#!/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.
import time
from abc import ABC
from collections import OrderedDict
from copy import deepcopy
from dataclasses import dataclass
from typing import Any, Dict, List, MutableMapping, Optional, Set, Tuple, Type
import numpy as np
from ax.core.arm import Arm
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun, extract_arm_predictions
from ax.core.observation import (
Observation,
ObservationData,
ObservationFeatures,
observations_from_data,
separate_observations,
)
from ax.core.optimization_config import (
OptimizationConfig,
)
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.core.types import (
TCandidateMetadata,
TGenMetadata,
TModelCov,
TModelMean,
TModelPredict,
)
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.cast import Cast
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast, not_none
logger = get_logger(__name__)
[docs]@dataclass
class BaseGenArgs:
search_space: SearchSpace
optimization_config: OptimizationConfig
pending_observations: Dict[str, List[ObservationFeatures]]
fixed_features: ObservationFeatures
[docs]class ModelBridge(ABC):
"""The main object for using models in Ax.
ModelBridge specifies 3 methods for using models:
- predict: Make model predictions. This method is not optimized for
speed and so should be used primarily for plotting or similar tasks
and not inside an optimization loop.
- gen: Use the model to generate new candidates.
- cross_validate: Do cross validation to assess model predictions.
ModelBridge converts Ax types like Data and Arm to types that are
meant to be consumed by the models. The data sent to the model will depend
on the implementation of the subclass, which will specify the actual API
for external model.
This class also applies a sequence of transforms to the input data and
problem specification which can be used to ensure that the external model
receives appropriate inputs.
Subclasses will implement what is here referred to as the "terminal
transform," which is a transform that changes types of the data and problem
specification.
"""
def __init__(
self,
search_space: SearchSpace,
model: Any,
transforms: Optional[List[Type[Transform]]] = None,
experiment: Optional[Experiment] = None,
data: Optional[Data] = None,
transform_configs: Optional[Dict[str, TConfig]] = None,
status_quo_name: Optional[str] = None,
status_quo_features: Optional[ObservationFeatures] = None,
optimization_config: Optional[OptimizationConfig] = None,
fit_out_of_design: bool = False,
fit_abandoned: bool = False,
) -> None:
"""
Applies transforms and fits model.
Args:
experiment: Is used to get arm parameters. Is not mutated.
search_space: Search space for fitting the model. Constraints need
not be the same ones used in gen.
data: Ax Data.
model: Interface will be specified in subclass. If model requires
initialization, that should be done prior to its use here.
transforms: List of uninitialized transform classes. Forward
transforms will be applied in this order, and untransforms in
the reverse order.
transform_configs: A dictionary from transform name to the
transform config dictionary.
status_quo_name: Name of the status quo arm. Can only be used if
Data has a single set of ObservationFeatures corresponding to
that arm.
status_quo_features: ObservationFeatures to use as status quo.
Either this or status_quo_name should be specified, not both.
optimization_config: Optimization config defining how to optimize
the model.
fit_out_of_design: If specified, all training data is returned.
Otherwise, only in design points are returned.
fit_abandoned: Whether data for abandoned arms or trials should be
included in model training data. If ``False``, only
non-abandoned points are returned.
"""
t_fit_start = time.time()
transforms = transforms or []
# pyre-ignore: Cast is a Tranform
transforms: List[Type[Transform]] = [Cast] + transforms
self._metric_names: Set[str] = set()
self._training_data: List[Observation] = []
self._optimization_config: Optional[OptimizationConfig] = optimization_config
self._training_in_design: List[bool] = []
self._status_quo: Optional[Observation] = None
self._arms_by_signature: Optional[Dict[str, Arm]] = None
self.transforms: MutableMapping[str, Transform] = OrderedDict()
self._model_key: Optional[str] = None
self._model_kwargs: Optional[Dict[str, Any]] = None
self._bridge_kwargs: Optional[Dict[str, Any]] = None
self._model_space = search_space.clone()
self._raw_transforms = transforms
self._transform_configs: Optional[Dict[str, TConfig]] = transform_configs
self._fit_out_of_design = fit_out_of_design
self._fit_abandoned = fit_abandoned
imm = experiment and experiment.immutable_search_space_and_opt_config
self._experiment_has_immutable_search_space_and_opt_config = imm
if experiment is not None:
if self._optimization_config is None:
self._optimization_config = experiment.optimization_config
self._arms_by_signature = experiment.arms_by_signature
observations = (
observations_from_data(
experiment=experiment,
data=data,
include_abandoned=self._fit_abandoned,
)
if experiment is not None and data is not None
else []
)
obs_feats_raw, obs_data_raw = self._set_training_data(
observations=observations, search_space=search_space
)
