,
"_metric_to_canonical_name": experiment._metric_to_canonical_name,
"_metric_to_trial_type": experiment._metric_to_trial_type,
"_trial_type_to_runner": experiment._trial_type_to_runner,
}
multi_type_dict.update(experiment_to_dict(experiment))
return multi_type_dict
[docs]def batch_to_dict(batch: BatchTrial) -> Dict[str, Any]:
"""Convert Ax batch to a dictionary."""
return {
"__type": batch.__class__.__name__,
"index": batch.index,
"trial_type": batch.trial_type,
"ttl_seconds": batch.ttl_seconds,
"status": batch.status,
"status_quo": batch.status_quo,
"status_quo_weight_override": batch._status_quo_weight_override,
"time_created": batch.time_created,
"time_completed": batch.time_completed,
"time_staged": batch.time_staged,
"time_run_started": batch.time_run_started,
"abandoned_reason": batch.abandoned_reason,
"run_metadata": batch.run_metadata,
"stop_metadata": batch.stop_metadata,
"generator_run_structs": batch.generator_run_structs,
"runner": batch.runner,
"abandoned_arms_metadata": batch._abandoned_arms_metadata,
"num_arms_created": batch._num_arms_created,
"optimize_for_power": batch.optimize_for_power,
"generation_step_index": batch._generation_step_index,
"properties": batch._properties,
}
[docs]def trial_to_dict(trial: Trial) -> Dict[str, Any]:
"""Convert Ax trial to a dictionary."""
return {
"__type": trial.__class__.__name__,
"index": trial.index,
"trial_type": trial.trial_type,
"ttl_seconds": trial.ttl_seconds,
"status": trial.status,
"time_created": trial.time_created,
"time_completed": trial.time_completed,
"time_staged": trial.time_staged,
"time_run_started": trial.time_run_started,
"abandoned_reason": trial.abandoned_reason,
"run_metadata": trial.run_metadata,
"stop_metadata": trial.stop_metadata,
"generator_run": trial.generator_run,
"runner": trial.runner,
"num_arms_created": trial._num_arms_created,
"generation_step_index": trial._generation_step_index,
"properties": trial._properties,
}
[docs]def range_parameter_to_dict(parameter: RangeParameter) -> Dict[str, Any]:
"""Convert Ax range parameter to a dictionary."""
return {
"__type": parameter.__class__.__name__,
"name": parameter.name,
"parameter_type": parameter.parameter_type,
"lower": parameter.lower,
"upper": parameter.upper,
"log_scale": parameter.log_scale,
"digits": parameter.digits,
"is_fidelity": parameter.is_fidelity,
"target_value": parameter.target_value,
}
[docs]def choice_parameter_to_dict(parameter: ChoiceParameter) -> Dict[str, Any]:
"""Convert Ax choice parameter to a dictionary."""
return {
"__type": parameter.__class__.__name__,
"is_ordered": parameter.is_ordered,
"is_task": parameter.is_task,
"name": parameter.name,
"parameter_type": parameter.parameter_type,
"values": parameter.values,
"is_fidelity": parameter.is_fidelity,
"target_value": parameter.target_value,
}
[docs]def fixed_parameter_to_dict(parameter: FixedParameter) -> Dict[str, Any]:
"""Convert Ax fixed parameter to a dictionary."""
return {
"__type": parameter.__class__.__name__,
"name": parameter.name,
"parameter_type": parameter.parameter_type,
"value": parameter.value,
"is_fidelity": parameter.is_fidelity,
"target_value": parameter.target_value,
}
[docs]def order_parameter_constraint_to_dict(
parameter_constraint: OrderConstraint,
) -> Dict[str, Any]:
"""Convert Ax order parameter constraint to a dictionary."""
return {
"__type": parameter_constraint.__class__.__name__,
"lower_name": parameter_constraint.lower_parameter.name,
"upper_name": parameter_constraint.upper_parameter.name,
}
[docs]def sum_parameter_constraint_to_dict(
parameter_constraint: SumConstraint,
) -> Dict[str, Any]:
"""Convert Ax sum parameter constraint to a dictionary."""
return {
"__type": parameter_constraint.__class__.__name__,
"parameter_names": parameter_constraint._parameter_names,
"is_upper_bound": parameter_constraint._is_upper_bound,
# SumParameterConstraint constructor takes in absolute value of
# the bound and transforms it based on the is_upper_bound value
"bound": abs(parameter_constraint._bound),
}
[docs]def parameter_constraint_to_dict(
parameter_constraint: ParameterConstraint,
) -> Dict[str, Any]:
"""Convert Ax sum parameter constraint to a dictionary."""
return {
"__type": parameter_constraint.__class__.__name__,
"constraint_dict": parameter_constraint.constraint_dict,
"bound": parameter_constraint.bound,
}
[docs]def arm_to_dict(arm: Arm) -> Dict[str, Any]:
"""Convert Ax arm to a dictionary."""
