#!/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 collections import OrderedDict
from collections.abc import Iterable
from functools import reduce
from logging import Logger
import pandas as pd
import torch
from ax.core.base_trial import TrialStatus
from ax.core.batch_trial import BatchTrial
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.generator_run import GeneratorRun
from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
from ax.core.observation import Observation
from ax.core.optimization_config import (
MultiObjectiveOptimizationConfig,
OptimizationConfig,
)
from ax.core.outcome_constraint import ObjectiveThreshold, OutcomeConstraint
from ax.core.trial import Trial
from ax.core.types import ComparisonOp, TModelPredictArm, TParameterization
from ax.exceptions.core import UnsupportedError, UserInputError
from ax.modelbridge.base import ModelBridge
from ax.modelbridge.cross_validation import (
assess_model_fit,
compute_diagnostics,
cross_validate,
)
from ax.modelbridge.generation_strategy import GenerationStrategy
from ax.modelbridge.modelbridge_utils import (
observed_pareto_frontier as observed_pareto,
predicted_pareto_frontier as predicted_pareto,
)
from ax.modelbridge.registry import (
get_model_from_generator_run,
ModelRegistryBase,
Models,
)
from ax.modelbridge.torch import TorchModelBridge
from ax.modelbridge.transforms.utils import (
derelativize_optimization_config_with_raw_status_quo,
)
from ax.plot.pareto_utils import get_tensor_converter_model
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from numpy import nan
from pyre_extensions import none_throws
from torch import Tensor
logger: Logger = get_logger(__name__)
[docs]
def get_best_raw_objective_point_with_trial_index(
experiment: Experiment,
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[int, TParameterization, dict[str, tuple[float, float]]]:
"""Given an experiment, identifies the arm that had the best raw objective,
based on the data fetched from the experiment.
Args:
experiment: Experiment, on which to identify best raw objective arm.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of parameterization and a mapping from metric name to a tuple of
the corresponding objective mean and SEM.
"""
optimization_config = optimization_config or experiment.optimization_config
if optimization_config is None:
raise UserInputError(
"Cannot identify the best point without an optimization config, but no "
"optimization config was provided on the experiment or as an argument."
)
if optimization_config.is_moo_problem:
logger.warning(
"get_best_raw_objective_point is deprecated for multi-objective "
"optimization. This method will return an arbitrary point on the "
"pareto frontier."
)
dat = experiment.lookup_data(trial_indices=trial_indices)
if dat.df.empty:
raise ValueError("Cannot identify best point if experiment contains no data.")
if any(oc.relative for oc in optimization_config.all_constraints):
if experiment.status_quo is not None:
optimization_config = _derel_opt_config_wrapper(
optimization_config=optimization_config,
experiment=experiment,
)
else:
logger.warning(
"No status quo provided; relative constraints will be ignored."