# Set model status quo
# NOTE: training data must be set before setting the status quo.
self._set_status_quo(
experiment=experiment,
status_quo_name=status_quo_name,
status_quo_features=status_quo_features,
)
obs_feats, obs_data, search_space = self._transform_data(
obs_feats=obs_feats_raw,
obs_data=obs_data_raw,
search_space=search_space,
transforms=transforms,
transform_configs=transform_configs,
)
# Save model, apply terminal transform, and fit
self.model = model
try:
self._fit(
model=model,
search_space=search_space,
observation_features=obs_feats,
observation_data=obs_data,
)
self.fit_time = time.time() - t_fit_start
self.fit_time_since_gen = float(self.fit_time)
except NotImplementedError:
self.fit_time = 0.0
self.fit_time_since_gen = 0.0
def _transform_data(
self,
obs_feats: List[ObservationFeatures],
obs_data: List[ObservationData],
search_space: SearchSpace,
transforms: Optional[List[Type[Transform]]],
transform_configs: Optional[Dict[str, TConfig]],
) -> Tuple[List[ObservationFeatures], List[ObservationData], SearchSpace]:
"""Initialize transforms and apply them to provided data."""
# Initialize transforms
search_space = search_space.clone()
if transforms is not None:
if transform_configs is None:
transform_configs = {}
for t in transforms:
t_instance = t(
search_space=search_space,
observation_features=obs_feats,
observation_data=obs_data,
modelbridge=self,
config=transform_configs.get(t.__name__, None),
)
search_space = t_instance.transform_search_space(search_space)
obs_feats = t_instance.transform_observation_features(obs_feats)
obs_data = t_instance.transform_observation_data(
obs_data,
obs_feats,
)
self.transforms[t.__name__] = t_instance
return obs_feats, obs_data, search_space
def _prepare_training_data(
self, observations: List[Observation]
) -> Tuple[List[ObservationFeatures], List[ObservationData]]:
observation_features, observation_data = separate_observations(observations)
if len(observation_features) != len(set(observation_features)):
raise ValueError(
"Observation features not unique."
"Something went wrong constructing training data..."
)
return observation_features, observation_data
def _set_training_data(
self, observations: List[Observation], search_space: SearchSpace
) -> Tuple[List[ObservationFeatures], List[ObservationData]]:
"""Store training data, not-transformed.
If the modelbridge specifies _fit_out_of_design, all training data is
returned. Otherwise, only in design points are returned.
"""
observation_features, observation_data = self._prepare_training_data(
observations=observations
)
self._training_data = deepcopy(observations)
self._metric_names: Set[str] = set()
for obsd in observation_data:
self._metric_names.update(obsd.metric_names)
return self._process_in_design(
search_space=search_space,
observation_features=observation_features,
observation_data=observation_data,
)
def _extend_training_data(
self, observations: List[Observation]
) -> Tuple[List[ObservationFeatures], List[ObservationData]]:
"""Extend and return training data, not-transformed.
If the modelbridge specifies _fit_out_of_design, all training data is
returned. Otherwise, only in design points are returned.
Args:
observations: New observations.
Returns:
observation_features: New + old observation features.
observation_data: New + old observation data.
"""
observation_features, observation_data = self._prepare_training_data(
observations=observations
)
for obsd in observation_data:
for metric_name in obsd.metric_names:
if metric_name not in self._metric_names:
raise ValueError(
f"Unrecognised metric {metric_name}; cannot update "
"training data with metrics that were not in the original "
"training data."
)
# Initialize with all points in design.
self._training_data.extend(deepcopy(observations))
all_observation_features, all_observation_data = separate_observations(
self.get_training_data()
)
return self._process_in_design(
search_space=self._model_space,
observation_features=all_observation_features,
observation_data=all_observation_data,
)
def _process_in_design(
self,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
) -> Tuple[List[ObservationFeatures], List[ObservationData]]:
"""Set training_in_design, and decide whether to filter out of design points."""