return {
"__type": arm.__class__.__name__,
"parameters": arm.parameters,
"name": arm._name,
}
[docs]def search_space_to_dict(search_space: SearchSpace) -> Dict[str, Any]:
"""Convert Ax search space to a dictionary."""
return {
"__type": search_space.__class__.__name__,
"parameters": list(search_space.parameters.values()),
"parameter_constraints": search_space.parameter_constraints,
}
[docs]def metric_to_dict(metric: Metric) -> Dict[str, Any]:
"""Convert Ax metric to a dictionary."""
properties = serialize_init_args(object=metric)
properties["__type"] = metric.__class__.__name__
return properties
[docs]def objective_to_dict(objective: Objective) -> Dict[str, Any]:
"""Convert Ax objective to a dictionary."""
return {
"__type": objective.__class__.__name__,
"metric": objective.metric,
"minimize": objective.minimize,
}
[docs]def multi_objective_to_dict(objective: MultiObjective) -> Dict[str, Any]:
"""Convert Ax objective to a dictionary."""
return {
"__type": objective.__class__.__name__,
"objectives": objective.objectives,
"weights": objective.weights,
}
[docs]def scalarized_objective_to_dict(objective: ScalarizedObjective) -> Dict[str, Any]:
"""Convert Ax objective to a dictionary."""
return {
"__type": objective.__class__.__name__,
"metrics": objective.metrics,
"weights": objective.weights,
"minimize": objective.minimize,
}
[docs]def outcome_constraint_to_dict(outcome_constraint: OutcomeConstraint) -> Dict[str, Any]:
"""Convert Ax outcome constraint to a dictionary."""
return {
"__type": outcome_constraint.__class__.__name__,
"metric": outcome_constraint.metric,
"op": outcome_constraint.op,
"bound": outcome_constraint.bound,
"relative": outcome_constraint.relative,
}
[docs]def optimization_config_to_dict(
optimization_config: OptimizationConfig,
) -> Dict[str, Any]:
"""Convert Ax optimization config to a dictionary."""
return {
"__type": optimization_config.__class__.__name__,
"objective": optimization_config.objective,
"outcome_constraints": optimization_config.outcome_constraints,
}
[docs]def multi_objective_optimization_config_to_dict(
multi_objective_optimization_config: MultiObjectiveOptimizationConfig,
) -> Dict[str, Any]:
"""Convert Ax optimization config to a dictionary."""
return {
"__type": multi_objective_optimization_config.__class__.__name__,
"objective": multi_objective_optimization_config.objective,
"outcome_constraints": multi_objective_optimization_config.outcome_constraints,
"objective_thresholds": multi_objective_optimization_config.objective_thresholds, # noqa E501
}
[docs]def generator_run_to_dict(generator_run: GeneratorRun) -> Dict[str, Any]:
"""Convert Ax generator run to a dictionary."""
gr = generator_run
cand_metadata = gr.candidate_metadata_by_arm_signature
return {
"__type": gr.__class__.__name__,
"arms": gr.arms,
"weights": gr.weights,
"optimization_config": gr.optimization_config,
"search_space": gr.search_space,
"time_created": gr.time_created,
"model_predictions": gr.model_predictions,
"best_arm_predictions": gr.best_arm_predictions,
"generator_run_type": gr.generator_run_type,
"index": gr.index,
"fit_time": gr.fit_time,
"gen_time": gr.gen_time,
"model_key": gr._model_key,
"model_kwargs": gr._model_kwargs,
"bridge_kwargs": gr._bridge_kwargs,
"gen_metadata": gr._gen_metadata,
"model_state_after_gen": gr._model_state_after_gen,
"generation_step_index": gr._generation_step_index,
"candidate_metadata_by_arm_signature": cand_metadata,
}
[docs]def runner_to_dict(runner: SyntheticRunner) -> Dict[str, Any]:
"""Convert Ax synthetic runner to a dictionary."""
properties = serialize_init_args(object=runner)
properties["__type"] = runner.__class__.__name__
return properties
[docs]def data_to_dict(data: Data) -> Dict[str, Any]:
"""Convert Ax data to a dictionary."""
return {
"__type": data.__class__.__name__,
"df": data.df,
"description": data.description,
}
[docs]def map_data_to_dict(map_data: MapData) -> Dict[str, Any]:
"""Convert Ax data to a dictionary."""
return {
"__type": map_data.__class__.__name__,
"df": map_data.df,
"map_keys": map_data.map_keys,
"description": map_data.description,
}
[docs]def generation_step_to_dict(generation_step: GenerationStep) -> Dict[str, Any]:
"""Converts Ax generation step to a dictionary."""
return {
"__type": generation_step.__class__.__name__,
"model": generation_step.model,
"num_trials": generation_step.num_trials,
"min_trials_observed": generation_step.min_trials_observed,
"max_parallelism": generation_step.max_parallelism,
"use_update": generation_step.use_update,
"enforce_num_trials": generation_step.enforce_num_trials,
"model_kwargs": _encode_callables_as_references(
generation_step.model_kwargs or {}
),
"model_gen_kwargs": _encode_callables_as_references(
generation_step.model_gen_kwargs or {}
),
"index": generation_step.index,
}
[docs]def generation_strategy_to_dict(
generation_strategy: GenerationStrategy,
) -> Dict[str, Any]:
"""Converts Ax generation strategy to a dictionary."""
if generation_strategy.uses_non_registered_models:
raise ValueError( # pragma: no cover
"Generation strategies that use custom models provided through "
"callables cannot be serialized and stored."