)
# Only COMPLETED trials should be considered when identifying the best point
completed_indices = {
t.index for t in experiment.trials_by_status[TrialStatus.COMPLETED]
}
completed_df = dat.df[dat.df["trial_index"].isin(completed_indices)]
feasible_df = _filter_feasible_rows(
df=completed_df,
optimization_config=optimization_config,
)
objective = optimization_config.objective
best_row_helper = (
_get_best_row_for_scalarized_objective
if isinstance(objective, ScalarizedObjective)
else _get_best_row_for_single_objective
)
# pyre-ignore Incompatible parameter type [6]
best_row = best_row_helper(df=feasible_df, objective=objective)
best_arm = experiment.arms_by_name[best_row["arm_name"]]
best_trial_index = int(best_row["trial_index"])
objective_rows = dat.df.loc[
(dat.df["arm_name"] == best_arm.name)
& (dat.df["trial_index"] == best_trial_index)
]
vals = {
row["metric_name"]: (row["mean"], row["sem"])
for _, row in objective_rows.iterrows()
}
return best_trial_index, none_throws(best_arm).parameters, vals
[docs]
def get_best_raw_objective_point(
experiment: Experiment,
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[TParameterization, dict[str, tuple[float, float]]]:
_, parameterization, vals = get_best_raw_objective_point_with_trial_index(
experiment=experiment,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
return parameterization, vals
def _gr_to_prediction_with_trial_index(
idx: int, gr: GeneratorRun
) -> tuple[int, TParameterization, TModelPredictArm | None] | None:
if gr.best_arm_predictions is None:
return None
best_arm, best_arm_predictions = gr.best_arm_predictions
if best_arm is None:
return None
return idx, best_arm.parameters, best_arm_predictions
def _raw_values_to_model_predict_arm(
values: dict[str, tuple[float, float]],
) -> TModelPredictArm:
return (
{k: v[0] for k, v in values.items()}, # v[0] is mean
{k: {k: v[1] * v[1]} for k, v in values.items()}, # v[1] is sem
)
[docs]
def get_best_parameters_from_model_predictions_with_trial_index(
experiment: Experiment,
models_enum: type[ModelRegistryBase],
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[int, TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, returns the best predicted parameterization and
corresponding prediction based on the most recent Trial with predictions. If no
trials have predictions returns None.
Only some models return predictions. For instance GPEI does while Sobol does not.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
models_enum: Registry of all models that may be in the experiment's
generation strategy.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of trial index, parameterization, and model predictions for it.
"""
optimization_config = optimization_config or experiment.optimization_config
if optimization_config is None:
raise ValueError(
"Cannot identify the best point without an optimization config, but no "
"optimization config was provided on the experiment or as an argument."
)
if optimization_config.is_moo_problem:
logger.warning(
"get_best_parameters_from_model_predictions is deprecated for "
"multi-objective optimization configs. This method will return an "
"arbitrary point on the pareto frontier."
)
for idx, trial in sorted(
experiment.trials.items(), key=lambda x: x[0], reverse=True
):
gr = None
if isinstance(trial, Trial):
gr = trial.generator_run
elif isinstance(trial, BatchTrial):
if len(trial.generator_run_structs) > 0:
# In theory batch_trial can have >1 gr, grab the first
gr = trial.generator_run_structs[0].generator_run
if gr is not None:
data = experiment.lookup_data(trial_indices=trial_indices)
try:
model = get_model_from_generator_run(
generator_run=gr,
experiment=experiment,
data=data,
models_enum=models_enum,
)
except ValueError:
return _gr_to_prediction_with_trial_index(idx, gr)
# If model is not TorchModelBridge, just use the best arm from the
# last good generator run
if not isinstance(model, TorchModelBridge):
return _gr_to_prediction_with_trial_index(idx, gr)
# Check to see if the model is worth using
cv_results = cross_validate(model=model)
diagnostics = compute_diagnostics(result=cv_results)
assess_model_fit_results = assess_model_fit(diagnostics=diagnostics)
objective_name = optimization_config.objective.metric.name
# If model fit is bad use raw results
if (
objective_name
in assess_model_fit_results.bad_fit_metrics_to_fisher_score
):
logger.warning(
"Model fit is poor; falling back on raw data for best point."
)
if not _is_all_noiseless(df=data.df, metric_name=objective_name):
logger.warning(
"Model fit is poor and data on objective metric "
+ f"{objective_name} is noisy; interpret best points "
+ "results carefully."
)
return get_best_by_raw_objective_with_trial_index(
experiment=experiment,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
res = model.model_best_point()
if res is None:
return _gr_to_prediction_with_trial_index(idx, gr)
best_arm, best_arm_predictions = res
return idx, none_throws(best_arm).parameters, best_arm_predictions
return None
[docs]
def get_best_parameters_from_model_predictions(
experiment: Experiment,
models_enum: type[ModelRegistryBase],
trial_indices: Iterable[int] | None = None,
) -> tuple[TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, returns the best predicted parameterization and
corresponding prediction based on the most recent Trial with predictions. If no
trials have predictions returns None.