# Don't filter points.
if self._fit_out_of_design:
# Use all data for training
# Set training_in_design to True for all observations so that
# all observations are used in CV and plotting
self.training_in_design = [True] * len(observation_features)
return observation_features, observation_data
in_design = [
search_space.check_membership(obsf.parameters)
for obsf in observation_features
]
self.training_in_design = in_design
in_design_indices = [i for i, in_design in enumerate(in_design) if in_design]
in_design_features = [observation_features[i] for i in in_design_indices]
in_design_data = [observation_data[i] for i in in_design_indices]
return in_design_features, in_design_data
def _set_status_quo(
self,
experiment: Optional[Experiment],
status_quo_name: Optional[str],
status_quo_features: Optional[ObservationFeatures],
) -> None:
"""Set model status quo.
First checks for status quo in inputs status_quo_name and
status_quo_features. If neither of these is provided, checks the
experiment for a status quo. If that is set, it is handled by name in
the same way as input status_quo_name.
Args:
experiment: Experiment that will be checked for status quo.
status_quo_name: Name of status quo arm.
status_quo_features: Features for status quo.
"""
self._status_quo: Optional[Observation] = None
if (
status_quo_name is None
and status_quo_features is None
and experiment is not None
and experiment.status_quo is not None
):
status_quo_name = experiment.status_quo.name
if status_quo_name is not None:
if status_quo_features is not None:
raise ValueError(
"Specify either status_quo_name or status_quo_features, not both."
)
sq_obs = [
obs for obs in self._training_data if obs.arm_name == status_quo_name
]
if len(sq_obs) == 0:
logger.warning(f"Status quo {status_quo_name} not present in data")
elif len(sq_obs) > 1:
logger.warning( # pragma: no cover
f"Status quo {status_quo_name} found in data with multiple "
"features. Use status_quo_features to specify which to use."
)
else:
self._status_quo = sq_obs[0]
elif status_quo_features is not None:
sq_obs = [
obs
for obs in self._training_data
if (obs.features.parameters == status_quo_features.parameters)
and (obs.features.trial_index == status_quo_features.trial_index)
]
if len(sq_obs) == 0:
logger.warning(
f"Status quo features {status_quo_features} not found in data."
)
else:
# len(sq_obs) will not be > 1,
# unique features verified in _set_training_data.
self._status_quo = sq_obs[0]
@property
def status_quo(self) -> Optional[Observation]:
"""Observation corresponding to status quo, if any."""
return self._status_quo
@property
def metric_names(self) -> Set[str]:
"""Metric names present in training data."""
return self._metric_names
@property
def model_space(self) -> SearchSpace:
"""SearchSpace used to fit model."""
return self._model_space
[docs] def get_training_data(self) -> List[Observation]:
"""A copy of the (untransformed) data with which the model was fit."""
return deepcopy(self._training_data)
@property
def training_in_design(self) -> List[bool]:
"""For each observation in the training data, a bool indicating if it
is in-design for the model.
"""
return self._training_in_design
@training_in_design.setter
def training_in_design(self, training_in_design: List[bool]) -> None:
if len(training_in_design) != len(self._training_data):
raise ValueError(
f"In-design indicators not same length ({len(training_in_design)})"
f" as training data ({len(self._training_data)})."
)
# Identify out-of-design arms
if sum(training_in_design) < len(training_in_design):
ood_names = []
for i, obs in enumerate(self._training_data):
if not training_in_design[i] and obs.arm_name is not None:
ood_names.append(obs.arm_name)
ood_str = ", ".join(set(ood_names))
logger.info(f"Leaving out out-of-design observations for arms: {ood_str}")
self._training_in_design = training_in_design
def _fit(
self,
model: Any,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
) -> None:
"""Apply terminal transform and fit model."""
raise NotImplementedError # pragma: no cover
def _batch_predict(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationData]:
"""Predict a list of ObservationFeatures together."""
