# SPDX-License-Identifier: MPL-2.0
# Copyright (C) 2025- The University of Tokyo
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
import numpy as np
import copy
import pickle as pickle
import time
from ._history import History
from .. import utility
from .. import score as search_score
from ..optimize.random import Optimizer as RandomOptimizer
from ... import gp
from ...gp import Predictor as gp_predictor
from ...blm import Predictor as blm_predictor
from ...misc import SetConfig
from ..._variable import Variable, normalize_t
[docs]
class Policy:
"""Single objective Bayesian optimization with continuous search space"""
def __init__(
self, *, min_X=None, max_X=None, config=None, initial_data=None, comm=None
):
"""
Parameters
----------
min_X: numpy.ndarray
The minimum value of each dimension of the search space.
max_X: numpy.ndarray
The maximum value of each dimension of the search space.
config: SetConfig object (physbo.misc.SetConfig)
initial_data: tuple[np.ndarray, np.ndarray]
The initial training datasets.
The first elements is the array of inputs and the second is the array of values of objective functions
comm: MPI.Comm, optional
MPI Communicator
"""
self.predictor = None
self.training = Variable()
self.new_data = None
if min_X is None or max_X is None:
raise ValueError("min_X and max_X must be specified")
self.min_X = np.array(min_X)
self.max_X = np.array(max_X)
self.dim = self.min_X.shape[0]
assert self.dim == self.max_X.shape[0], (
"The dimension of min_X and max_X must be the same"
)
assert np.all(self.min_X < self.max_X), (
"min_X must be less than max_X for each dimension"
)
self.L_X = self.max_X - self.min_X
self.history = History(dim=self.dim)
if config is None:
self.config = SetConfig()
else:
self.config = config
self.ard = False
if initial_data is not None:
if len(initial_data) != 2:
msg = "ERROR: initial_data should be 2-elements tuple or list (X and objectives)"
raise RuntimeError(msg)
init_X, fs = initial_data
fs_normalized = normalize_t(fs, k=1)
assert init_X.shape[0] == fs_normalized.shape[0], (
"The number of initial data must be the same"
)
assert init_X.shape[1] == self.dim, (
"The dimension of initial_data[0] must be the same as the dimension of min_X and max_X"
)
self.write(init_X, fs_normalized)
if comm is None:
self.mpicomm = None
self.mpisize = 1
self.mpirank = 0
else:
self.mpicomm = comm
self.mpisize = comm.size
self.mpirank = comm.rank
self.config.learning.is_disp = (
self.config.learning.is_disp and self.mpirank == 0
)
[docs]
def set_seed(self, seed):
"""
Setting a seed parameter for np.random.
Parameters
----------
seed: int
seed number
-------
"""
self.seed = seed
np.random.seed(self.seed)
[docs]
def write(
self,
X,
t,
time_total=None,
time_update_predictor=None,
time_get_action=None,
time_run_simulator=None,
):
"""
Writing history (update history, not output to a file).
Parameters
----------
X: numpy.ndarray
N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of each search candidate.
t: numpy.ndarray
N dimensional array (1D) or N x 1 dimensional array (2D).
The negative energy of each search candidate (value of the objective function to be optimized).
Will be normalized to (N, 1) shape internally.
All the values must be finite. Failed evaluations are not
supported in continuous search spaces.
time_total: numpy.ndarray
N dimenstional array. The total elapsed time in each step.
If None (default), filled by 0.0.
time_update_predictor: numpy.ndarray
N dimenstional array. The elapsed time for updating predictor (e.g., learning hyperparemters) in each step.
If None (default), filled by 0.0.
time_get_action: numpy.ndarray
N dimenstional array. The elapsed time for getting next action in each step.
If None (default), filled by 0.0.
time_run_simulator: numpy.ndarray
N dimenstional array. The elapsed time for running the simulator in each step.
If None (default), filled by 0.0.
Returns
-------
Raises
------
ValueError
If t contains a non-finite value (NaN or +-Inf).
Nothing is written to the history in this case.
"""
# Normalize t to (N, 1) shape
t_normalized = normalize_t(t, k=1)
utility.require_finite(X, t_normalized)
self.history.write(
t.flatten(),
X,
time_total=time_total,
time_update_predictor=time_update_predictor,
time_get_action=time_get_action,
time_run_simulator=time_run_simulator,
)
# Get basis and convert to (1, N, n) format for single-objective
Z_basis = self.predictor.get_basis(X) if self.predictor is not None else None
if Z_basis is not None:
Z = Z_basis[np.newaxis, :, :] # (N, n) -> (1, N, n)
else:
Z = None
self.training.add(X=X, t=t_normalized, Z=Z)
if self.new_data is None:
self.new_data = Variable(X=X, t=t_normalized, Z=Z)
else:
self.new_data.add(X=X, t=t_normalized, Z=Z)
[docs]
def random_search(
self, max_num_probes, num_search_each_probe=1, simulator=None, is_disp=True
):
"""
Performing random search.