Only some models return predictions. For instance GPEI does while Sobol does not.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
models_enum: Registry of all models that may be in the experiment's
generation strategy.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of parameterization and model predictions for it.
"""
res = get_best_parameters_from_model_predictions_with_trial_index(
experiment=experiment, models_enum=models_enum, trial_indices=trial_indices
)
if res is None:
return None
_, parameterization, vals = res
return parameterization, vals
[docs]
def get_best_by_raw_objective_with_trial_index(
experiment: Experiment,
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[int, TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, identifies the arm that had the best raw objective,
based on the data fetched from the experiment.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of trial index, parameterization, and model predictions for it.
"""
try:
(
trial_index,
parameterization,
values,
) = get_best_raw_objective_point_with_trial_index(
experiment=experiment,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
except ValueError as err:
logger.error(
f"Encountered error while trying to identify the best point: {err}"
)
return None
return (
trial_index,
parameterization,
_raw_values_to_model_predict_arm(values),
)
[docs]
def get_best_by_raw_objective(
experiment: Experiment,
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, identifies the arm that had the best raw objective,
based on the data fetched from the experiment.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of parameterization, and model predictions for it.
"""
res = get_best_by_raw_objective_with_trial_index(
experiment=experiment,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
if res is None:
return None
_, parameterization, vals = res
return parameterization, vals
[docs]
def get_best_parameters_with_trial_index(
experiment: Experiment,
models_enum: type[ModelRegistryBase],
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[int, TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, identifies the best arm.
First attempts according to do so with models used in optimization and
its corresponding predictions if available. Falls back to the best raw
objective based on the data fetched from the experiment.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
models_enum: Registry of all models that may be in the experiment's
generation strategy.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of trial index, parameterization, and model predictions for it.
"""
optimization_config = optimization_config or experiment.optimization_config
if optimization_config is None:
raise ValueError(
"Cannot identify the best point without an optimization config, but no "
"optimization config was provided on the experiment or as an argument."
)
if optimization_config.is_moo_problem:
logger.warning(
"get_best_parameters is deprecated for multi-objective optimization. "
"This method will return an arbitrary point on the pareto frontier."
)
# Find latest trial which has a generator_run attached and get its predictions
res = get_best_parameters_from_model_predictions_with_trial_index(
experiment=experiment,
models_enum=models_enum,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
if res is not None:
return res
return get_best_by_raw_objective_with_trial_index(
experiment=experiment, optimization_config=optimization_config
)
[docs]
def get_best_parameters(
experiment: Experiment,
models_enum: type[ModelRegistryBase],
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
) -> tuple[TParameterization, TModelPredictArm | None] | None:
"""Given an experiment, identifies the best arm.
First attempts according to do so with models used in optimization and
its corresponding predictions if available. Falls back to the best raw
objective based on the data fetched from the experiment.
TModelPredictArm is of the form:
({metric_name: mean}, {metric_name_1: {metric_name_2: cov_1_2}})
Args:
experiment: Experiment, on which to identify best raw objective arm.
models_enum: Registry of all models that may be in the experiment's
generation strategy.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
Returns:
Tuple of parameterization and model predictions for it.
"""
res = get_best_parameters_with_trial_index(
experiment=experiment,
models_enum=models_enum,
optimization_config=optimization_config,
trial_indices=trial_indices,
)
if res is None:
return None
_, parameterization, vals = res
return parameterization, vals
[docs]
def get_pareto_optimal_parameters(
experiment: Experiment,
generation_strategy: GenerationStrategy,
optimization_config: OptimizationConfig | None = None,
trial_indices: Iterable[int] | None = None,
use_model_predictions: bool = True,
) -> dict[int, tuple[TParameterization, TModelPredictArm]]:
"""Identifies the best parameterizations tried in the experiment so far,
using model predictions if ``use_model_predictions`` is true and using
observed values from the experiment otherwise. By default, uses model
predictions to account for observation noise.