# Get modifiable version
observation_features = deepcopy(observation_features)
# Transform
for t in self.transforms.values():
observation_features = t.transform_observation_features(
observation_features
)
# Apply terminal transform and predict
observation_data = self._predict(observation_features)
# Apply reverse transforms, in reverse order
for t in reversed(self.transforms.values()): # noqa T484
observation_features = t.untransform_observation_features(
observation_features
)
observation_data = t.untransform_observation_data(
observation_data, observation_features
)
return observation_data
def _single_predict(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationData]:
"""Predict one ObservationFeature at a time."""
observation_data = []
for obsf in observation_features:
try:
obsd = self._batch_predict([obsf])
observation_data += obsd
except (TypeError, ValueError) as e:
# If the prediction is not out of design, this is a real error.
# Let's re-raise.
if self.model_space.check_membership(obsf.parameters):
logger.debug(obsf.parameters)
logger.debug(self.model_space)
raise e from None
# Prediction is out of design.
# Training data is untranformed already.
observation = next(
(
data
for data in self.get_training_data()
if obsf.parameters == data.features.parameters
and obsf.trial_index == data.features.trial_index
),
None,
)
if not observation:
raise ValueError(
"Out-of-design point could not be transformed, and was "
"not found in the training data."
)
observation_data.append(observation.data)
return observation_data
[docs] def predict(self, observation_features: List[ObservationFeatures]) -> TModelPredict:
"""Make model predictions (mean and covariance) for the given
observation features.
Predictions are made for all outcomes.
If an out-of-design observation can successfully be transformed,
the predicted value will be returned.
Othwerise, we will attempt to find that observation in the training data
and return the raw value.
Args:
observation_features: observation features
Returns:
2-element tuple containing
- Dictionary from metric name to list of mean estimates, in same
order as observation_features.
- Nested dictionary with cov['metric1']['metric2'] a list of
cov(metric1@x, metric2@x) for x in observation_features.
"""
# Predict in single batch.
try:
observation_data = self._batch_predict(observation_features)
# Predict one by one.
except (TypeError, ValueError):
observation_data = self._single_predict(observation_features)
f, cov = unwrap_observation_data(observation_data)
return f, cov
def _predict(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationData]:
"""Apply terminal transform, predict, and reverse terminal transform on
output.
"""
raise NotImplementedError # pragma: no cover
[docs] def update(self, new_data: Data, experiment: Experiment) -> None:
"""Update the model bridge and the underlying model with new data. This
method should be used instead of `fit`, in cases where the underlying
model does not need to be re-fit from scratch, but rather updated.
Note: `update` expects only new data (obtained since the model initialization
or last update) to be passed in, not all data in the experiment.
Args:
new_data: Data from the experiment obtained since the last call to
`update`.
experiment: Experiment, in which this data was obtained.
"""
t_update_start = time.time()
observations = (
observations_from_data(
experiment=experiment,
data=new_data,
include_abandoned=self._fit_abandoned,
)
if experiment is not None and new_data is not None
else []
)
obs_feats_raw, obs_data_raw = self._extend_training_data(
observations=observations
)
obs_feats, obs_data, search_space = self._transform_data(
obs_feats=obs_feats_raw,
obs_data=obs_data_raw,
search_space=self._model_space,
transforms=self._raw_transforms,
transform_configs=self._transform_configs,
)
self._update(
search_space=search_space,
observation_features=obs_feats,
observation_data=obs_data,
)
self.fit_time += time.time() - t_update_start
self.fit_time_since_gen += time.time() - t_update_start
def _update(
self,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
) -> None:
"""Apply terminal transform and update model.
Note: This function requires ALL observation_features and
observation_data observed thus far, not just the new data to update with.
Args:
observation_features: All observation features observed so far.
observation_data: All observation data observed so far.
"""
raise NotImplementedError # pragma: no cover
def _get_transformed_gen_args(
self,
search_space: SearchSpace,
optimization_config: Optional[OptimizationConfig] = None,
pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None,
fixed_features: Optional[ObservationFeatures] = None,
) -> BaseGenArgs:
if pending_observations is None:
pending_observations = {}
if fixed_features is None:
fixed_features = ObservationFeatures({})
if optimization_config is None:
optimization_config = (
self._optimization_config.clone()
if self._optimization_config is not None
else None
)
else:
optimization_config = optimization_config.clone()
# TODO(T34225037): replace deepcopy with native clone() in Ax
pending_observations = deepcopy(pending_observations)
fixed_features = deepcopy(fixed_features)
# Transform
for t in self.transforms.values():
search_space = t.transform_search_space(search_space)
if optimization_config is not None:
optimization_config = t.transform_optimization_config(
optimization_config=optimization_config,
modelbridge=self,
fixed_features=fixed_features,
)
for metric, po in pending_observations.items():
pending_observations[metric] = t.transform_observation_features(po)
fixed_features = t.transform_observation_features([fixed_features])[0]
