Source code for ax.modelbridge.modelbridge_utils
#!/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 __future__ import annotations
from collections import defaultdict
from functools import partial
from typing import (
TYPE_CHECKING,
Callable,
Dict,
List,
MutableMapping,
Optional,
Tuple,
Union,
)
import numpy as np
import torch
from ax.core.base_trial import TrialStatus
from ax.core.batch_trial import BatchTrial
from ax.core.experiment import Experiment
from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
from ax.core.observation import Observation, ObservationData, ObservationFeatures
from ax.core.optimization_config import MultiObjectiveOptimizationConfig, TRefPoint
from ax.core.outcome_constraint import (
ComparisonOp,
OutcomeConstraint,
ScalarizedOutcomeConstraint,
)
from ax.core.parameter import ChoiceParameter, ParameterType, RangeParameter
from ax.core.parameter_constraint import ParameterConstraint
from ax.core.search_space import SearchSpace, SearchSpaceDigest
from ax.core.trial import Trial
from ax.core.types import TBounds, TCandidateMetadata
from ax.modelbridge.transforms.base import Transform
from ax.models.torch.frontier_utils import (
get_weighted_mc_objective_and_objective_thresholds,
get_default_frontier_evaluator,
)
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import (
checked_cast,
checked_cast_optional,
not_none,
checked_cast_to_tuple,
)
from botorch.utils.multi_objective.hypervolume import Hypervolume
from torch import Tensor
logger = get_logger(__name__)
if TYPE_CHECKING:
# import as module to make sphinx-autodoc-typehints happy
from ax import modelbridge as modelbridge_module # noqa F401 # pragma: no cover
[docs]def extract_parameter_constraints(
parameter_constraints: List[ParameterConstraint], param_names: List[str]
) -> Optional[TBounds]:
"""Extract parameter constraints."""
if len(parameter_constraints) > 0:
A = np.zeros((len(parameter_constraints), len(param_names)))
b = np.zeros((len(parameter_constraints), 1))
for i, c in enumerate(parameter_constraints):
b[i, 0] = c.bound
for name, val in c.constraint_dict.items():
A[i, param_names.index(name)] = val
linear_constraints: TBounds = (A, b)
else:
linear_constraints = None
return linear_constraints
[docs]def extract_search_space_digest(
search_space: SearchSpace, param_names: List[str]
) -> SearchSpaceDigest:
"""Extract basic parameter prpoerties from a search space."""
bounds: List[Tuple[Union[int, float], Union[int, float]]] = []
ordinal_features: List[int] = []
categorical_features: List[int] = []
discrete_choices: Dict[int, List[Union[int, float]]] = {}
task_features: List[int] = []
fidelity_features: List[int] = []
target_fidelities: Dict[int, Union[int, float]] = {}
for i, p_name in enumerate(param_names):
p = search_space.parameters[p_name]
if isinstance(p, ChoiceParameter):
if p.is_task:
task_features.append(i)
elif p.is_ordered:
ordinal_features.append(i)
else:
categorical_features.append(i)
# at this point we can assume that values are numeric due to transforms
discrete_choices[i] = p.values # pyre-ignore [6]
bounds.append((min(p.values), max(p.values))) # pyre-ignore [6]
elif isinstance(p, RangeParameter):
if p.log_scale:
raise ValueError(f"{p} is log scale")
if p.parameter_type == ParameterType.INT:
ordinal_features.append(i)
d_choices = list(range(int(p.lower), int(p.upper) + 1))
discrete_choices[i] = d_choices # pyre-ignore [6]
bounds.append((p.lower, p.upper))
else:
raise ValueError(f"Unknown parameter type {type(p)}")
if p.is_fidelity:
if not isinstance(not_none(p.target_value), (int, float)):
raise NotImplementedError("Only numerical target values are supported.")
target_fidelities[i] = checked_cast_to_tuple((int, float), p.target_value)
fidelity_features.append(i)
return SearchSpaceDigest(
feature_names=param_names,
bounds=bounds,
ordinal_features=ordinal_features,
categorical_features=categorical_features,
discrete_choices=discrete_choices,
task_features=task_features,
fidelity_features=fidelity_features,
target_fidelities=target_fidelities,
)
[docs]def extract_objective_thresholds(
objective_thresholds: TRefPoint,
objective: Objective,
outcomes: List[str],
) -> Optional[np.ndarray]:
"""Extracts objective thresholds' values, in the order of `outcomes`.
