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 typing import Callable, Dict, List, MutableMapping, Optional, Tuple
import numpy as np
import torch
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 ObservationFeatures
from ax.core.optimization_config import TRefPoint
from ax.core.outcome_constraint import ComparisonOp, OutcomeConstraint
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.parameter_constraint import ParameterConstraint
from ax.core.search_space import SearchSpace
from ax.core.trial import Trial
from ax.core.types import TBounds, TCandidateMetadata, TParamValue
from ax.modelbridge.transforms.base import Transform
from ax.utils.common.typeutils import not_none
from torch import Tensor
[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 get_bounds_and_task(
search_space: SearchSpace, param_names: List[str]
) -> Tuple[List[Tuple[float, float]], List[int], Dict[int, TParamValue]]:
"""Extract box bounds from a search space in the usual Scipy format.
Identify integer parameters as task features.
"""
bounds: List[Tuple[float, float]] = []
task_features: List[int] = []
target_fidelities: Dict[int, TParamValue] = {}
for i, p_name in enumerate(param_names):
p = search_space.parameters[p_name]
# Validation
if not isinstance(p, RangeParameter):
raise ValueError(f"{p} not RangeParameter")
elif p.log_scale:
raise ValueError(f"{p} is log scale")
# Set value
bounds.append((p.lower, p.upper))
if p.parameter_type == ParameterType.INT:
task_features.append(i)
if p.is_fidelity:
target_fidelities[i] = p.target_value
return bounds, task_features, target_fidelities
[docs]def extract_objective_thresholds(
objective_thresholds: TRefPoint, outcomes: List[str]
) -> np.ndarray:
"""Extracts objective thresholds' values, in the order of `outcomes`.
The extracted array will be no greater than the number of values in the
objective_thresholds, typically the same as number of objectives being
optimized.
Args:
objective_thresholds: Reference Point to extract values from.
outcomes: n-length list of names of metrics.
Returns:
len(objective_thresholds)-length list of reference point coordinates
"""
objective_threshold_dict = {ot.metric.name: ot.bound for ot in objective_thresholds}
return np.array(
[
objective_threshold_dict[name]
for name in outcomes
if name in objective_threshold_dict
]
)
[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.
"""
s = -1.0 if objective.minimize else 1.0
objective_weights = np.zeros(len(outcomes))
if isinstance(objective, ScalarizedObjective):
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_metric, obj_weight in objective.metric_weights:
# Rely on previously extracted lower_is_better weights not objective.
objective_weights[outcomes.index(obj_metric.name)] = obj_weight or s
else:
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
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]],
final_transform: Callable[[np.ndarray], Tensor] = torch.tensor,
) -> Tuple[
Tensor,
Optional[Tuple[Tensor, Tensor]],
Optional[Tuple[Tensor, Tensor]],
Optional[List[Tensor]],
]:
objective_weights: Tensor = final_transform(objective_weights)
if outcome_constraints is not None: # pragma: no cover
outcome_constraints: Tuple[Tensor, Tensor] = (
final_transform(outcome_constraints[0]),
final_transform(outcome_constraints[1]),
)
if linear_constraints is not None: # pragma: no cover
linear_constraints: Tuple[Tensor, Tensor] = (
final_transform(linear_constraints[0]),
final_transform(linear_constraints[1]),
)
if pending_observations is not None: # pragma: no cover
pending_observations: List[Tensor] = [
final_transform(pending_obs) for pending_obs in pending_observations
]
return (
objective_weights,
outcome_constraints,
linear_constraints,
pending_observations,
)
[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).
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.fetch_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_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)
)
)
if isinstance(trial, Trial):
if (
(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