#!/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.
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import List, Optional, Tuple, Union
import pandas as pd
from ax.core.arm import Arm
from ax.core.base_trial import BaseTrial, TrialStatus
from ax.core.batch_trial import AbandonedArm, BatchTrial, GeneratorRunStruct
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun, GeneratorRunType
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 ObjectiveThreshold, OutcomeConstraint
from ax.core.parameter import ChoiceParameter, FixedParameter, Parameter, RangeParameter
from ax.core.parameter_constraint import (
OrderConstraint,
ParameterConstraint,
SumConstraint,
)
from ax.core.runner import Runner
from ax.core.search_space import SearchSpace
from ax.core.simple_experiment import SimpleExperiment
from ax.core.trial import Trial
from ax.exceptions.storage import SQADecodeError
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.modelbridge.registry import Models
from ax.storage.json_store.decoder import object_from_json
from ax.storage.metric_registry import REVERSE_METRIC_REGISTRY
from ax.storage.runner_registry import REVERSE_RUNNER_REGISTRY
from ax.storage.sqa_store.sqa_classes import (
SQAAbandonedArm,
SQAArm,
SQAData,
SQAExperiment,
SQAGenerationStrategy,
SQAGeneratorRun,
SQAMetric,
SQAParameter,
SQAParameterConstraint,
SQARunner,
SQATrial,
)
from ax.storage.sqa_store.sqa_config import SQAConfig
from ax.storage.utils import DomainType, MetricIntent, ParameterConstraintType
from ax.utils.common.constants import Keys
from ax.utils.common.typeutils import not_none
[docs]class Decoder:
"""Class that contains methods for loading an Ax experiment from SQLAlchemy.
Instantiate with an instance of Config to customize the functionality.
For even more flexibility, create a subclass.
Attributes:
config: Metadata needed to save and load an experiment to SQLAlchemy.
"""
def __init__(self, config: SQAConfig) -> None:
self.config = config
[docs] def get_enum_name(
self, value: Optional[int], enum: Optional[Enum]
) -> Optional[str]:
"""Given an enum value (int) and an enum (of ints), return the
corresponding enum name. If the value is not present in the enum,
throw an error.
"""
if value is None or enum is None:
return None
try:
return enum(value).name # pyre-ignore T29651755
except ValueError:
raise SQADecodeError(f"Value {value} is invalid for enum {enum}.")
def _init_experiment_from_sqa(self, experiment_sqa: SQAExperiment) -> Experiment:
"""First step of conversion within experiment_from_sqa."""
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=experiment_sqa.metrics
)
search_space = self.search_space_from_sqa(
parameters_sqa=experiment_sqa.parameters,
parameter_constraints_sqa=experiment_sqa.parameter_constraints,
)
if search_space is None:
raise SQADecodeError( # pragma: no cover
"Experiment SearchSpace cannot be None."
)
status_quo = (
Arm(
# pyre-fixme[6]: Expected `Dict[str, Optional[Union[bool, float,
# int, str]]]` for 1st param but got `Optional[Dict[str,
# Optional[Union[bool, float, int, str]]]]`.
parameters=experiment_sqa.status_quo_parameters,
name=experiment_sqa.status_quo_name,
)
if experiment_sqa.status_quo_parameters is not None
else None
)
if len(experiment_sqa.runners) == 0:
runner = None
elif len(experiment_sqa.runners) == 1:
runner = self.runner_from_sqa(experiment_sqa.runners[0])
else:
raise ValueError( # pragma: no cover
"Multiple runners on experiment "
"only supported for MultiTypeExperiment."
)
# `experiment_sqa.properties` is `sqlalchemy.ext.mutable.MutableDict`
# so need to convert it to regular dict.
properties = dict(experiment_sqa.properties or {})
# Remove 'subclass' from experiment's properties, since its only
# used for decoding to the correct experiment subclass in storage.
subclass = properties.pop(Keys.SUBCLASS, None)
if subclass == "SimpleExperiment":
if opt_config is None:
raise SQADecodeError( # pragma: no cover
"SimpleExperiment must have an optimization config."