Will return None if no objective thresholds, otherwise the extracted tensor
will be the same length as `outcomes`.
If one objective threshold is specified, they must be specified for every
metric in the objective.
Outcomes that are not part of an objective will be given a threshold of 0
in this tensor, under the assumption that its value will not be used. Note
that setting it to 0 for an outcome that is part of the objective would be
incorrect, hence we validate that all objective metrics are represented.
Args:
objective_thresholds: Objective thresholds to extract values from.
objective: The corresponding Objective, for validation purposes.
outcomes: n-length list of names of metrics.
Returns:
(n,) array of thresholds
"""
if len(objective_thresholds) == 0:
return None
objective_threshold_dict = {}
for ot in objective_thresholds:
if ot.relative:
raise ValueError(
f"Objective {ot.metric.name} has a relative threshold that is not "
f"supported here."
)
objective_threshold_dict[ot.metric.name] = ot.bound
if len(objective_threshold_dict) != len(objective.metrics):
raise ValueError(
"Objective thresholds do not match number of objective metrics."
)
# Initialize these to be nan to make sure that objective thresholds for
# non-objective metrics are never used
obj_t = np.full(len(outcomes), float("nan"))
for metric in objective.metrics:
if metric.name not in objective_threshold_dict:
raise ValueError(
f"Objective threshold not specified for {metric.name}. Thresholds must "
f"be specified for all objective metrics or for none."
)
obj_t[outcomes.index(metric.name)] = objective_threshold_dict[metric.name]
return obj_t
[docs]def extract_objective_weights(objective: Objective, outcomes: List[str]) -> np.ndarray:
"""Extract a weights for objectives.
Weights are for a maximization problem.
Give an objective weight to each modeled outcome. Outcomes that are modeled
but not part of the objective get weight 0.
In the single metric case, the objective is given either +/- 1, depending
on the minimize flag.
In the multiple metric case, each objective is given the input weight,
multiplied by the minimize flag.
Args:
objective: Objective to extract weights from.
outcomes: n-length list of names of metrics.
Returns:
n-length list of weights.
"""
objective_weights = np.zeros(len(outcomes))
if isinstance(objective, ScalarizedObjective):
s = -1.0 if objective.minimize else 1.0
for obj_metric, obj_weight in objective.metric_weights:
objective_weights[outcomes.index(obj_metric.name)] = obj_weight * s
elif isinstance(objective, MultiObjective):
for obj, obj_weight in objective.objective_weights:
s = -1.0 if obj.minimize else 1.0
objective_weights[outcomes.index(obj.metric.name)] = obj_weight * s
else:
s = -1.0 if objective.minimize else 1.0
objective_weights[outcomes.index(objective.metric.name)] = s
return objective_weights
[docs]def extract_outcome_constraints(
outcome_constraints: List[OutcomeConstraint], outcomes: List[str]
) -> TBounds:
# Extract outcome constraints
if len(outcome_constraints) > 0:
A = np.zeros((len(outcome_constraints), len(outcomes)))
b = np.zeros((len(outcome_constraints), 1))
for i, c in enumerate(outcome_constraints):
s = 1 if c.op == ComparisonOp.LEQ else -1
if isinstance(c, ScalarizedOutcomeConstraint):
for c_metric, c_weight in c.metric_weights:
j = outcomes.index(c_metric.name)
A[i, j] = s * c_weight
else:
j = outcomes.index(c.metric.name)
A[i, j] = s
b[i, 0] = s * c.bound
outcome_constraint_bounds: TBounds = (A, b)
else:
outcome_constraint_bounds = None
return outcome_constraint_bounds
[docs]def validate_and_apply_final_transform(
objective_weights: np.ndarray,
outcome_constraints: Optional[Tuple[np.ndarray, np.ndarray]],
linear_constraints: Optional[Tuple[np.ndarray, np.ndarray]],
pending_observations: Optional[List[np.ndarray]],
objective_thresholds: Optional[np.ndarray] = None,
final_transform: Callable[[np.ndarray], Tensor] = torch.tensor,
) -> Tuple[
Tensor,
Optional[Tuple[Tensor, Tensor]],
Optional[Tuple[Tensor, Tensor]],
Optional[List[Tensor]],
Optional[Tensor],
]:
# TODO: use some container down the road (similar to
# SearchSpaceDigest) to limit the return arguments
# pyre-fixme[35]: Target cannot be annotated.
objective_weights: Tensor = final_transform(objective_weights)
if outcome_constraints is not None: # pragma: no cover
# pyre-fixme[35]: Target cannot be annotated.
outcome_constraints: Tuple[Tensor, Tensor] = (
final_transform(outcome_constraints[0]),
final_transform(outcome_constraints[1]),
)
if linear_constraints is not None: # pragma: no cover
# pyre-fixme[35]: Target cannot be annotated.
linear_constraints: Tuple[Tensor, Tensor] = (
final_transform(linear_constraints[0]),
final_transform(linear_constraints[1]),
)
if pending_observations is not None: # pragma: no cover
# pyre-fixme[35]: Target cannot be annotated.
pending_observations: List[Tensor] = [
final_transform(pending_obs) for pending_obs in pending_observations
]
if objective_thresholds is not None:
# pyre-fixme[35]: Target cannot be annotated.
objective_thresholds: Tensor = final_transform(objective_thresholds)
return (
objective_weights,
outcome_constraints,
linear_constraints,
pending_observations,
objective_thresholds,
)
[docs]def get_fixed_features(
fixed_features: ObservationFeatures, param_names: List[str]
) -> Optional[Dict[int, float]]:
"""Reformat a set of fixed_features."""
fixed_features_dict = {}
for p_name, val in fixed_features.parameters.items():
# These all need to be floats at this point.
# pyre-ignore[6]: All float here.
val_ = float(val)
fixed_features_dict[param_names.index(p_name)] = val_
fixed_features_dict = fixed_features_dict if len(fixed_features_dict) > 0 else None
return fixed_features_dict
[docs]def pending_observations_as_array(
pending_observations: Dict[str, List[ObservationFeatures]],
outcome_names: List[str],
param_names: List[str],
) -> Optional[List[np.ndarray]]:
"""Re-format pending observations.
Args:
pending_observations: List of raw numpy pending observations.
outcome_names: List of outcome names.
param_names: List fitted param names.
Returns:
Filtered pending observations data, by outcome and param names.
"""
if len(pending_observations) == 0:
pending_array: Optional[List[np.ndarray]] = None
else:
pending_array = [np.array([]) for _ in outcome_names]
for metric_name, po_list in pending_observations.items():
# It is possible that some metrics attached to the experiment should
# not be included in pending features for a given model. For example,
# if a model is fit to the initial data that is missing some of the
# metrics on the experiment or if a model just should not be fit for
# some of the metrics attached to the experiment, so metrics that
# appear in pending_observations (drawn from an experiment) but not
# in outcome_names (metrics, expected for the model) are filtered out.ß
if metric_name not in outcome_names:
continue
pending_array[outcome_names.index(metric_name)] = np.array(
[[po.parameters[p] for p in param_names] for po in po_list]
)
return pending_array
[docs]def parse_observation_features(
X: np.ndarray,
param_names: List[str],
candidate_metadata: Optional[List[TCandidateMetadata]] = None,
) -> List[ObservationFeatures]:
"""Re-format raw model-generated candidates into ObservationFeatures.