NOTE: The format of this method's output is as follows:
{ trial_index --> (parameterization, (means, covariances) }, where means
are a dictionary of form { metric_name --> metric_mean } and covariances
are a nested dictionary of form
{ one_metric_name --> { another_metric_name: covariance } }.
Args:
experiment: Experiment, from which to find Pareto-optimal arms.
generation_strategy: Generation strategy containing the modelbridge.
optimization_config: Optimization config to use in place of the one stored
on the experiment.
trial_indices: Indices of trials for which to retrieve data. If None will
retrieve data from all available trials.
use_model_predictions: Whether to extract the Pareto frontier using
model predictions or directly observed values. If ``True``,
the metric means and covariances in this method's output will
also be based on model predictions and may differ from the
observed values.
Returns:
A mapping from trial index to the tuple of:
- the parameterization of the arm in that trial,
- two-item tuple of metric means dictionary and covariance matrix
(model-predicted if ``use_model_predictions=True`` and observed
otherwise).
"""
optimization_config = optimization_config or experiment.optimization_config
if optimization_config is None:
raise ValueError(
"Cannot identify the best point without an optimization config, but no "
"optimization config was provided on the experiment or as an argument."
)
# Validate aspects of the experiment: that it is a MOO experiment and
# that the current model can be used to produce the Pareto frontier.
if not optimization_config.is_moo_problem:
raise UnsupportedError(
"Please use `get_best_parameters` for single-objective problems."
)
moo_optimization_config = checked_cast(
MultiObjectiveOptimizationConfig, optimization_config
)
# Use existing modelbridge if it supports MOO otherwise create a new MOO modelbridge
# to use for Pareto frontier extraction.
modelbridge = generation_strategy.model
is_moo_modelbridge = (
modelbridge
and isinstance(modelbridge, TorchModelBridge)
and checked_cast(TorchModelBridge, modelbridge).is_moo_problem
)
if is_moo_modelbridge:
generation_strategy._fit_current_model(data=None)
else:
modelbridge = Models.BOTORCH_MODULAR(
experiment=experiment,
data=checked_cast(
Data, experiment.lookup_data(trial_indices=trial_indices)
),
)
modelbridge = checked_cast(TorchModelBridge, modelbridge)
# If objective thresholds are not specified in optimization config, extract
# the inferred ones if possible or infer them anew if not.
objective_thresholds_override = None
if not moo_optimization_config.objective_thresholds:
lgr = generation_strategy.last_generator_run
if lgr and lgr.gen_metadata and "objective_thresholds" in lgr.gen_metadata:
objective_thresholds_override = lgr.gen_metadata["objective_thresholds"]
objective_thresholds_override = modelbridge.infer_objective_thresholds(
search_space=experiment.search_space,
optimization_config=optimization_config,
fixed_features=None,
)
logger.info(
f"Using inferred objective thresholds: {objective_thresholds_override}, "
"as objective thresholds were not specified as part of the optimization "
"configuration on the experiment."
)
pareto_util = predicted_pareto if use_model_predictions else observed_pareto
pareto_optimal_observations = pareto_util(
modelbridge=modelbridge,
optimization_config=moo_optimization_config,
objective_thresholds=objective_thresholds_override,
)
# Insert observations into OrderedDict in order of descending individual
# hypervolume, formated as
# { trial_index --> (parameterization, (means, covariances) }
res: dict[int, tuple[TParameterization, TModelPredictArm]] = OrderedDict()
for obs in pareto_optimal_observations:
res[int(none_throws(obs.features.trial_index))] = (
obs.features.parameters,
(obs.data.means_dict, obs.data.covariance_matrix),
)
return res
def _get_best_row_for_scalarized_objective(
df: pd.DataFrame,
objective: ScalarizedObjective,
) -> pd.Series:
df = df.copy()
# First, add a weight column, setting 0.0 if the metric is not part
# of the objective
metric_to_weight = {
m.name: objective.weights[i] for i, m in enumerate(objective.metrics)
}
df["weight"] = df["metric_name"].apply(lambda x: metric_to_weight.get(x) or 0.0)
# Now, calculate the weighted linear combination via groupby,
# filtering out NaN for missing data
df["weighted_mean"] = df["mean"] * df["weight"]
groupby_df = (
df[["arm_name", "trial_index", "weighted_mean"]]
.groupby(["arm_name", "trial_index"], as_index=False)
.sum(min_count=1)
.dropna()
)
if groupby_df.empty:
raise ValueError("No data has been logged for scalarized objective.")