Parameters
----------
max_num_probes: int
Maximum number of random search process.
num_search_each_probe: int
Number of search at each random search process.
simulator: callable
Callable (function or object with ``__call__``) from action to t
Here, action is an integer which represents the index of the candidate.
is_disp: bool
If true, process messages are outputted.
Returns
-------
history: history object (physbo.search.discrete.results.history)
"""
if self.mpirank != 0:
is_disp = False
N = int(num_search_each_probe)
if is_disp:
utility.show_interactive_mode(simulator, self.history)
for n in range(0, max_num_probes):
time_total = time.time()
if is_disp and N > 1:
utility.show_start_message_multi_search(self.history.num_runs)
time_get_action = time.time()
action = self._get_random_action(N)
time_get_action = time.time() - time_get_action
N_indeed = len(action)
if N_indeed == 0:
if self.mpirank == 0:
print("WARNING: All actions have already searched.")
return copy.deepcopy(self.history)
if simulator is None:
return action
time_run_simulator = time.time()
t = _run_simulator(simulator, action, self.mpicomm)
time_run_simulator = time.time() - time_run_simulator
time_total = time.time() - time_total
self.write(
action,
t,
time_total=[time_total] * N_indeed,
time_update_predictor=np.zeros(N_indeed, dtype=float),
time_get_action=[time_get_action] * N_indeed,
time_run_simulator=[time_run_simulator] * N_indeed,
)
if is_disp:
self.history.show_search_results(N_indeed)
return copy.deepcopy(self.history)
[docs]
def bayes_search(
self,
training=None,
max_num_probes=None,
num_search_each_probe=1,
predictor=None,
is_disp=True,
simulator=None,
score="TS",
interval=0,
num_rand_basis=0,
optimizer=None,
ard=False,
):
"""
Performing Bayesian optimization.
Parameters
----------
training: physbo.Variable
Training dataset.
max_num_probes: int
Maximum number of searching process by Bayesian optimization.
num_search_each_probe: int
Number of searching by Bayesian optimization at each process.
predictor: predictor object
Base class is defined in physbo.predictor.
If None, blm_predictor is defined.
is_disp: bool
If true, process messages are outputted.
simulator: callable
Callable (function or object with ``__call__``)
Here, action is an integer which represents the index of the candidate.
score: str
The type of aquision funciton.
TS (Thompson Sampling), EI (Expected Improvement) and PI (Probability of Improvement) are available.
interval: int
The interval number of learning the hyper parameter.
If you set the negative value to interval, the hyper parameter learning is not performed.
If you set zero to interval, the hyper parameter learning is performed only at the first step.
num_rand_basis: int
The number of basis function. If you choose 0, ordinary Gaussian process run.
optimizer: Optimizer object
Optimizer object for optimizing the acquisition function.
If None, the default optimizer is used.
ard: bool
If True, use Automatic Relevance Determination (ARD) for the Gaussian kernel.
Default is False.