Args:
param_names: List of param names.
X: Raw np.ndarray of candidate values.
candidate_metadata: Model's metadata for candidates it produced.
Returns:
List of candidates, represented as ObservationFeatures.
"""
if candidate_metadata and len(candidate_metadata) != len(X):
raise ValueError( # pragma: no cover
"Observations metadata list provided is not of "
"the same size as the number of candidates."
)
observation_features = []
for i, x in enumerate(X):
observation_features.append(
ObservationFeatures(
parameters=dict(zip(param_names, x)),
metadata=candidate_metadata[i] if candidate_metadata else None,
)
)
return observation_features
[docs]def transform_callback(
param_names: List[str], transforms: MutableMapping[str, Transform]
) -> Callable[[np.ndarray], np.ndarray]:
"""A closure for performing the `round trip` transformations.
The function round points by de-transforming points back into
the original space (done by applying transforms in reverse), and then
re-transforming them.
This function is specifically for points which are formatted as numpy
arrays. This function is passed to _model_gen.
Args:
param_names: Names of parameters to transform.
transforms: Ordered set of transforms which were applied to the points.
Returns:
a function with for performing the roundtrip transform.
"""
def _roundtrip_transform(x: np.ndarray) -> np.ndarray:
"""Inner function for performing aforementioned functionality.
Args:
x: points in the transformed space (e.g. all transforms have been applied
to them)
Returns:
points in the transformed space, but rounded via the original space.
"""
# apply reverse terminal transform to turn array to ObservationFeatures
observation_features = [
ObservationFeatures(
parameters={p: float(x[i]) for i, p in enumerate(param_names)}
)
]
# reverse loop through the transforms and do untransform
for t in reversed(transforms.values()):
observation_features = t.untransform_observation_features(
observation_features
)
# forward loop through the transforms and do transform
for t in transforms.values():
observation_features = t.transform_observation_features(
observation_features
)
# parameters are guaranteed to be float compatible here, but pyre doesn't know
new_x: List[float] = [
# pyre-fixme[6]: Expected `Union[_SupportsIndex, bytearray, bytes, str,
# typing.SupportsFloat]` for 1st param but got `Union[None, bool, float,
# int, str]`.
float(observation_features[0].parameters[p])
for p in param_names
]
# turn it back into an array
return np.array(new_x)
return _roundtrip_transform
[docs]def get_pending_observation_features(
experiment: Experiment, include_failed_as_pending: bool = False
) -> Optional[Dict[str, List[ObservationFeatures]]]:
"""Computes a list of pending observation features (corresponding to arms that
have been generated and deployed in the course of the experiment, but have not
been completed with data or to arms that have been abandoned or belong to
abandoned trials).
NOTE: Pending observation features are passed to the model to
instruct it to not generate the same points again.
Args:
experiment: Experiment, pending features on which we seek to compute.
include_failed_as_pending: Whether to include failed trials as pending
(for example, to avoid the model suggesting them again).
Returns:
An optional mapping from metric names to a list of observation features,
pending for that metric (i.e. do not have evaluation data for that metric).
If there are no pending features for any of the metrics, return is None.
