#!/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.
# pyre-strict
from logging import Logger
from typing import Any
import numpy as np
from ax.core.base_trial import TrialStatus
from ax.core.experiment import Experiment
from ax.core.observation import Observation, ObservationData, ObservationFeatures
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import FixedParameter, RangeParameter
from ax.core.search_space import SearchSpace
from ax.exceptions.core import UserInputError
from ax.modelbridge.base import ModelBridge
from ax.modelbridge.best_model_selector import (
ReductionCriterion,
SingleDiagnosticBestModelSelector,
)
from ax.modelbridge.cross_validation import FISHER_EXACT_TEST_P
from ax.modelbridge.dispatch_utils import choose_generation_strategy
from ax.modelbridge.factory import get_sobol
from ax.modelbridge.generation_node import GenerationNode
from ax.modelbridge.generation_node_input_constructors import (
InputConstructorPurpose,
NodeInputConstructors,
)
from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.model_spec import ModelSpec
from ax.modelbridge.registry import Models
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.int_to_float import IntToFloat
from ax.modelbridge.transforms.transform_to_new_sq import TransformToNewSQ
from ax.modelbridge.transition_criterion import (
AutoTransitionAfterGen,
IsSingleObjective,
MaxGenerationParallelism,
MaxTrials,
MinimumPreferenceOccurances,
MinTrials,
)
from ax.models.torch.botorch_modular.surrogate import Surrogate
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from ax.utils.testing.core_stubs import (
get_experiment,
get_search_space,
get_search_space_for_value,
)
from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
from botorch.models.transforms.input import InputTransform, Normalize
from botorch.models.transforms.outcome import OutcomeTransform, Standardize
logger: Logger = get_logger(__name__)
# Observations
[docs]
def get_observation_features() -> ObservationFeatures:
return ObservationFeatures(parameters={"x": 2.0, "y": 10.0}, trial_index=0)
[docs]
def get_observation(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(parameters={"x": 2.0, "y": 10.0}, trial_index=0),
data=ObservationData(
means=np.array([2.0, 4.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="1_1",
)
[docs]
def get_observation1(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(parameters={"x": 2.0, "y": 10.0}, trial_index=0),
data=ObservationData(
means=np.array([2.0, 4.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="1_1",
)
[docs]
def get_observation_status_quo0(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(
parameters={"w": 0.85, "x": 1, "y": "baz", "z": False},
trial_index=0,
),
data=ObservationData(
means=np.array([2.0, 4.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="0_0",
)
[docs]
def get_observation_status_quo1(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(
parameters={"w": 0.85, "x": 1, "y": "baz", "z": False},
trial_index=1,
),
data=ObservationData(
means=np.array([2.0, 4.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="0_0",
)
[docs]
def get_observation1trans(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(parameters={"x": 9.0, "y": 121.0}, trial_index=0),
data=ObservationData(
means=np.array([9.0, 25.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="1_1",
)
[docs]
def get_observation2(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(parameters={"x": 3.0, "y": 2.0}, trial_index=1),
data=ObservationData(
means=np.array([2.0, 1.0]),
covariance=np.array([[2.0, 3.0], [4.0, 5.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="1_1",
)
[docs]
def get_observation2trans(
first_metric_name: str = "a",
second_metric_name: str = "b",
) -> Observation:
return Observation(
features=ObservationFeatures(parameters={"x": 16.0, "y": 9.0}, trial_index=1),
data=ObservationData(
means=np.array([9.0, 4.0]),
covariance=np.array([[2.0, 3.0], [4.0, 5.0]]),
metric_names=[first_metric_name, second_metric_name],
),
arm_name="1_1",
)
# Modeling layer
[docs]
def get_generation_strategy(
with_experiment: bool = False,
with_callable_model_kwarg: bool = True,
with_completion_criteria: int = 0,
with_generation_nodes: bool = False,
) -> GenerationStrategy:
if with_generation_nodes:
gs = sobol_gpei_generation_node_gs()
gs._nodes[0]._model_spec_to_gen_from = ModelSpec(
model_enum=Models.SOBOL,
model_kwargs={"init_position": 3},
model_gen_kwargs={"some_gen_kwarg": "some_value"},
)
if with_callable_model_kwarg:
# pyre-ignore[16]: testing hack to test serialization of callable kwargs
# in generation steps.
gs._nodes[0]._model_spec_to_gen_from.model_kwargs["model_constructor"] = (
get_sobol
)
else:
gs = choose_generation_strategy(
search_space=get_search_space(), should_deduplicate=True
)
if with_callable_model_kwarg:
# Testing hack to test serialization of callable kwargs
# in generation steps.
gs._steps[0].model_kwargs["model_constructor"] = get_sobol
if with_experiment:
gs._experiment = get_experiment()
if with_completion_criteria > 0:
gs._steps[0].num_trials = -1
gs._steps[0].completion_criteria = [
MinimumPreferenceOccurances(metric_name="m1", threshold=3)
] * with_completion_criteria
return gs
[docs]
def sobol_gpei_generation_node_gs(
with_model_selection: bool = False,
with_auto_transition: bool = False,
with_previous_node: bool = False,
with_input_constructors_all_n: bool = False,
with_input_constructors_remaining_n: bool = False,
with_input_constructors_repeat_n: bool = False,
with_input_constructors_target_trial: bool = False,
with_input_constructors_sq_features: bool = False,
with_unlimited_gen_mbm: bool = False,
with_trial_type: bool = False,
with_is_SOO_transition: bool = False,
) -> GenerationStrategy:
"""Returns a basic SOBOL+MBM GS using GenerationNodes for testing.
Args:
with_model_selection: If True, will add a second ModelSpec in the MBM node.
This can be used for testing model selection.
"""
if sum([with_auto_transition, with_unlimited_gen_mbm, with_is_SOO_transition]) > 1:
raise UserInputError(
"Only one of with_auto_transition, with_unlimited_gen_mbm, "
"with_is_SOO_transition can be set to True."
)
if (
sum(
[
with_input_constructors_all_n,
with_input_constructors_remaining_n,
with_input_constructors_repeat_n,
with_input_constructors_target_trial,
with_input_constructors_sq_features,
]
)
> 1
):
raise UserInputError(
"Only one of the input_constructors kwargs can be set to True."
)
sobol_criterion = [
MaxTrials(
threshold=5,
transition_to="MBM_node",
block_gen_if_met=True,
only_in_statuses=None,
not_in_statuses=[TrialStatus.FAILED, TrialStatus.ABANDONED],
)
]
# self-transitioning for mbm criterion isn't representative of real-world, but is
# useful for testing attributes likes repr etc
mbm_criterion = [
MaxTrials(
threshold=2,
transition_to="MBM_node",
block_gen_if_met=True,
only_in_statuses=None,
not_in_statuses=[TrialStatus.FAILED, TrialStatus.ABANDONED],
),
# Here MinTrials and MaxParallelism don't enforce anything, but
# we wanted to have an instance of them to test for storage compatibility.
MinTrials(
threshold=0,
transition_to="MBM_node",
block_gen_if_met=False,
only_in_statuses=[TrialStatus.CANDIDATE],
not_in_statuses=None,
),
MaxGenerationParallelism(
threshold=1000,
transition_to=None,
block_gen_if_met=True,
only_in_statuses=[TrialStatus.RUNNING],
not_in_statuses=None,
),
]
auto_mbm_criterion = [AutoTransitionAfterGen(transition_to="MBM_node")]
is_SOO_mbm_criterion = [IsSingleObjective(transition_to="MBM_node")]
step_model_kwargs = {"silently_filter_kwargs": True}
sobol_model_spec = ModelSpec(
model_enum=Models.SOBOL,
model_kwargs=step_model_kwargs,
model_gen_kwargs={},
)
mbm_model_specs = [
ModelSpec(
model_enum=Models.BOTORCH_MODULAR,
model_kwargs=step_model_kwargs,
model_gen_kwargs={},
)
]
sobol_node = GenerationNode(
node_name="sobol_node",
transition_criteria=sobol_criterion,
model_specs=[sobol_model_spec],
)
if with_model_selection:
