# 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.
# pyre-strict
from typing import Any, Optional
import numpy as np
import torch
from ax.core.base_trial import TrialStatus
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.map_data import MapData
from ax.core.observation import (
Observation,
ObservationData,
ObservationFeatures,
observations_from_map_data,
separate_observations,
)
from ax.core.optimization_config import OptimizationConfig
from ax.core.search_space import SearchSpace
from ax.core.types import TCandidateMetadata
from ax.modelbridge.base import GenResults
from ax.modelbridge.modelbridge_utils import (
array_to_observation_data,
observation_features_to_array,
parse_observation_features,
)
from ax.modelbridge.torch import FIT_MODEL_ERROR, TorchModelBridge
from ax.modelbridge.transforms.base import Transform
from ax.models.torch_base import TorchModel
from ax.models.types import TConfig
from ax.utils.common.constants import Keys
from ax.utils.common.typeutils import not_none
# A mapping from map_key to its target (or final) value; by default,
# we assume normalization to [0, 1], making 1.0 the target value.
# Used in both generation and prediction.
DEFAULT_TARGET_MAP_VALUES = {"steps": 1.0}
[docs]class MapTorchModelBridge(TorchModelBridge):
"""A model bridge for using torch-based models that fit on MapData. Most
of the `TorchModelBridge` functionality is retained, except that this
class should be used in the case where `model` makes use of map_key values.
For example, the use case of fitting a joint surrogate model on
`(parameters, map_key)`, while candidate generation is only for `parameters`.
"""
def __init__(
self,
experiment: Experiment,
search_space: SearchSpace,
data: Data,
model: TorchModel,
transforms: list[type[Transform]],
transform_configs: Optional[dict[str, TConfig]] = None,
torch_dtype: Optional[torch.dtype] = None,
torch_device: Optional[torch.device] = 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_on_init: bool = True,
fit_abandoned: bool = False,
default_model_gen_options: Optional[TConfig] = None,
map_data_limit_rows_per_metric: Optional[int] = None,
map_data_limit_rows_per_group: Optional[int] = None,
) -> 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.
torch_dtype: Torch data type.
torch_device: Torch device.
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_on_init: Whether to fit the model on initialization. This can
be used to skip model fitting when a fitted model is not needed.
To fit the model afterwards, use `_process_and_transform_data`
to get the transformed inputs and call `_fit_if_implemented` with
the transformed inputs.
fit_abandoned: Whether data for abandoned arms or trials should be
included in model training data. If ``False``, only
non-abandoned points are returned.
default_model_gen_options: Options passed down to `model.gen(...)`.
map_data_limit_rows_per_metric: Subsample the map data so that the
total number of rows per metric is limited by this value.
map_data_limit_rows_per_group: Subsample the map data so that the
number of rows in the `map_key` column for each (arm, metric)
is limited by this value.
"""
if not isinstance(data, MapData):
raise ValueError(
"`MapTorchModelBridge expects `MapData` instead of `Data`."
)
# pyre-fixme[4]: Attribute must be annotated.
self._map_key_features = data.map_keys
self._map_data_limit_rows_per_metric = map_data_limit_rows_per_metric
self._map_data_limit_rows_per_group = map_data_limit_rows_per_group
super().__init__(
experiment=experiment,
search_space=search_space,
data=data,
model=model,
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch_dtype,
torch_device=torch_device,
status_quo_name=status_quo_name,
status_quo_features=status_quo_features,
optimization_config=optimization_config,
fit_out_of_design=fit_out_of_design,
fit_on_init=fit_on_init,
default_model_gen_options=default_model_gen_options,
)
@property
def statuses_to_fit_map_metric(self) -> set[TrialStatus]:
return self.statuses_to_fit
@property
def parameters_with_map_keys(self) -> list[str]:
return self.parameters + self._map_key_features
def _predict(
self, observation_features: list[ObservationFeatures]
) -> list[ObservationData]:
"""This method is updated from `TorchModelBridge._predict(...) in that it
will accept observation features with or without map_keys. If observation
features do not contain map_keys, it will insert them based on
`target_map_values`.