"""
pending_features = {}
# Note that this assumes that if a metric appears in fetched data, the trial is
# not pending for the metric. Where only the most recent data matters, this will
# work, but may need to add logic to check previously added data objects, too.
for trial_index, trial in experiment.trials.items():
dat = trial.lookup_data()
for metric_name in experiment.metrics:
if metric_name not in pending_features:
pending_features[metric_name] = []
include_since_failed = include_failed_as_pending and trial.status.is_failed
if isinstance(trial, BatchTrial):
if trial.status.is_abandoned or (
(trial.status.is_deployed or include_since_failed)
and metric_name not in dat.df.metric_name.values
and trial.arms is not None
):
for arm in trial.arms:
not_none(pending_features.get(metric_name)).append(
ObservationFeatures.from_arm(
arm=arm, trial_index=np.int64(trial_index)
)
)
abandoned_arms = trial.abandoned_arms
for abandoned_arm in abandoned_arms:
not_none(pending_features.get(metric_name)).append(
ObservationFeatures.from_arm(
arm=abandoned_arm, trial_index=np.int64(trial_index)
)
)
if isinstance(trial, Trial):
if trial.status.is_abandoned or (
(trial.status.is_deployed or include_since_failed)
and metric_name not in dat.df.metric_name.values
and trial.arm is not None
):
not_none(pending_features.get(metric_name)).append(
ObservationFeatures.from_arm(
arm=not_none(trial.arm), trial_index=np.int64(trial_index)
)
)
return pending_features if any(x for x in pending_features.values()) else None
[docs]def get_pending_observation_features_based_on_trial_status(
experiment: Experiment,
) -> Optional[Dict[str, List[ObservationFeatures]]]:
"""A faster analogue of ``get_pending_observation_features`` that makes
assumptions about trials in experiment in order to speed up extraction
of pending points.
Assumptions:
* All arms in all trials in ``STAGED,`` ``RUNNING`` and ``ABANDONED`` statuses
are to be considered pending for all outcomes.
* All arms in all trials in other statuses are to be considered not pending for
all outcomes.
This entails:
* No actual data-fetching for trials to determine whether arms in them are pending
for specific outcomes.
* Even if data is present for some outcomes in ``RUNNING`` trials, their arms will
still be considered pending for those outcomes.
NOTE: This function should not be used to extract pending features in field
experiments, where arms in running trials should not be considered pending if
there is data for those arms.
Args:
experiment: Experiment, pending features on which we seek to compute.
Returns:
An optional mapping from metric names to a list of observation features,
pending for that metric (i.e. do not have evaluation data for that metric).
If there are no pending features for any of the metrics, return is None.
"""
pending_features = defaultdict(list)
for status in [TrialStatus.STAGED, TrialStatus.RUNNING, TrialStatus.ABANDONED]:
for trial in experiment.trials_by_status[status]:
for metric_name in experiment.metrics:
pending_features[metric_name].extend(
ObservationFeatures.from_arm(
arm=arm, trial_index=np.int64(trial.index)
)
for arm in trial.arms
)
return dict(pending_features) if any(x for x in pending_features.values()) else None
[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
[docs]def get_pareto_frontier_and_transformed_configs(
modelbridge: modelbridge_module.array.ArrayModelBridge,
observation_features: List[ObservationFeatures],
observation_data: Optional[List[ObservationData]] = None,
objective_thresholds: Optional[TRefPoint] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
arm_names: Optional[List[Optional[str]]] = None,
use_model_predictions: bool = True,
) -> Tuple[List[Observation], Tensor, Tensor, Optional[Tensor]]:
"""Helper that applies transforms and calls frontier_evaluator.
Returns transformed configs in addition to the Pareto observations.
Args:
modelbridge: Modelbridge used to predict metrics outcomes.
observation_features: observation features to predict, if provided and
use_model_predictions is True.
observation_data: data for computing the Pareto front, unless features
are provided and model_predictions is True.
objective_thresholds: metric values bounding the region of interest in
the objective outcome space.
optimization_config: Optimization config.
arm_names: Arm names for each observation.
use_model_predictions: If True, will use model predictions at
observation_features to compute Pareto front, if provided. If False,
will use observation_data directly to compute Pareto front, regardless
of whether observation_features are provided.