Returns
-------
history: history object (physbo.search.discrete.results.history)
"""
if self.mpirank != 0:
is_disp = False
old_disp = self.config.learning.is_disp
self.config.learning.is_disp = is_disp
if max_num_probes is None:
max_num_probes = 1
simulator = None
is_rand_expans = num_rand_basis != 0
self.ard = ard
if training is not None:
self.training = training
if predictor is not None:
self.predictor = predictor
elif self.predictor is None:
self._init_predictor(is_rand_expans)
if max_num_probes > 0 or interval >= 0:
utility.require_training_data(self.training)
if max_num_probes == 0 and interval >= 0:
self._learn_hyperparameter(num_rand_basis)
if optimizer is None:
optimizer = RandomOptimizer(
min_X=self.min_X, max_X=self.max_X, nsamples=1000
)
N = int(num_search_each_probe)
for n in range(max_num_probes):
time_total = time.time()
time_update_predictor = time.time()
if utility.is_learning(n, interval):
self._learn_hyperparameter(num_rand_basis)
else:
self._update_predictor()
time_update_predictor = time.time() - time_update_predictor
if num_search_each_probe != 1:
utility.show_start_message_multi_search(self.history.num_runs, score)
time_get_action = time.time()
K = self.config.search.multi_probe_num_sampling
alpha = self.config.search.alpha
action = self._get_actions(
score, N, K, alpha, optimizer, num_rand_basis=num_rand_basis
)
time_get_action = time.time() - time_get_action
if simulator is None:
self.config.learning.is_disp = old_disp
return action
time_run_simulator = time.time()
t = _run_simulator(simulator, action, self.mpicomm)
time_run_simulator = time.time() - time_run_simulator
time_total = time.time() - time_total
self.write(
action,
t,
time_total=[time_total] * N,
time_update_predictor=[time_update_predictor] * N,
time_get_action=[time_get_action] * N,
time_run_simulator=[time_run_simulator] * N,
)
if is_disp:
self.history.show_search_results(N)
self._update_predictor()
self.config.learning.is_disp = old_disp
return copy.deepcopy(self.history)
@staticmethod
def _warn_no_predictor(method_name):
print("Warning: Since policy.predictor is not yet set,")
print(" a GP predictor (num_rand_basis=0) is used for predicting")
print(" If you want to use a BLM predictor (num_rand_basis>0),")
print(" call bayes_search(max_num_probes=0, num_rand_basis=nrb)")
print(" before calling {}.".format(method_name))
[docs]
def get_post_fmean(self, xs):
"""
Calculate mean value of predictor (post distribution)
Parameters
----------
xs: physbo.Variable or np.ndarray
input parameters to calculate mean value
shape is (num_points, num_parameters)
Returns
-------
fmean: numpy.ndarray
Mean value of the post distribution.
Returned shape is (num_points).
"""
X = self._make_variable_X(xs)
if self.predictor is None:
self._warn_no_predictor("get_post_fmean()")
predictor = self._make_gp_predictor()
predictor.fit(self.training, 0, comm=self.mpicomm, objective_index=0)
predictor.prepare(self.training, objective_index=0)
return predictor.get_post_fmean(self.training, X, objective_index=0)
else:
self._update_predictor()
return self.predictor.get_post_fmean(self.training, X, objective_index=0)
[docs]
def get_post_fcov(self, xs, diag=True):
"""
Calculate covariance of predictor (post distribution)
Parameters
----------
xs: physbo.Variable or np.ndarray
input parameters to calculate covariance
shape is (num_points, num_parameters)
diag: bool
If true, only variances (diagonal elements) are returned.
Returns
-------
fcov: numpy.ndarray
Covariance matrix of the post distribution.
Returned shape is (num_points) if diag=true, (num_points, num_points) if diag=false.
"""
X = self._make_variable_X(xs)
if self.predictor is None:
self._warn_no_predictor("get_post_fcov()")
predictor = self._make_gp_predictor()
predictor.fit(self.training, 0, comm=self.mpicomm, objective_index=0)
predictor.prepare(self.training, objective_index=0)
return predictor.get_post_fcov(self.training, X, diag, objective_index=0)
else:
self._update_predictor()
return self.predictor.get_post_fcov(
self.training, X, diag, objective_index=0
)
[docs]
def get_kernel_length_scale(self):
"""
Return the Gaussian kernel length scale(s) (width) of the predictor.
With ARD, returns one length scale per input dimension; otherwise a single value.
Returns
-------
numpy.ndarray or None
Length scale(s). Shape (num_dim,) when ARD is used, (1,) otherwise.
None if the predictor is not set or not a GP with Gaussian kernel.
"""
if self.predictor is None:
return None
self._update_predictor()
try:
cov = self.predictor.model.prior.cov
except AttributeError:
return None
if not hasattr(cov, "width"):
return None
return np.atleast_1d(np.asarray(cov.width).flatten())
[docs]
def get_num_dim(self):
"""
Return the input dimension (number of features) of the search space.
Returns
-------
int
The number of dimensions (from min_X / max_X).
"""
return self.dim
[docs]
def get_score(
self, mode, *, xs=None, predictor=None, training=None, parallel=True, alpha=1
):
"""
Calcualte score (acquisition function)
Parameters
----------
mode: str
The type of aquisition funciton. TS, EI and PI are available.
These functions are defined in score.py.
xs: physbo.Variable or np.ndarray
input parameters to calculate score
predictor: predictor object
predictor used to calculate score.
If not given, self.predictor will be used.
training:physbo.Variable
Training dataset.
If not given, self.training will be used.
parallel: bool
Calculate scores in parallel by MPI (default: True)
alpha: float
Tuning parameter which is used if mode = TS.