return (
groupby_df.loc[groupby_df["weighted_mean"].idxmin()]
if objective.minimize
else groupby_df.loc[groupby_df["weighted_mean"].idxmax()]
)
def _get_best_row_for_single_objective(
df: pd.DataFrame, objective: Objective
) -> pd.Series:
objective_name = objective.metric.name
objective_rows = df.loc[df["metric_name"] == objective_name]
if objective_rows.empty:
raise ValueError(f'No data has been logged for objective "{objective_name}".')
return (
objective_rows.loc[objective_rows["mean"].idxmin()]
if objective.minimize
else objective_rows.loc[objective_rows["mean"].idxmax()]
)
def _is_row_feasible(
df: pd.DataFrame,
optimization_config: OptimizationConfig,
) -> pd.Series:
"""Return a series of boolean values indicating whether arms satisfy outcome
constraints or not.
Looks at all arm data collected and returns False for rows corresponding to arms in
which one or more of their associated metrics' 95% confidence interval
falls outside of any outcome constraint's bounds (i.e. we are 95% sure the
bound is not satisfied), else True.
"""
if len(optimization_config.all_constraints) < 1:
return pd.Series([True] * len(df), index=df.index)
name = df["metric_name"]
# When SEM is NaN we should treat it as if it were 0
sems = none_throws(df["sem"].fillna(0))
# Bounds computed for 95% confidence interval on Normal distribution
lower_bound = df["mean"] - sems * 1.96
upper_bound = df["mean"] + sems * 1.96
# Nested function from OC -> Mask for consumption in later map/reduce from
# [OC] -> Mask. Constraint relativity is handled inside so long as relative bounds
# are set in surrounding closure (which will occur in proper experiment setup).
# pyre-fixme[53]: Captured variable `lower_bound` is not annotated.
# pyre-fixme[53]: Captured variable `name` is not annotated.
# pyre-fixme[53]: Captured variable `rel_lower_bound` is not annotated.
# pyre-fixme[53]: Captured variable `rel_upper_bound` is not annotated.
# pyre-fixme[53]: Captured variable `upper_bound` is not annotated.
def oc_mask(oc: OutcomeConstraint) -> pd.Series:
name_match_mask = name == oc.metric.name
if oc.relative:
logger.warning(
f"Ignoring relative constraint {oc}. Derelativize "
"OptimizationConfig before passing to `_is_row_feasible`."
)
return pd.Series(True, index=df.index)
# Return True if metrics are different, or whether the confidence
# interval is entirely not within the bound
if oc.op == ComparisonOp.GEQ:
return ~name_match_mask | (upper_bound >= float(oc.bound))
else:
return ~name_match_mask | (lower_bound <= float(oc.bound))
mask = reduce(
lambda left, right: left & right,
map(oc_mask, optimization_config.all_constraints),
)
# Mark all rows corresponding to infeasible arms as infeasible.
bad_arm_names = df[~mask]["arm_name"].tolist()
return checked_cast(
pd.Series, df["arm_name"].apply(lambda x: x not in bad_arm_names)
)
def _filter_feasible_rows(
df: pd.DataFrame,
optimization_config: OptimizationConfig,
) -> pd.DataFrame:
"""Filter out arms that do not satisfy outcome constraints
Looks at all arm data collected and removes rows corresponding to arms in
which one or more of their associated metrics' 95% confidence interval
falls outside of any outcome constraint's bounds (i.e. we are 95% sure the
bound is not satisfied).