"""
if not self.model:
raise ValueError(FIT_MODEL_ERROR.format(action="_model_predict"))
# The fitted model expects map_keys. If they do not exist, we use the
# target values.
target_map_values = self._default_model_gen_options.get(
"target_map_values", DEFAULT_TARGET_MAP_VALUES
)
for p in self._map_key_features:
for obsf in observation_features:
if p not in obsf.parameters:
obsf.parameters[p] = target_map_values[p] # pyre-ignore[16]
# Convert observation features to array
X = observation_features_to_array(
self.parameters_with_map_keys, observation_features
)
f, cov = not_none(self.model).predict(X=self._array_to_tensor(X))
f = f.detach().cpu().clone().numpy()
cov = cov.detach().cpu().clone().numpy()
# Convert resulting arrays to observations
return array_to_observation_data(f=f, cov=cov, outcomes=self.outcomes)
def _fit(
self,
model: TorchModel,
search_space: SearchSpace,
observations: list[Observation],
parameters: Optional[list[str]] = None,
**kwargs: Any,
) -> None:
"""The difference from `TorchModelBridge._fit(...)` is that we use
`self.parameters_with_map_keys` instead of `self.parameters`.
"""
self.parameters = list(search_space.parameters.keys())
if parameters is None:
parameters = self.parameters_with_map_keys
super()._fit(
model=model,
search_space=search_space,
observations=observations,
parameters=parameters,
**kwargs,
)
def _gen(
self,
n: int,
search_space: SearchSpace,
pending_observations: dict[str, list[ObservationFeatures]],
fixed_features: Optional[ObservationFeatures],
model_gen_options: Optional[TConfig] = None,
optimization_config: Optional[OptimizationConfig] = None,
) -> GenResults:
"""An updated version of `TorchModelBridge._gen(...) that first injects
`map_dim_to_target` (e.g., `{-1: 1.0}`) into `model_gen_options` so that
the target values of the map_keys are known during candidate generation.
"""
model_gen_options = self._add_map_dim_to_target(options=model_gen_options or {})
return super()._gen(
n=n,
search_space=search_space,
pending_observations=pending_observations,
fixed_features=fixed_features,
model_gen_options=model_gen_options,
optimization_config=optimization_config,
)
def _array_to_observation_features(
self, X: np.ndarray, candidate_metadata: Optional[list[TCandidateMetadata]]
) -> list[ObservationFeatures]:
"""The difference b/t this method and
TorchModelBridge._array_to_observation_features(...) is
that this one makes use of `self.parameters_with_map_keys`.
"""
return parse_observation_features(
X=X,
param_names=self.parameters_with_map_keys,
candidate_metadata=candidate_metadata,
)
def _prepare_observations(
self, experiment: Optional[Experiment], data: Optional[Data]
) -> list[Observation]:
"""The difference b/t this method and ModelBridge._prepare_observations(...)
is that this one uses `observations_from_map_data`.
"""
if experiment is None or data is None:
return []
return observations_from_map_data(
experiment=experiment,
map_data=data, # pyre-ignore[6]: Checked in __init__.
map_keys_as_parameters=True,
limit_rows_per_metric=self._map_data_limit_rows_per_metric,
limit_rows_per_group=self._map_data_limit_rows_per_group,
statuses_to_include=self.statuses_to_fit,
statuses_to_include_map_metric=self.statuses_to_fit_map_metric,
)
def _compute_in_design(
self, search_space: SearchSpace, observations: list[Observation]
) -> list[bool]:
"""The difference b/t this method and ModelBridge._compute_in_design(...)
is that this one correctly excludes map_keys when checking membership in
search space (as map_keys are not explicitly in the search space).
"""
return [
search_space.check_membership(
# Exclude map key features when checking
{
p: v
for p, v in obs.features.parameters.items()
if p not in self._map_key_features
}
)
for obs in observations
]
def _cross_validate(
self,
search_space: SearchSpace,
cv_training_data: list[Observation],
cv_test_points: list[ObservationFeatures],
parameters: Optional[list[str]] = None,
use_posterior_predictive: bool = False,
**kwargs: Any,
) -> list[ObservationData]:
"""Make predictions at cv_test_points using only the data in obs_feats
and obs_data. The difference from `TorchModelBridge._cross_validate`
is that here we do cross validation on the parameters + map_keys. There
is some extra logic to filter out out-of-design points in the map_key
dimension.