)
experiment = SimpleExperiment(
name=experiment_sqa.name,
search_space=search_space,
objective_name=opt_config.objective.metric.name,
minimize=opt_config.objective.minimize,
outcome_constraints=opt_config.outcome_constraints,
status_quo=status_quo,
properties=properties,
)
experiment.description = experiment_sqa.description
experiment.is_test = experiment_sqa.is_test
else:
experiment = Experiment(
name=experiment_sqa.name,
description=experiment_sqa.description,
search_space=search_space,
optimization_config=opt_config,
tracking_metrics=tracking_metrics,
runner=runner,
status_quo=status_quo,
is_test=experiment_sqa.is_test,
properties=properties,
)
return experiment
def _init_mt_experiment_from_sqa(
self, experiment_sqa: SQAExperiment
) -> MultiTypeExperiment:
"""First step of conversion within experiment_from_sqa."""
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=experiment_sqa.metrics
)
search_space = self.search_space_from_sqa(
parameters_sqa=experiment_sqa.parameters,
parameter_constraints_sqa=experiment_sqa.parameter_constraints,
)
if search_space is None:
raise SQADecodeError( # pragma: no cover
"Experiment SearchSpace cannot be None."
)
status_quo = (
Arm(
# pyre-fixme[6]: Expected `Dict[str, Optional[Union[bool, float,
# int, str]]]` for 1st param but got `Optional[Dict[str,
# Optional[Union[bool, float, int, str]]]]`.
parameters=experiment_sqa.status_quo_parameters,
name=experiment_sqa.status_quo_name,
)
if experiment_sqa.status_quo_parameters is not None
else None
)
trial_type_to_runner = {
not_none(sqa_runner.trial_type): self.runner_from_sqa(sqa_runner)
for sqa_runner in experiment_sqa.runners
}
default_trial_type = not_none(experiment_sqa.default_trial_type)
properties = experiment_sqa.properties
if properties:
# Remove 'subclass' from experiment's properties, since its only
# used for decoding to the correct experiment subclass in storage.
properties.pop(Keys.SUBCLASS, None)
experiment = MultiTypeExperiment(
name=experiment_sqa.name,
description=experiment_sqa.description,
search_space=search_space,
default_trial_type=default_trial_type,
default_runner=trial_type_to_runner[default_trial_type],
optimization_config=opt_config,
status_quo=status_quo,
properties=properties,
)
experiment._trial_type_to_runner = trial_type_to_runner
sqa_metric_dict = {metric.name: metric for metric in experiment_sqa.metrics}
for tracking_metric in tracking_metrics:
sqa_metric = sqa_metric_dict[tracking_metric.name]
experiment.add_tracking_metric(
tracking_metric,
trial_type=not_none(sqa_metric.trial_type),
canonical_name=sqa_metric.canonical_name,
)
return experiment
[docs] def experiment_from_sqa(
self, experiment_sqa: SQAExperiment, reduced_state: bool = False
) -> Experiment:
"""Convert SQLAlchemy Experiment to Ax Experiment.
Args:
experiment_sqa: `SQAExperiment` to decode.
reduced_state: Whether to load experiment with a slightly reduced state
(without abandoned arms on experiment and without model state,
search space, and optimization config on generator runs).