Returns:
frontier_observations: Observations of points on the pareto frontier.
f: n x m tensor representation of the Pareto frontier values where n is the
length of frontier_observations and m is the number of metrics.
obj_w: m tensor of objective weights.
obj_t: m tensor of objective thresholds corresponding to Y, or None if no
objective thresholds used.
"""
array_to_tensor = partial(_array_to_tensor, modelbridge=modelbridge)
X = (
modelbridge.transform_observation_features(observation_features)
if use_model_predictions
else None
)
X = array_to_tensor(X) if X is not None else None
Y, Yvar = (None, None)
if observation_data is not None:
Y, Yvar = modelbridge.transform_observation_data(observation_data)
Y, Yvar = (array_to_tensor(Y), array_to_tensor(Yvar))
if arm_names is None:
arm_names = [None] * len(observation_features)
# Optimization_config
mooc = optimization_config or checked_cast_optional(
MultiObjectiveOptimizationConfig, modelbridge._optimization_config
)
if not mooc:
raise ValueError(
(
"Experiment must have an existing optimization_config "
"of type `MultiObjectiveOptimizationConfig` "
"or `optimization_config` must be passed as an argument."
)
)
if not isinstance(mooc, MultiObjectiveOptimizationConfig):
mooc = not_none(MultiObjectiveOptimizationConfig.from_opt_conf(mooc))
if objective_thresholds:
mooc = mooc.clone_with_args(objective_thresholds=objective_thresholds)
optimization_config = mooc
# Transform OptimizationConfig.
optimization_config = modelbridge.transform_optimization_config(
optimization_config=optimization_config,
fixed_features=ObservationFeatures(parameters={}),
)
# Extract weights, constraints, and objective_thresholds
objective_weights = extract_objective_weights(
objective=optimization_config.objective, outcomes=modelbridge.outcomes
)
outcome_constraints = extract_outcome_constraints(
outcome_constraints=optimization_config.outcome_constraints,
outcomes=modelbridge.outcomes,
)
obj_t = extract_objective_thresholds(
objective_thresholds=optimization_config.objective_thresholds,
objective=optimization_config.objective,
outcomes=modelbridge.outcomes,
)
obj_t = array_to_tensor(obj_t)
# Transform to tensors.
obj_w, oc_c, _, _, _ = validate_and_apply_final_transform(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=None,
pending_observations=None,
final_transform=array_to_tensor,
)
frontier_evaluator = get_default_frontier_evaluator()
# pyre-ignore[28]: Unexpected keyword `modelbridge` to anonymous call
f, cov, indx = frontier_evaluator(
model=modelbridge.model,
X=X,
Y=Y,
Yvar=Yvar,
objective_thresholds=obj_t,
objective_weights=obj_w,
outcome_constraints=oc_c,
)
f, cov = f.detach().cpu().clone(), cov.detach().cpu().clone()
indx = indx.tolist()
frontier_observation_data = array_to_observation_data(
f=f.numpy(), cov=cov.numpy(), outcomes=not_none(modelbridge.outcomes)
)
# Untransform observations
for t in reversed(modelbridge.transforms.values()): # noqa T484
frontier_observation_data = t.untransform_observation_data(
frontier_observation_data, []
)
# Construct observations
frontier_observations = []
for i, obsd in enumerate(frontier_observation_data):
frontier_observations.append(
Observation(
features=observation_features[indx[i]],
data=obsd,
arm_name=arm_names[indx[i]],
)
)
return frontier_observations, f, obj_w.cpu(), obj_t.cpu()
[docs]def pareto_frontier(
modelbridge: modelbridge_module.array.ArrayModelBridge,
observation_features: List[ObservationFeatures],
observation_data: Optional[List[ObservationData]] = None,
objective_thresholds: Optional[TRefPoint] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
arm_names: Optional[List[Optional[str]]] = None,
use_model_predictions: bool = True,
) -> List[Observation]:
"""Helper that applies transforms and calls frontier_evaluator.