return BaseGenArgs(
search_space=search_space,
# pyre-fixme[6]: Expected `OptimizationConfig` for 2nd param but got
# `Optional[OptimizationConfig]`.
optimization_config=optimization_config,
pending_observations=pending_observations,
fixed_features=fixed_features,
)
[docs] def gen(
self,
n: int,
search_space: Optional[SearchSpace] = None,
optimization_config: Optional[OptimizationConfig] = None,
pending_observations: Optional[Dict[str, List[ObservationFeatures]]] = None,
fixed_features: Optional[ObservationFeatures] = None,
model_gen_options: Optional[TConfig] = None,
) -> GeneratorRun:
"""
Args:
n: Number of points to generate
search_space: Search space
optimization_config: Optimization config
pending_observations: A map from metric name to pending
observations for that metric.
fixed_features: An ObservationFeatures object containing any
features that should be fixed at specified values during
generation.
model_gen_options: A config dictionary that is passed along to the
model.
"""
t_gen_start = time.monotonic()
# Get modifiable versions
if search_space is None:
search_space = self._model_space
orig_search_space = search_space
search_space = search_space.clone()
base_gen_args = self._get_transformed_gen_args(
search_space=search_space,
optimization_config=optimization_config,
pending_observations=pending_observations,
fixed_features=fixed_features,
)
# Apply terminal transform and gen
observation_features, weights, best_obsf, gen_metadata = self._gen(
n=n,
search_space=base_gen_args.search_space,
optimization_config=base_gen_args.optimization_config,
pending_observations=base_gen_args.pending_observations,
fixed_features=base_gen_args.fixed_features,
model_gen_options=model_gen_options,
)
# Apply reverse transforms
for t in reversed(self.transforms.values()): # noqa T484
observation_features = t.untransform_observation_features(
observation_features
)
if best_obsf is not None:
best_obsf = t.untransform_observation_features([best_obsf])[0]
# Clamp the untransformed data to the original search space if
# we don't fit/gen OOD points
if not self._fit_out_of_design:
observation_features = clamp_observation_features(
observation_features, orig_search_space
)
if best_obsf is not None:
best_obsf = clamp_observation_features([best_obsf], orig_search_space)[
0
]
best_point_predictions = None
try:
model_predictions = self.predict(observation_features)
if best_obsf is not None:
best_point_predictions = extract_arm_predictions(
model_predictions=self.predict([best_obsf]), arm_idx=0
)
except NotImplementedError: # pragma: no cover
model_predictions = None
if best_obsf is None:
best_arm = None
else:
best_arms, _ = gen_arms(
observation_features=[best_obsf],
arms_by_signature=self._arms_by_signature,
)
best_arm = best_arms[0]
arms, candidate_metadata = gen_arms(
observation_features=observation_features,
arms_by_signature=self._arms_by_signature,
)
# If experiment has immutable search space and metrics, no need to
# save them on generator runs.
immutable = getattr(
self, "_experiment_has_immutable_search_space_and_opt_config", False
)
optimization_config = None if immutable else base_gen_args.optimization_config
gr = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=optimization_config,
search_space=None if immutable else base_gen_args.search_space,
model_predictions=model_predictions,
best_arm_predictions=None
if best_arm is None
else (best_arm, best_point_predictions),
fit_time=self.fit_time_since_gen,
gen_time=time.monotonic() - t_gen_start,
model_key=self._model_key,
model_kwargs=self._model_kwargs,
bridge_kwargs=self._bridge_kwargs,
gen_metadata=gen_metadata,
model_state_after_gen=self._get_serialized_model_state(),
candidate_metadata_by_arm_signature=candidate_metadata,
)
self.fit_time_since_gen = 0.0
return gr
def _gen(
self,
n: int,
search_space: SearchSpace,
optimization_config: Optional[OptimizationConfig],
pending_observations: Dict[str, List[ObservationFeatures]],
fixed_features: ObservationFeatures,
model_gen_options: Optional[TConfig],
) -> Tuple[
List[ObservationFeatures],
List[float],
Optional[ObservationFeatures],
TGenMetadata,
]:
"""Apply terminal transform, gen, and reverse terminal transform on
output.