"""
subclass = (experiment_sqa.properties or {}).get(Keys.SUBCLASS)
if subclass == "MultiTypeExperiment":
experiment = self._init_mt_experiment_from_sqa(experiment_sqa)
else:
experiment = self._init_experiment_from_sqa(experiment_sqa)
trials = [
self.trial_from_sqa(
trial_sqa=trial, experiment=experiment, reduced_state=reduced_state
)
for trial in experiment_sqa.trials
]
data_by_trial = defaultdict(dict)
for data_sqa in experiment_sqa.data:
trial_index = data_sqa.trial_index
timestamp = data_sqa.time_created
data_by_trial[trial_index][timestamp] = self.data_from_sqa(
data_sqa=data_sqa
)
data_by_trial = {
trial_index: OrderedDict(sorted(data_by_timestamp.items()))
for trial_index, data_by_timestamp in data_by_trial.items()
}
experiment._trials = {trial.index: trial for trial in trials}
experiment._arms_by_name = {}
for trial in trials:
if trial.ttl_seconds is not None:
experiment._trials_have_ttl = True
for arm in trial.arms:
experiment._register_arm(arm)
if experiment.status_quo is not None:
sq = not_none(experiment.status_quo)
experiment._register_arm(sq)
experiment._time_created = experiment_sqa.time_created
experiment._experiment_type = self.get_enum_name(
value=experiment_sqa.experiment_type, enum=self.config.experiment_type_enum
)
experiment._data_by_trial = dict(data_by_trial)
experiment.db_id = experiment_sqa.id
return experiment
[docs] def parameter_from_sqa(self, parameter_sqa: SQAParameter) -> Parameter:
"""Convert SQLAlchemy Parameter to Ax Parameter."""
if parameter_sqa.domain_type == DomainType.RANGE:
if parameter_sqa.lower is None or parameter_sqa.upper is None:
raise SQADecodeError( # pragma: no cover
"`lower` and `upper` must be set for RangeParameter."
)
parameter = RangeParameter(
name=parameter_sqa.name,
parameter_type=parameter_sqa.parameter_type,
# pyre-fixme[6]: Expected `float` for 3rd param but got
# `Optional[float]`.
lower=parameter_sqa.lower,
upper=parameter_sqa.upper,
log_scale=parameter_sqa.log_scale or False,
digits=parameter_sqa.digits,
is_fidelity=parameter_sqa.is_fidelity or False,
target_value=parameter_sqa.target_value,
)
elif parameter_sqa.domain_type == DomainType.CHOICE:
if parameter_sqa.choice_values is None:
raise SQADecodeError( # pragma: no cover
"`values` must be set for ChoiceParameter."
)
parameter = ChoiceParameter(
name=parameter_sqa.name,
parameter_type=parameter_sqa.parameter_type,
# pyre-fixme[6]: Expected `List[Optional[Union[bool, float, int,
# str]]]` for 3rd param but got `Optional[List[Optional[Union[bool,
# float, int, str]]]]`.
values=parameter_sqa.choice_values,
is_fidelity=parameter_sqa.is_fidelity or False,
target_value=parameter_sqa.target_value,
)
elif parameter_sqa.domain_type == DomainType.FIXED:
# Don't throw an error if parameter_sqa.fixed_value is None;
# that might be the actual value!
parameter = FixedParameter(
name=parameter_sqa.name,
parameter_type=parameter_sqa.parameter_type,
value=parameter_sqa.fixed_value,
is_fidelity=parameter_sqa.is_fidelity or False,
target_value=parameter_sqa.target_value,
)
else:
raise SQADecodeError(
f"Cannot decode SQAParameter because {parameter_sqa.domain_type} "
"is an invalid domain type."
)
parameter.db_id = parameter_sqa.id
return parameter
[docs] def parameter_constraint_from_sqa(
self,
parameter_constraint_sqa: SQAParameterConstraint,
parameters: List[Parameter],
) -> ParameterConstraint:
"""Convert SQLAlchemy ParameterConstraint to Ax ParameterConstraint."""
parameter_map = {p.name: p for p in parameters}
if parameter_constraint_sqa.type == ParameterConstraintType.ORDER:
lower_name = None
upper_name = None
for k, v in parameter_constraint_sqa.constraint_dict.items():
if v == 1:
lower_name = k
elif v == -1:
upper_name = k
if not lower_name or not upper_name:
raise SQADecodeError(
"Cannot decode SQAParameterConstraint because `lower_name` or "
"`upper_name` was not found."