# This is just MBM with different transforms.
mbm_model_specs.append(ModelSpec(model_enum=Models.BO_MIXED))
best_model_selector = SingleDiagnosticBestModelSelector(
diagnostic=FISHER_EXACT_TEST_P,
metric_aggregation=ReductionCriterion.MEAN,
criterion=ReductionCriterion.MIN,
)
else:
best_model_selector = None
if with_auto_transition:
mbm_node = GenerationNode(
node_name="MBM_node",
transition_criteria=auto_mbm_criterion,
model_specs=mbm_model_specs,
best_model_selector=best_model_selector,
)
elif with_unlimited_gen_mbm:
# no TC defined is equivalent to unlimited gen
mbm_node = GenerationNode(
node_name="MBM_node",
model_specs=mbm_model_specs,
best_model_selector=best_model_selector,
)
elif with_is_SOO_transition:
mbm_node = GenerationNode(
node_name="MBM_node",
transition_criteria=is_SOO_mbm_criterion,
model_specs=mbm_model_specs,
best_model_selector=best_model_selector,
)
else:
mbm_node = GenerationNode(
node_name="MBM_node",
transition_criteria=mbm_criterion,
model_specs=mbm_model_specs,
best_model_selector=best_model_selector,
)
# in an actual GS, this would be set during transition, manually setting here for
# testing purposes
if with_previous_node:
mbm_node._previous_node_name = sobol_node.node_name
if with_trial_type:
sobol_node._trial_type = Keys.LONG_RUN
mbm_node._trial_type = Keys.SHORT_RUN
# test input constructors, this also leaves the mbm node with no input
# constructors which validates encoding/decoding of instances with no
# input constructors
if with_input_constructors_all_n:
sobol_node._input_constructors = {
InputConstructorPurpose.N: NodeInputConstructors.ALL_N,
}
elif with_input_constructors_remaining_n:
sobol_node._input_constructors = {
InputConstructorPurpose.N: NodeInputConstructors.REMAINING_N,
}
elif with_input_constructors_repeat_n:
sobol_node._input_constructors = {
InputConstructorPurpose.N: NodeInputConstructors.REPEAT_N,
}
elif with_input_constructors_target_trial:
purpose = InputConstructorPurpose.FIXED_FEATURES
sobol_node._input_constructors = {
purpose: NodeInputConstructors.TARGET_TRIAL_FIXED_FEATURES,
}
elif with_input_constructors_sq_features:
purpose = InputConstructorPurpose.STATUS_QUO_FEATURES
sobol_node._input_constructors = {
purpose: NodeInputConstructors.STATUS_QUO_FEATURES,
}
sobol_mbm_GS_nodes = GenerationStrategy(
name="Sobol+MBM_Nodes",
nodes=[sobol_node, mbm_node],
steps=None,
)
return sobol_mbm_GS_nodes
[docs]
def get_outcome_transfrom_type() -> type[OutcomeTransform]:
return Standardize
[docs]
def get_experiment_for_value() -> Experiment:
return Experiment(get_search_space_for_value(), "test")
[docs]
def get_legacy_list_surrogate_generation_step_as_dict() -> dict[str, Any]:
"""
For use ensuring backwards compatibility loading the now deprecated ListSurrogate.
"""
# Generated via `get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnei`
# before new multi-Surrogate Model and new Surrogate diffs D42013742
return {
"__type": "GenerationStep",
"model": {"__type": "Models", "name": "BOTORCH_MODULAR"},
"num_trials": -1,
"min_trials_observed": 0,
"completion_criteria": [],
"max_parallelism": 1,
"use_update": False,
"enforce_num_trials": True,
"model_kwargs": {
"surrogate": {
"__type": "ListSurrogate",
"botorch_submodel_class_per_outcome": {},
"botorch_submodel_class": {
"__type": "Type[Model]",
"index": "SaasFullyBayesianSingleTaskGP",
"class": "<class 'botorch.models.model.Model'>",
},
"submodel_options_per_outcome": {},
"submodel_options": {},
"mll_class": {
"__type": "Type[MarginalLogLikelihood]",
"index": "ExactMarginalLogLikelihood",
"class": (
"<class 'gpytorch.mlls.marginal_log_likelihood."
"MarginalLogLikelihood'>"
),
},
"mll_options": {},
"submodel_outcome_transforms": [
{
"__type": "Standardize",
"index": {
"__type": "Type[OutcomeTransform]",
"index": "Standardize",
"class": (
"<class 'botorch.models.transforms.outcome."