)
return {
"__type": generation_strategy.__class__.__name__,
"db_id": generation_strategy._db_id,
"name": generation_strategy.name,
"steps": generation_strategy._steps,
"curr_index": generation_strategy._curr.index,
"generator_runs": generation_strategy._generator_runs,
"had_initialized_model": generation_strategy.model is not None,
"experiment": generation_strategy._experiment,
}
[docs]def observation_features_to_dict(obs_features: ObservationFeatures) -> Dict[str, Any]:
"""Converts Ax observation features to a dictionary"""
return {
"__type": obs_features.__class__.__name__,
"parameters": obs_features.parameters,
"trial_index": obs_features.trial_index,
"start_time": obs_features.start_time,
"end_time": obs_features.end_time,
"random_split": obs_features.random_split,
"metadata": obs_features.metadata,
}
[docs]def benchmark_problem_to_dict(benchmark_problem: BenchmarkProblem) -> Dict[str, Any]:
"""Converts an Ax benchmark problem to a serializable dictionary."""
if isinstance(benchmark_problem, SimpleBenchmarkProblem):
if benchmark_problem.uses_synthetic_function:
function_name = benchmark_problem.f.name # pyre-ignore[16]
f = None
else:
function_name = benchmark_problem.f.__name__ # pyre-ignore[16]
f = pickle.dumps(benchmark_problem.f, 0).decode()
return {
"__type": benchmark_problem.__class__.__name__,
"uses_synthetic_function": benchmark_problem.uses_synthetic_function,
"function_name": function_name,
# If the benchamrk problem uses a custom callable, pickle it.
"f": f,
"name": benchmark_problem.name,
"domain": benchmark_problem.domain,
"minimize": benchmark_problem.minimize,
"noise_sd": benchmark_problem.noise_sd,
"evaluate_suggested": benchmark_problem.evaluate_suggested,
"optimal_value": benchmark_problem.optimal_value,
}
elif isinstance(benchmark_problem, BenchmarkProblem):
properties = serialize_init_args(object=benchmark_problem)
properties["__type"] = benchmark_problem.__class__.__name__
return properties
else: # pragma: no cover
raise ValueError(f"Expected benchmark problem, got: {benchmark_problem}.")
[docs]def botorch_model_to_dict(model: BoTorchModel) -> Dict[str, Any]:
"""Convert Ax model to a dictionary."""
return {
"__type": model.__class__.__name__,
"surrogate": model.surrogate,
"surrogate_options": model.surrogate_options,
"acquisition_class": model.acquisition_class,
"botorch_acqf_class": model._botorch_acqf_class,
"acquisition_options": model.acquisition_options or {},
"refit_on_update": model.refit_on_update,
"refit_on_cv": model.refit_on_cv,
"warm_start_refit": model.warm_start_refit,
}
[docs]def surrogate_to_dict(surrogate: Surrogate) -> Dict[str, Any]:
"""Convert Ax surrogate to a dictionary."""
dict_representation = {"__type": surrogate.__class__.__name__}
dict_representation.update(surrogate._serialize_attributes_as_kwargs())
return dict_representation
[docs]def botorch_modular_to_dict(class_type: Type[Any]) -> Dict[str, Any]:
"""Convert any class to a dictionary."""
for _class in CLASS_TO_REGISTRY:
if issubclass(class_type, _class):
registry = CLASS_TO_REGISTRY[_class]
if class_type not in registry:
raise ValueError( # pragma: no cover
f"Class `{class_type.__name__}` not in Type[{_class.__name__}] "
"registry, please add it. BoTorch object registries are "
"located in `ax/storage/botorch_modular_registry.py`."
)
return {
"__type": f"Type[{_class.__name__}]",
"index": registry[class_type],
"class": f"{_class}",
}
raise ValueError(
f"{class_type} does not have a corresponding parent class in "
"CLASS_TO_REGISTRY."
)
[docs]def percentile_early_stopping_strategy_to_dict(
strategy: PercentileEarlyStoppingStrategy,
) -> Dict[str, Any]:
"""Convert Ax percentile early stopping strategy to a dictionary."""
return {
"__type": strategy.__class__.__name__,
"percentile_threshold": strategy.percentile_threshold,
"min_progression": strategy.min_progression,
"min_curves": strategy.min_curves,
}