)
# pyre-fixme[6]: Expected `str` for 1st param but got `None`.
lower_parameter = parameter_map[lower_name]
# pyre-fixme[6]: Expected `str` for 1st param but got `None`.
upper_parameter = parameter_map[upper_name]
constraint = OrderConstraint(
lower_parameter=lower_parameter, upper_parameter=upper_parameter
)
elif parameter_constraint_sqa.type == ParameterConstraintType.SUM:
# This operation is potentially very inefficient.
# It is O(#constrained_parameters * #total_parameters)
parameter_names = list(parameter_constraint_sqa.constraint_dict.keys())
constraint_parameters = [
next(
search_space_param
for search_space_param in parameters
if search_space_param.name == c_p_name
)
for c_p_name in parameter_names
]
a_values = list(parameter_constraint_sqa.constraint_dict.values())
if len(a_values) == 0:
raise SQADecodeError(
"Cannot decode SQAParameterConstraint because `constraint_dict` "
"is empty."
)
a = a_values[0]
is_upper_bound = a == 1
bound = parameter_constraint_sqa.bound * a
constraint = SumConstraint(
parameters=constraint_parameters,
is_upper_bound=is_upper_bound,
bound=bound,
)
else:
constraint = ParameterConstraint(
constraint_dict=dict(parameter_constraint_sqa.constraint_dict),
bound=parameter_constraint_sqa.bound,
)
constraint.db_id = parameter_constraint_sqa.id
return constraint
[docs] def search_space_from_sqa(
self,
parameters_sqa: List[SQAParameter],
parameter_constraints_sqa: List[SQAParameterConstraint],
) -> Optional[SearchSpace]:
"""Convert a list of SQLAlchemy Parameters and ParameterConstraints to an
Ax SearchSpace.
"""
parameters = [
self.parameter_from_sqa(parameter_sqa=parameter_sqa)
for parameter_sqa in parameters_sqa
]
parameter_constraints = [
self.parameter_constraint_from_sqa(
parameter_constraint_sqa=parameter_constraint_sqa, parameters=parameters
)
for parameter_constraint_sqa in parameter_constraints_sqa
]
if len(parameters) == 0:
return None
return SearchSpace(
parameters=parameters, parameter_constraints=parameter_constraints
)
[docs] def metric_from_sqa_util(self, metric_sqa: SQAMetric) -> Metric:
"""Convert SQLAlchemy Metric to Ax Metric"""
metric_class = REVERSE_METRIC_REGISTRY.get(metric_sqa.metric_type)
if metric_class is None:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.metric_type} "
f"is an invalid type."
)
args = metric_sqa.properties or {}
args["name"] = metric_sqa.name
args["lower_is_better"] = metric_sqa.lower_is_better
args = metric_class.deserialize_init_args(args=args)
metric = metric_class(**args)
metric.db_id = metric_sqa.id
return metric
[docs] def metric_from_sqa(
self, metric_sqa: SQAMetric
) -> Union[Metric, Objective, OutcomeConstraint]:
"""Convert SQLAlchemy Metric to Ax Metric, Objective, or OutcomeConstraint."""
metric = self.metric_from_sqa_util(metric_sqa)
if metric_sqa.intent == MetricIntent.TRACKING:
return metric
elif metric_sqa.intent == MetricIntent.OBJECTIVE:
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Objective because minimize is None."
)
if metric_sqa.scalarized_objective_weight is not None:
raise SQADecodeError( # pragma: no cover
"The metric corresponding to regular objective does not \
have weight attribute"
)
return Objective(metric=metric, minimize=metric_sqa.minimize)
elif (
metric_sqa.intent == MetricIntent.MULTI_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in MultiObjective
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to MultiObjective \
because minimize is None."
)
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to MultiObjective \
because the parent metric has no children metrics."
)
# Extracting metric and weight for each child
metrics = [
self.metric_from_sqa_util(child) for child in metrics_sqa_children
]
return MultiObjective(
metrics=list(metrics),
# pyre-fixme[6]: Expected `bool` for 2nd param but got `Optional[bool]`.
minimize=metric_sqa.minimize,
)
elif (
metric_sqa.intent == MetricIntent.SCALARIZED_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in Scalarized Objective
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because minimize is None."
)
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because the parent metric has no children metrics."