"OutcomeTransform'>"
),
},
"class": (
"<class 'botorch.models.transforms.outcome.Standardize'>"
),
"state_dict": {"m": 1, "outputs": None, "min_stdv": 1e-8},
}
],
"submodel_input_transforms": [
{
"__type": "Normalize",
"index": {
"__type": "Type[InputTransform]",
"index": "Normalize",
"class": (
"<class 'botorch.models.transforms.input."
"InputTransform'>"
),
},
"class": "<class 'botorch.models.transforms.input.Normalize'>",
"state_dict": {
"d": 3,
"indices": None,
"transform_on_train": True,
"transform_on_eval": True,
"transform_on_fantasize": True,
"reverse": False,
"min_range": 1e-08,
"learn_bounds": False,
},
}
],
"submodel_covar_module_class": None,
"submodel_covar_module_options": {},
"submodel_likelihood_class": None,
"submodel_likelihood_options": {},
},
"botorch_acqf_class": {
"__type": "Type[AcquisitionFunction]",
"index": "qNoisyExpectedImprovement",
"class": "<class 'botorch.acquisition.acquisition.AcquisitionFunction'>", # noqa
},
},
"model_gen_kwargs": {},
"index": -1,
"should_deduplicate": False,
}
[docs]
def get_surrogate_generation_step() -> GenerationStep:
return GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
max_parallelism=1,
model_kwargs={
"surrogate": Surrogate(
botorch_model_class=SaasFullyBayesianSingleTaskGP,
input_transform_classes=[Normalize],
input_transform_options={
"Normalize": {
"d": 3,
"indices": None,
"transform_on_train": True,
"transform_on_eval": True,
"transform_on_fantasize": True,
"reverse": False,
"min_range": 1e-08,
"learn_bounds": False,
}
},
outcome_transform_classes=[Standardize],
outcome_transform_options={
"Standardize": {"m": 1, "outputs": None, "min_stdv": 1e-8}
},
),
"botorch_acqf_class": qNoisyExpectedImprovement,
},
)
[docs]
def get_surrogate_as_dict() -> dict[str, Any]:
"""
For use ensuring backwards compatibility when loading Surrogate
with input_transform and outcome_transform kwargs.
"""
return {
"__type": "Surrogate",
"botorch_model_class": None,
"model_options": {},
"mll_class": {
"__type": "Type[MarginalLogLikelihood]",
"index": "ExactMarginalLogLikelihood",
"class": (
"<class 'gpytorch.mlls.marginal_log_likelihood."
"MarginalLogLikelihood'>"
),
},
"mll_options": {},
"outcome_transform": None,
"input_transform": None,
"covar_module_class": None,
"covar_module_options": {},
"likelihood_class": None,
"likelihood_options": {},
"allow_batched_models": False,
}
[docs]
def get_surrogate_spec_as_dict(
model_class: str | None = None, with_legacy_input_transform: bool = False
) -> dict[str, Any]:
"""
For use ensuring backwards compatibility when loading SurrogateSpec
with input_transform and outcome_transform kwargs.
"""
if model_class is None:
model_class = "SingleTaskGP"
if with_legacy_input_transform:
input_transform = {
"__type": "Normalize",
"index": {
"__type": "Type[InputTransform]",
"index": "Normalize",
"class": "<class 'botorch.models.transforms.input.InputTransform'>",
},
"class": "<class 'botorch.models.transforms.input.Normalize'>",
"state_dict": {
"d": 7,
"indices": None,
"bounds": None,
"batch_shape": {"__type": "torch_Size", "value": "[]"},
"transform_on_train": True,
"transform_on_eval": True,
"transform_on_fantasize": True,
"reverse": False,
"min_range": 1e-08,
"learn_bounds": False,
},
}
else:
input_transform = None
return {
"__type": "SurrogateSpec",
"botorch_model_class": {
"__type": "Type[Model]",
"index": model_class,
"class": "<class 'botorch.models.model.Model'>",
},
"botorch_model_kwargs": {},
"mll_class": {
"__type": "Type[MarginalLogLikelihood]",
"index": "ExactMarginalLogLikelihood",
"class": (
"<class 'gpytorch.mlls.marginal_log_likelihood"
".MarginalLogLikelihood'>"
),
},
"mll_kwargs": {},
"covar_module_class": None,
"covar_module_kwargs": None,
"likelihood_class": None,
"likelihood_kwargs": None,
"input_transform": input_transform,
"outcome_transform": None,
"allow_batched_models": False,
"outcomes": [],
}