In TS, multi variation is tuned as np.random.multivariate_normal(mean, cov*alpha**2, size).
Returns
-------
f: float or list of float
Score defined in each mode.
Raises
------
RuntimeError
If both *actions* and *xs* are given
Notes
-----
When neither *actions* nor *xs* are given, scores for actions not yet searched will be calculated.
When *parallel* is True, it is assumed that the function receives the same input (*actions* or *xs*) for all the ranks.
If you want to split the input array itself, set *parallel* be False and merge results by yourself.
"""
if training is None:
training = self.training
if training.X is None or training.X.shape[0] == 0:
msg = "ERROR: No training data is registered."
raise RuntimeError(msg)
if predictor is None:
if self.predictor is None:
self._warn_no_predictor("get_score()")
predictor = self._make_gp_predictor()
predictor.fit(training, 0, comm=self.mpicomm, objective_index=0)
predictor.prepare(training, objective_index=0)
else:
self._update_predictor()
predictor = self.predictor
if xs is not None:
test = self._make_variable_X(xs)
if parallel and self.mpisize > 1:
actions = np.array_split(np.arange(test.X.shape[0]), self.mpisize)
test = test.get_subset(actions[self.mpirank])
else:
raise RuntimeError("ERROR: xs is not given")
f = search_score.score(
mode, predictor=predictor, training=training, test=test, alpha=alpha
)
if parallel and self.mpisize > 1:
fs = self.mpicomm.allgather(f)
f = np.hstack(fs)
return f
def _argmax_score(self, mode, predictor, training, extra_trainings, optimizer):
K = len(extra_trainings)
if K == 0:
predictor.prepare(training, objective_index=0)
def fn(x):
return self.get_score(
mode, xs=x.reshape(1, -1), predictor=predictor, parallel=False
)[0]
else: # marginal score
trains = [copy.deepcopy(training) for _ in range(K)]
predictors = [copy.deepcopy(predictor) for _ in range(K)]
for k in range(K):
extra_train = extra_trainings[k]
# Ensure t is normalized
if extra_train.t is not None:
extra_train.t = normalize_t(extra_train.t, k=1)
trains[k].add(X=extra_train.X, t=extra_train.t)
predictors[k].update(trains[k], extra_train, objective_index=0)
def fn(x):
f = np.zeros(K)
for k in range(K):
f[k] = self.get_score(
mode,
xs=x.reshape(1, -1),
predictor=predictors[k],
training=trains[k],
parallel=False,
)[0]
return np.mean(f)
X = optimizer(fn, mpicomm=self.mpicomm)
return X
def _get_actions(self, mode, N, K, alpha, optimizer, num_rand_basis=0):
"""
Getting next candidates
Parameters
----------
mode: str
The type of aquisition funciton.
TS (Thompson Sampling), EI (Expected Improvement) and PI (Probability of Improvement) are available.
These functions are defined in score.py.
N: int
The total number of actions to return.
K: int
The total number of samples to evaluate marginal score
alpha: float
Tuning parameter which is used if mode = TS.
In TS, multi variation is tuned as np.random.multivariate_normal(mean, cov*alpha**2, size).
Returns
-------
chosen_actions: numpy.ndarray
An N-dimensional array of actions selected in each search process.
"""
X = np.zeros((N, self.dim))
self._update_predictor()
predictor = copy.deepcopy(self.predictor)
predictor.config.is_disp = False
X[0, :] = self._argmax_score(
mode, predictor, self.training, [], optimizer=optimizer
)
for n in range(1, N):
extra_training = Variable(X=X[0:n, :])
t = self.predictor.get_predict_samples(
self.training, extra_training, K, objective_index=0
)
extra_trainings = [copy.deepcopy(extra_training) for _ in range(K)]
for k in range(K):
# Normalize t to (N, 1) shape
t_normalized = normalize_t(t[k, :], k=1)
extra_trainings[k].t = t_normalized
X[n, :] = self._argmax_score(
mode, predictor, self.training, extra_trainings, optimizer=optimizer
)
return X
def _get_random_action(self, N):
"""
Getting indexes of actions randomly.
Parameters
----------
N: int
Total number of search candidates.
Returns
-------
action: numpy.ndarray
Indexes of actions selected randomly from search candidates.
"""
action = np.random.rand(N, self.dim) * self.L_X.reshape(
1, -1
) + self.min_X.reshape(1, -1)
if self.mpisize > 1:
self.mpicomm.Bcast(action, root=0)
return action
[docs]
def save(self, file_history, file_training=None, file_predictor=None):
"""
Saving history, training and predictor into the corresponding files.