"""
if parameters is None:
parameters = self.parameters_with_map_keys
cv_test_data = super()._cross_validate(
search_space=search_space,
cv_training_data=cv_training_data,
cv_test_points=cv_test_points,
parameters=parameters, # we pass the map_keys too by default
use_posterior_predictive=use_posterior_predictive,
**kwargs,
)
observation_features, observation_data = separate_observations(cv_training_data)
# Since map_keys are used as features, there can be the possibility that
# models for different outcomes were fit on different ranges of map_key
# values; for example, this is the case if we (1) mix learning curve data with
# standard data (taking default map value), or (2) are in a situation where
# some learning curves start later than others. These prediction results are
# "out-of-design" in the map_key dimension, so we should filter them out.
map_key_ranges = self._get_map_key_ranges(
observation_features=observation_features,
observation_data=observation_data,
)
cv_test_data = self._filter_outcomes_out_of_map_range(
observation_features=cv_test_points,
observation_data=cv_test_data,
map_key_ranges=map_key_ranges,
)
return cv_test_data
def _filter_outcomes_out_of_map_range(
self,
observation_features: list[ObservationFeatures],
observation_data: list[ObservationData],
# pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
map_key_ranges: dict[str, dict[str, Optional[tuple]]],
) -> list[ObservationData]:
"""Uses `map_key_ranges` to detect which `observation_features` have
out-of-range map_keys and filters out the corresponding outcomes in
`observation_data`.
"""
filtered_observation_data = []
for obsf, obsd in zip(observation_features, observation_data):
metric_names = obsd.metric_names
means = obsd.means
covariance = obsd.covariance
for o in self.outcomes:
if o in metric_names:
for p in self._map_key_features:
map_key_value = obsf.parameters[p]
map_key_range = map_key_ranges[o][p]
if map_key_range is not None:
range_min, range_max = map_key_range
# pyre-fixme[58]: `<` is not supported for operand types
# `Union[None, bool, float, int, str]` and `Any`.
# pyre-fixme[58]: `>` is not supported for operand types
# `Union[None, bool, float, int, str]` and `Any`.
if map_key_value < range_min or map_key_value > range_max:
p_idx = metric_names.index(o)
metric_names.pop(p_idx)
means = np.delete(means, p_idx, axis=0)
covariance = np.delete(covariance, p_idx, axis=0)
covariance = np.delete(covariance, p_idx, axis=1)
break
new_obsd = ObservationData(
metric_names=metric_names, means=means, covariance=covariance
)
filtered_observation_data.append(new_obsd)
return filtered_observation_data
def _get_map_key_ranges(
self,
observation_features: list[ObservationFeatures],
observation_data: list[ObservationData],
# pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.
) -> dict[str, dict[str, Optional[tuple]]]:
"""Get ranges of map_key values in observation features. Returns a dict of the
form: {"outcome": {"map_key": (min_val, max_val)}}.
"""
map_values = {o: {p: [] for p in self._map_key_features} for o in self.outcomes}
for obsd, obsf in zip(observation_data, observation_features):
for p in self._map_key_features:
param_value = obsf.parameters[p]
for o in obsd.metric_names:
map_values[o][p].append(param_value)
# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[24]: Generic type `list` expects 1 type parameter, use
# `typing.List` to avoid runtime subscripting errors.
def get_range(values: list):
return (min(values), max(values)) if len(values) > 0 else None
return {
o: {p: get_range(map_values[o][p]) for p in self._map_key_features}
for o in self.outcomes
}
def _add_map_dim_to_target(self, options: TConfig) -> TConfig:
"""Convert `target_map_values` to `map_dim_to_target`, a form useable
by the acquisition function and insert into options dict.
"""
target_map_values = self._default_model_gen_options.get("target_map_values")
if target_map_values is None:
target_map_values = DEFAULT_TARGET_MAP_VALUES
param_and_map = self.parameters_with_map_keys
map_dim_to_target = {
param_and_map.index(p): target_map_values[p] # pyre-ignore[16]
for p in self._map_key_features
}
options[Keys.ACQF_KWARGS] = { # pyre-ignore[32]
**options.get(Keys.ACQF_KWARGS, {}),
"map_dim_to_target": map_dim_to_target,
}
return options