"""
raise NotImplementedError # pragma: no cover
[docs] def cross_validate(
self,
cv_training_data: List[Observation],
cv_test_points: List[ObservationFeatures],
) -> List[ObservationData]:
"""Make a set of cross-validation predictions.
Args:
cv_training_data: The training data to use for cross validation.
cv_test_points: The test points at which predictions will be made.
Returns:
A list of predictions at the test points.
"""
# Apply transforms to cv_training_data and cv_test_points
cv_test_points = deepcopy(cv_test_points)
obs_feats, obs_data = separate_observations(
observations=cv_training_data, copy=True
)
search_space = self._model_space.clone()
for t in self.transforms.values():
obs_feats = t.transform_observation_features(obs_feats)
obs_data = t.transform_observation_data(
obs_data,
obs_feats,
)
cv_test_points = t.transform_observation_features(cv_test_points)
search_space = t.transform_search_space(search_space)
# Apply terminal transform, and get predictions.
cv_predictions = self._cross_validate(
search_space=search_space,
obs_feats=obs_feats,
obs_data=obs_data,
cv_test_points=cv_test_points,
)
# Apply reverse transforms, in reverse order
for t in reversed(self.transforms.values()):
cv_test_points = t.untransform_observation_features(cv_test_points)
cv_predictions = t.untransform_observation_data(
cv_predictions, cv_test_points
)
return cv_predictions
def _cross_validate(
self,
search_space: SearchSpace,
obs_feats: List[ObservationFeatures],
obs_data: List[ObservationData],
cv_test_points: List[ObservationFeatures],
) -> List[ObservationData]:
"""Apply the terminal transform, make predictions on the test points,
and reverse terminal transform on the results.
"""
raise NotImplementedError # pragma: no cover
[docs] def evaluate_acquisition_function(
self,
observation_features: List[ObservationFeatures],
search_space: Optional[SearchSpace] = None,
optimization_config: Optional[OptimizationConfig] = None,
objective_thresholds: Optional[np.ndarray] = None,
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
linear_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[np.ndarray]] = None,
acq_options: Optional[Dict[str, Any]] = None,
) -> List[float]:
"""Evaluate the acquisition function for given set of observation
features.
Args:
observation_features: A list of observation features, representing
parameterizations, for which to evaluate the acquisition function.
search_space: Search space for fitting the model.
optimization_config: Optimization config defining how to optimize
the model.
objective_thresholds: The `m`-dim tensor of objective thresholds. There is
one for each modeled metric.
outcome_constraints: A tuple of (A, b). For k outcome constraints and m
outputs at f(x), A is (k x m) and b is (k x 1) such that A f(x) <= b.
(Not used by single task models)
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that A x <= b.
fixed_features: A map {feature_index: value} for features that should be
held fixed during the evaluation.
pending_observations: A list of m (k_i x d) feature tensors X for m
outcomes and k_i pending observations for outcome i.
acq_options: Keyword arguments used to contruct the acquisition function.
Returns:
A list of acquisition function values, in the same order as the
input observation features.
"""
search_space = self._get_transformed_gen_args(
search_space=search_space or self._model_space,
).search_space
optimization_config = optimization_config or self._optimization_config
if optimization_config is None:
raise ValueError(
"The `optimization_config` must be specified either while initializing "
"the ModelBridge or to the `evaluate_acquisition_function` call."
)
obs_feats = deepcopy(observation_features)
for t in self.transforms.values():
obs_feats = t.transform_observation_features(obs_feats)
return self._evaluate_acquisition_function(
observation_features=obs_feats,
search_space=search_space,
optimization_config=optimization_config,
objective_thresholds=objective_thresholds,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
pending_observations=pending_observations,
acq_options=acq_options,
)
def _evaluate_acquisition_function(
self,
observation_features: List[ObservationFeatures],
search_space: SearchSpace,
optimization_config: OptimizationConfig,
objective_thresholds: Optional[np.ndarray] = None,
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
linear_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[np.ndarray]] = None,
acq_options: Optional[Dict[str, Any]] = None,
) -> List[float]:
raise NotImplementedError # pragma: no cover
def _set_kwargs_to_save(
self,
model_key: str,
model_kwargs: Dict[str, Any],
bridge_kwargs: Dict[str, Any],
) -> None:
"""Set properties used to save the model that created a given generator
run, on the `GeneratorRun` object. Each generator run produced by the
`gen` method of this model bridge will have the model key and kwargs
fields set as provided in arguments to this function.