Parameters
----------
file_history: str
The name of the file that stores the information of the history.
file_training: str
The name of the file that stores the training dataset.
file_predictor: str
The name of the file that stores the predictor dataset.
Returns
-------
"""
if self.mpirank == 0:
self.history.save(file_history)
if file_training is not None:
self.training.save(file_training)
if file_predictor is not None:
with open(file_predictor, "wb") as f:
pickle.dump(self.predictor, f)
[docs]
def load(self, file_history, file_training=None, file_predictor=None):
"""
Loading files about history, training and predictor.
Parameters
----------
file_history: str
The name of the file that stores the information of the history.
file_training: str
The name of the file that stores the training dataset.
file_predictor: str
The name of the file that stores the predictor dataset.
Returns
-------
"""
self.history.load(file_history)
if file_training is None:
# rebuild the training data from the valid observations only
X, t = self.history.export_valid()
# Normalize t to (N, 1) shape
t_normalized = normalize_t(t, k=1)
self.training = Variable(X=X, t=t_normalized)
else:
self.training = Variable()
self.training.load(file_training)
# Ensure t is normalized to (N, 1) shape after loading
if self.training.t is not None:
self.training.t = normalize_t(self.training.t, k=1)
if file_predictor is not None:
with open(file_predictor, "rb") as f:
self.predictor = pickle.load(f)
[docs]
def export_predictor(self):
"""
Returning the predictor dataset
Returns
-------
"""
return self.predictor
[docs]
def export_training(self):
"""
Returning the training dataset
Returns
-------
"""
return self.training
[docs]
def export_history(self):
"""
Returning the information of the history.
Returns
-------
"""
return self.history
def _init_predictor(self, is_rand_expans):
"""
Initialize predictor.
Parameters
----------
is_rand_expans: bool
If true, physbo.blm.predictor is selected.
If false, physbo.gp.Predictor is selected.
"""
if is_rand_expans:
self.predictor = self._make_blm_predictor()
else:
self.predictor = self._make_gp_predictor()
def _make_gp_predictor(self):
"""Create a GP predictor, with ARD if self.ard is True."""
ard = self.ard
num_dim = self.get_num_dim()
model = gp.core.Model.create_default(ard=ard, num_dim=num_dim)
return gp_predictor(self.config, model=model)
def _make_blm_predictor(self):
"""Create a BLM predictor, with ARD if self.ard is True."""
ard = self.ard
num_dim = self.get_num_dim()
model = gp.core.Model.create_default(ard=ard, num_dim=num_dim)
return blm_predictor(self.config, model=model)
def _learn_hyperparameter(self, num_rand_basis):
self.predictor.fit(
self.training, num_rand_basis, comm=self.mpicomm, objective_index=0
)
# Get basis and convert to (1, N, n) format for single-objective
training_Z_basis = self.predictor.get_basis(self.training.X)
if training_Z_basis is not None:
self.training.Z = training_Z_basis[np.newaxis, :, :] # (N, n) -> (1, N, n)
self.predictor.prepare(self.training, objective_index=0)
self.new_data = None
def _update_predictor(self):
if self.new_data is not None:
self.predictor.update(self.training, self.new_data, objective_index=0)
self.new_data = None
def _make_variable_X(self, test_X):
"""
Make a new *Variable* with X=test_X
Parameters
----------
test_X: numpy.ndarray or physbo.Variable
The set of candidates. Each row vector represents the feature vector of each search candidate.
Returns
-------
test_X: numpy.ndarray or physbo.Variable
The set of candidates. Each row vector represents the feature vector of each search candidate.
"""
if isinstance(test_X, np.ndarray):
test = Variable(X=test_X)
elif isinstance(test_X, Variable):
test = test_X
else:
raise TypeError("The type of test_X must be ndarray or physbo.Variable")
return test
def _run_simulator(simulator, action, comm=None):
"""
Run simulator and normalize return value to (N, 1) shape.
Parameters
----------
simulator: callable
Function that takes action and returns t value(s)
action: numpy.ndarray
Array of actions
comm: MPI.Comm, optional
MPI communicator
Returns
-------
numpy.ndarray
Normalized array with shape (N, 1)
"""
if comm is None:
t = simulator(action)
else:
if comm.rank == 0:
t = simulator(action)
else:
t = 0.0
t = comm.bcast(t, root=0)
return normalize_t(t, k=1)