#!/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 datetime
import pickle
from collections import OrderedDict
from enum import Enum
from inspect import isclass
from typing import Any, Dict, List, Tuple, Type, cast
import numpy as np
import pandas as pd
from ax.benchmark.benchmark_problem import SimpleBenchmarkProblem
from ax.core.base_trial import BaseTrial
from ax.core.data import Data # noqa F401
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun
from ax.core.parameter import Parameter
from ax.core.parameter_constraint import (
OrderConstraint,
ParameterConstraint,
SumConstraint,
)
from ax.core.search_space import SearchSpace
from ax.core.simple_experiment import (
SimpleExperiment,
unimplemented_evaluation_function,
)
from ax.exceptions.storage import JSONDecodeError
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.modelbridge.registry import Models
from ax.modelbridge.transforms.base import Transform
from ax.storage.json_store.decoders import batch_trial_from_json, trial_from_json
from ax.storage.json_store.registry import DECODER_REGISTRY
from ax.storage.transform_registry import REVERSE_TRANSFORM_REGISTRY
from ax.utils.common.typeutils import torch_type_from_str
from ax.utils.measurement import synthetic_functions
[docs]def object_from_json(object_json: Any) -> Any:
"""Recursively load objects from a JSON-serializable dictionary."""
if type(object_json) in (str, int, float, bool, type(None)) or isinstance(
object_json, Enum
):
return object_json
elif isinstance(object_json, list):
return [object_from_json(i) for i in object_json]
elif isinstance(object_json, tuple):
return tuple(object_from_json(i) for i in object_json)
elif isinstance(object_json, dict):
if "__type" not in object_json:
# this is just a regular dictionary, e.g. the one in Parameter
# containing parameterizations
return {k: object_from_json(v) for k, v in object_json.items()}
_type = object_json.pop("__type")
if _type == "datetime":
return datetime.datetime.strptime(
object_json["value"], "%Y-%m-%d %H:%M:%S.%f"
)
elif _type == "OrderedDict":
return OrderedDict(
[(k, object_from_json(v)) for k, v in object_json["value"]]
)
elif _type == "DataFrame":
# Need dtype=False, otherwise infers arm_names like "4_1"
# should be int 41
return pd.read_json(object_json["value"], dtype=False)
elif _type == "ndarray":
return np.array(object_json["value"])
elif _type.startswith("torch"):
# Torch types will be encoded as "torch_<type_name>", so we drop prefix
return torch_type_from_str(
identifier=object_json["value"], type_name=_type[6:]
)
elif _type not in DECODER_REGISTRY:
err = (
f"The JSON dictionary passed to `object_from_json` has a type "
f"{_type} that is not registered with a corresponding class in "
f"DECODER_REGISTRY."
)
raise JSONDecodeError(err)
_class = DECODER_REGISTRY[_type]
if isclass(_class) and issubclass(_class, Enum):
# to access enum members by name, use item access
return _class[object_json["name"]]
elif _class == GeneratorRun:
return generator_run_from_json(object_json=object_json)
elif _class == GenerationStrategy:
return generation_strategy_from_json(generation_strategy_json=object_json)
elif _class == SimpleExperiment:
return simple_experiment_from_json(object_json=object_json)
elif _class == Experiment:
return experiment_from_json(object_json=object_json)
elif _class == SearchSpace:
return search_space_from_json(search_space_json=object_json)
elif _class == Type[Transform]:
return transform_type_from_json(object_json=object_json)
elif _class == SimpleBenchmarkProblem:
return simple_benchmark_problem_from_json(object_json=object_json)
return _class(**{k: object_from_json(v) for k, v in object_json.items()})
else:
err = (
f"The object {object_json} passed to `object_from_json` has an "
f"unsupported type: {type(object_json)}."