)
# Extracting metric and weight for each child
metrics, weights = zip(
*[
(
self.metric_from_sqa_util(child),
child.scalarized_objective_weight,
)
for child in metrics_sqa_children
]
)
return ScalarizedObjective(
metrics=list(metrics),
weights=list(weights),
# pyre-fixme[6]: Expected `bool` for 3nd param but got `Optional[bool]`.
minimize=metric_sqa.minimize,
)
elif metric_sqa.intent == MetricIntent.OUTCOME_CONSTRAINT:
if (
metric_sqa.bound is None
or metric_sqa.op is None
or metric_sqa.relative is None
):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to OutcomeConstraint because "
"bound, op, or relative is None."
)
return OutcomeConstraint(
metric=metric,
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
op=metric_sqa.op,
relative=metric_sqa.relative,
)
elif metric_sqa.intent == MetricIntent.OBJECTIVE_THRESHOLD:
if metric_sqa.bound is None or metric_sqa.relative is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to ObjectiveThreshold because "
"bound, op, or relative is None."
)
return ObjectiveThreshold(
metric=metric,
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
relative=metric_sqa.relative,
op=metric_sqa.op,
)
else:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.intent} "
f"is an invalid intent."
)
[docs] def opt_config_and_tracking_metrics_from_sqa(
self, metrics_sqa: List[SQAMetric]
) -> Tuple[Optional[OptimizationConfig], List[Metric]]:
"""Convert a list of SQLAlchemy Metrics to a a tuple of Ax OptimizationConfig
and tracking metrics.
"""
objective = None
objective_thresholds = []
outcome_constraints = []
tracking_metrics = []
for metric_sqa in metrics_sqa:
metric = self.metric_from_sqa(metric_sqa=metric_sqa)
if isinstance(metric, Objective):
objective = metric
elif isinstance(metric, ObjectiveThreshold):
objective_thresholds.append(metric)
elif isinstance(metric, OutcomeConstraint):
outcome_constraints.append(metric)
else:
tracking_metrics.append(metric)
if objective is None:
return None, tracking_metrics
if objective_thresholds or type(objective) == MultiObjective:
optimization_config = MultiObjectiveOptimizationConfig(
objective=objective,
outcome_constraints=outcome_constraints,
objective_thresholds=objective_thresholds,
)
else:
optimization_config = OptimizationConfig(
objective=objective, outcome_constraints=outcome_constraints
)
return (optimization_config, tracking_metrics)
[docs] def arm_from_sqa(self, arm_sqa: SQAArm) -> Arm:
"""Convert SQLAlchemy Arm to Ax Arm."""
arm = Arm(parameters=arm_sqa.parameters, name=arm_sqa.name)
arm.db_id = arm_sqa.id
return arm
[docs] def abandoned_arm_from_sqa(
self, abandoned_arm_sqa: SQAAbandonedArm
) -> AbandonedArm:
"""Convert SQLAlchemy AbandonedArm to Ax AbandonedArm."""
return AbandonedArm(
name=abandoned_arm_sqa.name,
reason=abandoned_arm_sqa.abandoned_reason,
time=abandoned_arm_sqa.time_abandoned,
)
[docs] def generator_run_from_sqa(
self, generator_run_sqa: SQAGeneratorRun, reduced_state: bool = False
) -> GeneratorRun:
"""Convert SQLAlchemy GeneratorRun to Ax GeneratorRun.
Args:
generator_run_sqa: `SQAGeneratorRun` to decode.
reduced_state: Whether to load generator runs with a slightly reduced state
(without model state, search space, and optimization config).
"""
arms = []
weights = []
opt_config = None
search_space = None
for arm_sqa in generator_run_sqa.arms:
arms.append(self.arm_from_sqa(arm_sqa=arm_sqa))
weights.append(arm_sqa.weight)
if not reduced_state:
(
opt_config,
tracking_metrics,
) = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=generator_run_sqa.metrics
)
if len(tracking_metrics) > 0:
raise SQADecodeError( # pragma: no cover
"GeneratorRun should not have tracking metrics."