Args:
modelbridge: Modelbridge used to predict metrics outcomes.
observation_features: observation features to predict, if provided and
use_model_predictions is True.
observation_data: data for computing the Pareto front, unless features
are provided and model_predictions is True.
objective_thresholds: metric values bounding the region of interest in
the objective outcome space.
optimization_config: Optimization config.
arm_names: Arm names for each observation.
use_model_predictions: If True, will use model predictions at
observation_features to compute Pareto front, if provided. If False,
will use observation_data directly to compute Pareto front, regardless
of whether observation_features are provided.
Returns:
frontier_observations: Observations of points on the pareto frontier.
"""
return get_pareto_frontier_and_transformed_configs(
modelbridge=modelbridge,
observation_features=observation_features,
observation_data=observation_data,
objective_thresholds=objective_thresholds,
optimization_config=optimization_config,
arm_names=arm_names,
use_model_predictions=use_model_predictions,
)[0]
[docs]def predicted_pareto_frontier(
modelbridge: modelbridge_module.array.ArrayModelBridge,
objective_thresholds: Optional[TRefPoint] = None,
observation_features: Optional[List[ObservationFeatures]] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> List[Observation]:
"""Generate a pareto frontier based on the posterior means of given
observation features.
Given a model and features to evaluate use the model to predict which points
lie on the pareto frontier.
Args:
modelbridge: Modelbridge used to predict metrics outcomes.
objective_thresholds: metric values bounding the region of interest in
the objective outcome space.
observation_features: observation features to predict. Model's training
data used by default if unspecified.
optimization_config: Optimization config
Returns:
Observations representing points on the pareto frontier.
"""
if observation_features is None:
observation_features = []
arm_names = []
for obs in modelbridge.get_training_data():
observation_features.append(obs.features)
arm_names.append(obs.arm_name)
else:
arm_names = None
if not observation_features:
raise ValueError(
"Must receive observation_features as input or the model must "
"have training data."
)
pareto_observations = pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
optimization_config=optimization_config,
arm_names=arm_names,
)
return pareto_observations
[docs]def observed_pareto_frontier(
modelbridge: modelbridge_module.array.ArrayModelBridge,
objective_thresholds: Optional[TRefPoint] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> List[Observation]:
"""Generate a pareto frontier based on observed data.
Given observed data, return those outcomes in the pareto frontier.
Args:
modelbridge: Modelbridge that holds previous training data.
objective_thresholds: metric values bounding the region of interest in
the objective outcome space.
optimization_config: Optimization config
Returns:
Data representing points on the pareto frontier.
"""
# Get observation_data from current training data
observation_data = []
observation_features = []
arm_names = []
for obs in modelbridge.get_training_data():
observation_data.append(obs.data)
observation_features.append(obs.features)
arm_names.append(obs.arm_name)
pareto_observations = pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_data=observation_data,
observation_features=observation_features,
optimization_config=optimization_config,
arm_names=arm_names,
use_model_predictions=False,
)
return pareto_observations
[docs]def hypervolume(
modelbridge: modelbridge_module.array.ArrayModelBridge,
observation_features: List[ObservationFeatures],
objective_thresholds: Optional[TRefPoint] = None,
observation_data: Optional[List[ObservationData]] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
use_model_predictions: bool = True,
) -> float:
"""Helper function that computes hypervolume of a given list of outcomes."""
# Get Pareto front
observations, f, obj_w, obj_t = get_pareto_frontier_and_transformed_configs(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
observation_data=observation_data,
optimization_config=optimization_config,
use_model_predictions=use_model_predictions,
)
if obj_t is None:
raise ValueError(
"Cannot compute hypervolume without having objective thresholds specified."