"""
self._model_key = model_key
self._model_kwargs = model_kwargs
self._bridge_kwargs = bridge_kwargs
def _get_serialized_model_state(self) -> Dict[str, Any]:
"""Obtains the state of the underlying model (if using a stateful one)
in a readily JSON-serializable form.
"""
model = not_none(self.model)
return model.serialize_state(raw_state=model._get_state())
def _deserialize_model_state(
self, serialized_state: Dict[str, Any]
) -> Dict[str, Any]:
model = not_none(self.model)
return model.deserialize_state(serialized_state=serialized_state)
[docs] def feature_importances(self, metric_name: str) -> Dict[str, float]:
raise NotImplementedError(
"Feature importance not available for this model type"
)
def _transform_observation_data(
self, observation_data: List[ObservationData]
) -> Any:
"""Apply terminal transform to given observation features and return result."""
raise NotImplementedError # pragma: no cover
def _transform_observation_features(
self, observation_features: List[ObservationFeatures]
) -> Any:
"""Apply terminal transform to given observation features and return result."""
raise NotImplementedError # pragma: no cover
[docs]def unwrap_observation_data(observation_data: List[ObservationData]) -> TModelPredict:
"""Converts observation data to the format for model prediction outputs.
That format assumes each observation data has the same set of metrics.
"""
metrics = set(observation_data[0].metric_names)
f: TModelMean = {metric: [] for metric in metrics}
cov: TModelCov = {m1: {m2: [] for m2 in metrics} for m1 in metrics}
for od in observation_data:
if set(od.metric_names) != metrics:
raise ValueError(
"Each ObservationData should use same set of metrics. "
"Expected {exp}, got {got}.".format(
exp=metrics, got=set(od.metric_names)
)
)
for i, m1 in enumerate(od.metric_names):
f[m1].append(od.means[i])
for j, m2 in enumerate(od.metric_names):
cov[m1][m2].append(od.covariance[i, j])
return f, cov
[docs]def gen_arms(
observation_features: List[ObservationFeatures],
arms_by_signature: Optional[Dict[str, Arm]] = None,
) -> Tuple[List[Arm], Optional[Dict[str, TCandidateMetadata]]]:
"""Converts observation features to a tuple of arms list and candidate metadata
dict, where arm signatures are mapped to their respective candidate metadata.
"""
# TODO(T34225939): handle static context (which is stored on observation_features)
arms = []
candidate_metadata = {}
for of in observation_features:
arm = Arm(parameters=of.parameters)
if arms_by_signature is not None and arm.signature in arms_by_signature:
existing_arm = arms_by_signature[arm.signature]
arm = Arm(name=existing_arm.name, parameters=existing_arm.parameters)
arms.append(arm)
if of.metadata:
candidate_metadata[arm.signature] = of.metadata
return arms, candidate_metadata or None # None if empty cand. metadata.
[docs]def clamp_observation_features(
observation_features: List[ObservationFeatures], search_space: SearchSpace
) -> List[ObservationFeatures]:
range_parameters = [
p for p in search_space.parameters.values() if isinstance(p, RangeParameter)
]
for obsf in observation_features:
for p in range_parameters:
if p.name not in obsf.parameters:
continue
if p.parameter_type == ParameterType.FLOAT:
val = checked_cast(float, obsf.parameters[p.name])
else:
val = checked_cast(int, obsf.parameters[p.name])
if val < p.lower:
logger.info(
f"Untransformed parameter {val} "
f"less than lower bound {p.lower}, clamping"
)
obsf.parameters[p.name] = p.lower
elif val > p.upper:
logger.info(
f"Untransformed parameter {val} "
f"greater than upper bound {p.upper}, clamping"
)
obsf.parameters[p.name] = p.upper
return observation_features