)
search_space = self.search_space_from_sqa(
parameters_sqa=generator_run_sqa.parameters,
parameter_constraints_sqa=generator_run_sqa.parameter_constraints,
)
best_arm_predictions = None
model_predictions = None
if (
generator_run_sqa.best_arm_parameters is not None
and generator_run_sqa.best_arm_predictions is not None
):
best_arm = Arm(
name=generator_run_sqa.best_arm_name,
parameters=not_none(generator_run_sqa.best_arm_parameters),
)
best_arm_predictions = (
best_arm,
tuple(not_none(generator_run_sqa.best_arm_predictions)),
)
model_predictions = (
tuple(not_none(generator_run_sqa.model_predictions))
if generator_run_sqa.model_predictions is not None
else None
)
generator_run = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=opt_config,
search_space=search_space,
fit_time=generator_run_sqa.fit_time,
gen_time=generator_run_sqa.gen_time,
best_arm_predictions=best_arm_predictions, # pyre-ignore[6]
model_predictions=model_predictions,
model_key=generator_run_sqa.model_key,
model_kwargs=None
if reduced_state
else object_from_json(generator_run_sqa.model_kwargs),
bridge_kwargs=None
if reduced_state
else object_from_json(generator_run_sqa.bridge_kwargs),
gen_metadata=None
if reduced_state
else object_from_json(generator_run_sqa.gen_metadata),
model_state_after_gen=None
if reduced_state
else object_from_json(generator_run_sqa.model_state_after_gen),
generation_step_index=generator_run_sqa.generation_step_index,
candidate_metadata_by_arm_signature=object_from_json(
generator_run_sqa.candidate_metadata_by_arm_signature
),
)
generator_run._time_created = generator_run_sqa.time_created
generator_run._generator_run_type = self.get_enum_name(
value=generator_run_sqa.generator_run_type,
enum=self.config.generator_run_type_enum,
)
generator_run._index = generator_run_sqa.index
generator_run.db_id = generator_run_sqa.id
return generator_run
[docs] def generation_strategy_from_sqa(
self,
gs_sqa: SQAGenerationStrategy,
experiment: Optional[Experiment] = None,
reduced_state: bool = False,
) -> GenerationStrategy:
"""Convert SQALchemy generation strategy to Ax `GenerationStrategy`."""
steps = object_from_json(gs_sqa.steps)
gs = GenerationStrategy(name=gs_sqa.name, steps=steps)
gs._curr = gs._steps[gs_sqa.curr_index]
if reduced_state and gs_sqa.generator_runs:
# Only fully load the last of the generator runs, load the rest with
# reduced state.
gs._generator_runs = [
self.generator_run_from_sqa(generator_run_sqa=gr, reduced_state=True)
for gr in gs_sqa.generator_runs[:-1]
]
gs._generator_runs.append(
self.generator_run_from_sqa(
generator_run_sqa=gs_sqa.generator_runs[-1], reduced_state=False
)
)
else:
gs._generator_runs = [
self.generator_run_from_sqa(gr) for gr in gs_sqa.generator_runs
]
if len(gs._generator_runs) > 0:
# Generation strategy had an initialized model.
if experiment is None:
raise SQADecodeError(
"Cannot decode a generation strategy with a non-zero number of "
"generator runs without an experiment."
)
gs._experiment = experiment
# 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)
else:
gs._restore_model_from_generator_run()
gs.db_id = gs_sqa.id
return gs
[docs] def runner_from_sqa(self, runner_sqa: SQARunner) -> Runner:
"""Convert SQLAlchemy Runner to Ax Runner."""
runner_class = REVERSE_RUNNER_REGISTRY.get(runner_sqa.runner_type)
if runner_class is None:
raise SQADecodeError(
f"Cannot decode SQARunner because {runner_sqa.runner_type} "
f"is an invalid type."