)
raise JSONDecodeError(err)
[docs]def generator_run_from_json(object_json: Dict[str, Any]) -> GeneratorRun:
"""Load Ax GeneratorRun from JSON."""
time_created_json = object_json.pop("time_created")
type_json = object_json.pop("generator_run_type")
index_json = object_json.pop("index")
generator_run = GeneratorRun(
**{k: object_from_json(v) for k, v in object_json.items()}
)
generator_run._time_created = object_from_json(time_created_json)
generator_run._generator_run_type = object_from_json(type_json)
generator_run._index = object_from_json(index_json)
return generator_run
[docs]def search_space_from_json(search_space_json: Dict[str, Any]) -> SearchSpace:
"""Load a SearchSpace from JSON.
This function is necessary due to the coupled loading of SearchSpace
and parameter constraints.
"""
parameters = object_from_json(search_space_json.pop("parameters"))
json_param_constraints = search_space_json.pop("parameter_constraints")
return SearchSpace(
parameters=parameters,
parameter_constraints=parameter_constraints_from_json(
parameter_constraint_json=json_param_constraints, parameters=parameters
),
)
[docs]def parameter_constraints_from_json(
parameter_constraint_json: List[Dict[str, Any]], parameters: List[Parameter]
) -> List[ParameterConstraint]:
"""Load ParameterConstraints from JSON.
Order and SumConstraint are tied to a search space,
and require that SearchSpace's parameters to be passed in for decoding.
Args:
parameter_constraint_json: JSON representation of parameter constraints.
parameters: Parameter definitions for decoding via parameter names.
Returns:
parameter_constraints: Python classes for parameter constraints.
"""
parameter_constraints = []
parameter_map = {p.name: p for p in parameters}
for constraint in parameter_constraint_json:
if constraint["__type"] == "OrderConstraint":
lower_parameter = parameter_map[constraint["lower_name"]]
upper_parameter = parameter_map[constraint["upper_name"]]
parameter_constraints.append(
OrderConstraint(
lower_parameter=lower_parameter, upper_parameter=upper_parameter
)
)
elif constraint["__type"] == "SumConstraint":
parameters = [parameter_map[name] for name in constraint["parameter_names"]]
parameter_constraints.append(
SumConstraint(
parameters=parameters,
is_upper_bound=constraint["is_upper_bound"],
bound=constraint["bound"],
)
)
else:
parameter_constraints.append(object_from_json(constraint))
return parameter_constraints
[docs]def trials_from_json(
experiment: Experiment, trials_json: Dict[str, Any]
) -> Dict[int, BaseTrial]:
"""Load Ax Trials from JSON."""
loaded_trials = {}
for index, batch_json in trials_json.items():
is_trial = batch_json["__type"] == "Trial"
batch_json = {
k: object_from_json(v) for k, v in batch_json.items() if k != "__type"
}
loaded_trials[int(index)] = (
trial_from_json(experiment=experiment, **batch_json)
if is_trial
else batch_trial_from_json(experiment=experiment, **batch_json)
)
return loaded_trials
[docs]def data_from_json(
data_by_trial_json: Dict[str, Any]
) -> Dict[int, "OrderedDict[int, Data]"]:
"""Load Ax Data from JSON."""
data_by_trial = object_from_json(data_by_trial_json)
# hack necessary because Python's json module converts dictionary
# keys to strings: https://stackoverflow.com/q/1450957
return {
int(k): OrderedDict({int(k2): v2 for k2, v2 in v.items()})
for k, v in data_by_trial.items()
}
[docs]def simple_experiment_from_json(object_json: Dict[str, Any]) -> SimpleExperiment:
"""Load AE SimpleExperiment from JSON."""
time_created_json = object_json.pop("time_created")
trials_json = object_json.pop("trials")
experiment_type_json = object_json.pop("experiment_type")
data_by_trial_json = object_json.pop("data_by_trial")
description_json = object_json.pop("description")
is_test_json = object_json.pop("is_test")
optimization_config = object_from_json(object_json.pop("optimization_config"))
# not relevant to simple experiment
del object_json["tracking_metrics"]
del object_json["runner"]
kwargs = {k: object_from_json(v) for k, v in object_json.items()}
kwargs["evaluation_function"] = unimplemented_evaluation_function
kwargs["objective_name"] = optimization_config.objective.metric.name
kwargs["minimize"] = optimization_config.objective.minimize
kwargs["outcome_constraints"] = optimization_config.outcome_constraints
experiment = SimpleExperiment(**kwargs)
experiment.description = object_from_json(description_json)
experiment.is_test = object_from_json(is_test_json)
experiment._time_created = object_from_json(time_created_json)
experiment._trials = trials_from_json(experiment, trials_json)
for trial in experiment._trials.values():
for arm in trial.arms:
experiment._arms_by_signature[arm.signature] = arm
if experiment.status_quo is not None:
# pyre-fixme[16]: Optional type has no attribute `signature`.
sq_sig = experiment.status_quo.signature
experiment._arms_by_signature[sq_sig] = experiment.status_quo
experiment._experiment_type = object_from_json(experiment_type_json)
experiment._data_by_trial = data_from_json(data_by_trial_json)
return experiment
[docs]def experiment_from_json(object_json: Dict[str, Any]) -> Experiment:
"""Load Ax Experiment from JSON."""
time_created_json = object_json.pop("time_created")
trials_json = object_json.pop("trials")
experiment_type_json = object_json.pop("experiment_type")
data_by_trial_json = object_json.pop("data_by_trial")
experiment = Experiment(**{k: object_from_json(v) for k, v in object_json.items()})
experiment._time_created = object_from_json(time_created_json)
experiment._trials = trials_from_json(experiment, trials_json)
for trial in experiment._trials.values():
for arm in trial.arms:
experiment._arms_by_signature[arm.signature] = arm
if experiment.status_quo is not None:
# pyre-fixme[16]: Optional type has no attribute `signature`.
sq_sig = experiment.status_quo.signature
experiment._arms_by_signature[sq_sig] = experiment.status_quo
experiment._experiment_type = object_from_json(experiment_type_json)
experiment._data_by_trial = data_from_json(data_by_trial_json)
return experiment
[docs]def generation_strategy_from_json(
generation_strategy_json: Dict[str, Any]
) -> GenerationStrategy:
"""Load generation strategy from JSON."""
steps = object_from_json(generation_strategy_json.pop("steps"))
gs = GenerationStrategy(steps=steps, name=generation_strategy_json.pop("name"))
gs._db_id = object_from_json(generation_strategy_json.pop("db_id"))
gs._experiment = object_from_json(generation_strategy_json.pop("experiment"))
gs._generated = generation_strategy_json.pop("generated")
gs._observed = generation_strategy_json.pop("observed")
gs._data = object_from_json(generation_strategy_json.pop("data"))
gs._curr = gs._steps[generation_strategy_json.pop("curr_index")]
gs._generator_runs = object_from_json(
generation_strategy_json.pop("generator_runs")
)
if generation_strategy_json.pop("had_initialized_model"): # pragma: no cover
# If model in the current step was not directly from the `Models` enum,
# pass its type to `restore_model_from_generator_run`, which will then
# attempt to use this type to recreate the model.
if type(gs._curr.model) != Models:
models_enum = type(gs._curr.model)
assert issubclass(models_enum, Models)
# pyre-ignore[6]: `models_enum` typing hackiness
gs._restore_model_from_generator_run(models_enum=models_enum)
return gs
gs._restore_model_from_generator_run()
return gs
[docs]def simple_benchmark_problem_from_json(
object_json: Dict[str, Any]
) -> SimpleBenchmarkProblem:
"""Load a benchmark problem from JSON."""
uses_synthetic_function = object_json.pop("uses_synthetic_function")
if uses_synthetic_function:
function_name = object_json.pop("function_name")
if function_name.startswith(synthetic_functions.FromBotorch.__name__):
raise NotImplementedError # TODO[Lena], pragma: no cover
else:
f = getattr(synthetic_functions, function_name)()
else:
f = pickle.loads(object_json.pop("f").encode())
domain = object_from_json(object_json.pop("domain"))
assert isinstance(domain, list) and all(
isinstance(x, (tuple, list)) for x in domain
)
return SimpleBenchmarkProblem(
f=f,
name=object_json.pop("name"),
domain=cast(List[Tuple[float, float]], domain),
minimize=object_json.pop("minimize"),
)