)
# Apply appropriate weights and thresholds
obj, obj_t = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=obj_w, objective_thresholds=obj_t
)
f_t = obj(f)
hv = Hypervolume(ref_point=obj_t)
return hv.compute(f_t)
[docs]def predicted_hypervolume(
modelbridge: modelbridge_module.array.ArrayModelBridge,
objective_thresholds: Optional[TRefPoint] = None,
observation_features: Optional[List[ObservationFeatures]] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> float:
"""Calculate hypervolume of a pareto frontier based on the posterior means of
given observation features.
Given a model and features to evaluate calculate the hypervolume of the pareto
frontier formed from their predicted outcomes.
Args:
modelbridge: Modelbridge used to predict metrics outcomes.
objective_thresholds: point defining the origin of hyperrectangles that
can contribute to hypervolume.
observation_features: observation features to predict. Model's training
data used by default if unspecified.
optimization_config: Optimization config
Returns:
calculated hypervolume.
"""
observation_features = (
observation_features
if observation_features is not None
else [obs.features for obs in modelbridge.get_training_data()]
)
if not observation_features:
raise ValueError(
"Must receive observation_features as input or the model must "
"have training data."
)
return hypervolume(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
optimization_config=optimization_config,
)
[docs]def observed_hypervolume(
modelbridge: modelbridge_module.array.ArrayModelBridge,
objective_thresholds: Optional[TRefPoint] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> float:
"""Calculate hypervolume of a pareto frontier based on observed data.
Given observed data, return the hypervolume of the pareto frontier formed from
those outcomes.
Args:
modelbridge: Modelbridge that holds previous training data.
objective_thresholds: point defining the origin of hyperrectangles that
can contribute to hypervolume.
observation_features: observation features to predict. Model's training
data used by default if unspecified.
optimization_config: Optimization config
Returns:
(float) calculated hypervolume.
"""
# Get observation_data from current training data.
observation_data = [obs.data for obs in modelbridge.get_training_data()]
observation_features = [obs.features for obs in modelbridge.get_training_data()]
return hypervolume(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
observation_data=observation_data,
optimization_config=optimization_config,
use_model_predictions=False,
)
[docs]def array_to_observation_data(
f: np.ndarray, cov: np.ndarray, outcomes: List[str]
) -> List[ObservationData]:
"""Convert arrays of model predictions to a list of ObservationData.
Args:
f: An (n x m) array
cov: An (n x m x m) array
outcomes: A list of d outcome names
Returns: A list of n ObservationData
"""
observation_data = []
for i in range(f.shape[0]):
observation_data.append(
ObservationData(
metric_names=list(outcomes),
means=f[i, :].copy(),
covariance=cov[i, :, :].copy(),
)
)
return observation_data
[docs]def observation_data_to_array(
outcomes: List[str],
observation_data: List[ObservationData],
) -> Tuple[np.ndarray, np.ndarray]:
"""Convert a list of Observation data to arrays.
Args:
observation_data: A list of n ObservationData
Returns:
An array of n ObservationData, each containing
- f: An (n x m) array
- cov: An (n x m x m) array
"""
means = []
cov = []
for obsd in observation_data:
metric_idxs = np.array([obsd.metric_names.index(m) for m in outcomes])
means.append(obsd.means[metric_idxs])
cov.append(obsd.covariance[metric_idxs][:, metric_idxs])
return np.array(means), np.array(cov)
[docs]def observation_features_to_array(
parameters: List[str], obsf: List[ObservationFeatures]
) -> np.ndarray:
"""Convert a list of Observation features to arrays."""
return np.array([[of.parameters[p] for p in parameters] for of in obsf])
def _array_to_tensor(
array: Union[np.ndarray, List[float]],
modelbridge: Optional[modelbridge_module.base.ModelBridge] = None,
) -> Tensor:
if modelbridge and hasattr(modelbridge, "_array_to_tensor"):
# pyre-ignore[16]: modelbridge does not have attribute `_array_to_tensor`
return modelbridge._array_to_tensor(array)
else:
return torch.tensor(array)