)
args = runner_class.deserialize_init_args(args=runner_sqa.properties or {})
# pyre-ignore[45]: Cannot instantiate abstract class `Runner`.
runner = runner_class(**args)
runner.db_id = runner_sqa.id
return runner
[docs] def trial_from_sqa(
self, trial_sqa: SQATrial, experiment: Experiment, reduced_state: bool = False
) -> BaseTrial:
"""Convert SQLAlchemy Trial to Ax Trial.
Args:
trial_sqa: `SQATrial` to decode.
reduced_state: Whether to load trial's generator run(s) with a slightly
reduced state (without model state, search space, and optimization config).
"""
if trial_sqa.is_batch:
trial = BatchTrial(
experiment=experiment,
optimize_for_power=trial_sqa.optimize_for_power,
ttl_seconds=trial_sqa.ttl_seconds,
index=trial_sqa.index,
)
generator_run_structs = [
GeneratorRunStruct(
generator_run=self.generator_run_from_sqa(
generator_run_sqa=generator_run_sqa,
reduced_state=reduced_state,
),
weight=generator_run_sqa.weight or 1.0,
)
for generator_run_sqa in trial_sqa.generator_runs
]
if trial_sqa.status_quo_name is not None:
new_generator_run_structs = []
for struct in generator_run_structs:
if (
struct.generator_run.generator_run_type
== GeneratorRunType.STATUS_QUO.name
):
status_quo_weight = struct.generator_run.weights[0]
trial._status_quo = struct.generator_run.arms[0]
trial._status_quo_weight_override = status_quo_weight
else:
new_generator_run_structs.append(struct)
generator_run_structs = new_generator_run_structs
trial._generator_run_structs = generator_run_structs
if not reduced_state:
trial._abandoned_arms_metadata = {
abandoned_arm_sqa.name: self.abandoned_arm_from_sqa(
abandoned_arm_sqa=abandoned_arm_sqa
)
for abandoned_arm_sqa in trial_sqa.abandoned_arms
}
else:
trial = Trial(
experiment=experiment,
ttl_seconds=trial_sqa.ttl_seconds,
index=trial_sqa.index,
)
if trial_sqa.generator_runs:
if len(trial_sqa.generator_runs) != 1:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQATrial to Trial because trial is not batched "
"but has more than one generator run."
)
trial._generator_run = self.generator_run_from_sqa(
generator_run_sqa=trial_sqa.generator_runs[0],
reduced_state=reduced_state,
)
trial._trial_type = trial_sqa.trial_type
# Swap `DISPATCHED` for `RUNNING`, since `DISPATCHED` is deprecated and nearly
# equivalent to `RUNNING`.
trial._status = (
trial_sqa.status
if trial_sqa.status != TrialStatus.DISPATCHED
else TrialStatus.RUNNING
)
trial._time_created = trial_sqa.time_created
trial._time_completed = trial_sqa.time_completed
trial._time_staged = trial_sqa.time_staged
trial._time_run_started = trial_sqa.time_run_started
trial._abandoned_reason = trial_sqa.abandoned_reason
# pyre-fixme[9]: _run_metadata has type `Dict[str, Any]`; used as
# `Optional[Dict[str, Any]]`.
trial._run_metadata = (
# pyre-fixme[6]: Expected `Mapping[Variable[_KT], Variable[_VT]]` for
# 1st param but got `Optional[Dict[str, typing.Any]]`.
dict(trial_sqa.run_metadata)
if trial_sqa.run_metadata is not None
else None
)
trial._num_arms_created = trial_sqa.num_arms_created
trial._runner = (
self.runner_from_sqa(trial_sqa.runner) if trial_sqa.runner else None
)
trial._generation_step_index = trial_sqa.generation_step_index
trial._properties = trial_sqa.properties or {}
trial.db_id = trial_sqa.id
return trial
[docs] def data_from_sqa(self, data_sqa: SQAData) -> Data:
"""Convert SQLAlchemy Data to AE Data."""
dat = Data(
description=data_sqa.description,
# NOTE: Need dtype=False, otherwise infers arm_names like
# "4_1" should be int 41.
df=pd.read_json(data_sqa.data_json, dtype=False),
)
dat.db_id = data_sqa.id
return dat