"""
feasible = df.loc[_is_row_feasible(df=df, optimization_config=optimization_config)]
if feasible.empty:
raise ValueError(
"No points satisfied all outcome constraints within 95 percent"
"confidence interval."
)
return feasible
def _is_all_noiseless(df: pd.DataFrame, metric_name: str) -> bool:
"""Noiseless is defined as SEM = 0 or SEM = NaN on a given metric (usually
the objective).
"""
name_mask = df["metric_name"] == metric_name
df_metric_arms_sems = df[name_mask]["sem"]
return ((df_metric_arms_sems == 0) | df_metric_arms_sems == nan).all()
def _derel_opt_config_wrapper(
optimization_config: OptimizationConfig,
modelbridge: ModelBridge | None = None,
experiment: Experiment | None = None,
observations: list[Observation] | None = None,
) -> OptimizationConfig:
"""Derelativize optimization_config using raw status-quo values"""
# If optimization_config is already derelativized, return a copy.
if not any(oc.relative for oc in optimization_config.all_constraints):
return optimization_config.clone()
if modelbridge is None and experiment is None:
raise ValueError(
"Must specify ModelBridge or Experiment when calling "
"`_derel_opt_config_wrapper`."
)
elif not modelbridge:
modelbridge = get_tensor_converter_model(
experiment=none_throws(experiment),
data=none_throws(experiment).lookup_data(),
)
else: # Both modelbridge and experiment specified.
logger.warning(
"ModelBridge and Experiment provided to `_derel_opt_config_wrapper`. "
"Ignoring the latter."
)
if not modelbridge.status_quo:
raise ValueError(
"`modelbridge` must have status quo if specified. If `modelbridge` is "
"unspecified, `experiment` must have a status quo."
)
observations = observations or modelbridge.get_training_data()
return derelativize_optimization_config_with_raw_status_quo(
optimization_config=optimization_config,
modelbridge=modelbridge,
observations=observations,
)
def _objective_threshold_from_nadir(
experiment: Experiment,
objective: Objective,
optimization_config: MultiObjectiveOptimizationConfig | None = None,
) -> ObjectiveThreshold:
"""
Find the worst value observed for each objective and create an ObjectiveThreshold
with this as the bound.
"""
logger.info(f"Inferring ObjectiveThreshold for {objective} using nadir point.")
optimization_config = optimization_config or checked_cast(
MultiObjectiveOptimizationConfig, experiment.optimization_config
)
data_df = experiment.fetch_data().df
mean = data_df[data_df["metric_name"] == objective.metric.name]["mean"]
bound = max(mean) if objective.minimize else min(mean)
op = ComparisonOp.LEQ if objective.minimize else ComparisonOp.GEQ
return ObjectiveThreshold(
metric=objective.metric, bound=bound, op=op, relative=False
)
[docs]
def fill_missing_thresholds_from_nadir(
experiment: Experiment, optimization_config: OptimizationConfig
) -> list[ObjectiveThreshold]:
r"""Get the objective thresholds from the optimization config and
fill the missing thresholds based on the nadir point.
Args:
experiment: The experiment, whose data is used to calculate the nadir point.
optimization_config: Optimization config to get the objective thresholds
and the objective directions from.
Returns:
A list of objective thresholds, one for each objective in
optimization config.
"""
objectives = checked_cast(MultiObjective, optimization_config.objective).objectives
optimization_config = checked_cast(
MultiObjectiveOptimizationConfig, optimization_config
)
provided_thresholds = {
obj_t.metric.name: obj_t for obj_t in optimization_config.objective_thresholds
}
objective_thresholds = [
(
provided_thresholds[objective.metric.name]
if objective.metric.name in provided_thresholds
else _objective_threshold_from_nadir(
experiment=experiment,
objective=objective,
optimization_config=optimization_config,
)
)
for objective in objectives
]
return objective_thresholds
