This is a patch release with optimizations and bug fixes.
Client(): The Client constructor can be used without server host or server ID.
In this case, it is checked, whether a cloud server is running to connect to.
If not, a new server is started automatically.Client.create_study(): Two new arguments, extend_on_request and
extend_on_suggestion are introduced that extend the runtime of a cloud-based
server automatically on each new request and each new retrieved suggestion. This
allows to select a shutdown time in the near future and to extend the server
runtime only if required.~/.jcmoptimizer.conf.yml) allows
do configure a default server runtime when stating a server via the Python
or Matlab interface as well as defailt values for extend_on_request
and extend_on_suggestion.state: str | None)
the client was incompatible with Python v3.9. Now we use Union[] and Optional[] everywhere.Server() constructor.
The error was fixed.This is a major upgrade of JCMoptimizer that now supports a cloud-based operation. To support both a local and a cloud-based operation, the installation process has been extended. Please refer to the Python or Matlab installation guides.
The cloud-based operation adds a cloud interface. Here, you can
Server() constructor has been extended to support both a local and
cloud-based operation (see the
documentation
of the Server interface).Client() constructor has been extended to support both a local and
cloud-based operation (see the
documentation
of the Client interface).client.create_study(..., project_name='My project', project_id='project_01')client.create_study(..., clear_storage=True)
with the clear_storage argument.jcmoptimizer is published on pypi and can be installed by running,
e.g., pip install jcmoptimizer.client.create_study(...), the name
argument must be changed to study_name. This renaming was done to avoid confusion
with the new argument project_name.study.add_many() lead to uninformative errors if the design space data points had the wrong dimension. Now an error message informing about the wrong dimensionality is shown.This is a patch release with optimizations and bug fixes.
server = jcmoptimizer.Server()) failed, when a warning about a soon expiring license is displayed by the server. This case is now handled correctly. This is a patch release with optimizations and bug fixes.
uv to speed up the installation process.This is a patch release with optimizations and bug fixes.
Study.add_many(): The time required to register many data points at once is significantly reduced by caching.bokeh package updated. This adds a context menu for interactions with figures. avg property parameter of the surrogate, which is not normalized or warped.ActiveLearning Driver: In some cases the trained neural network was influenced by an update of the minimum and maximum of the training data, which is used to normalize the training data. This could lead to prediction errors. Now the minimum and maximum used for the normalization is frozen after training.This is a minor update release. It introduces features that enable the training of neural network surrogates to be more robust. Furthermore, the state of a surrogate (e.g. the trained weights of a network) is now saved between runs of a study.
{save_dir}/{study_id}.jcmb.train_mean_grad=False now enables each ensemble member to be trained to match the exact gradient. However, this will lead to much longer computation times for large ensembles.study.add_many() function, has now been increased to 1024 MB. Previously, the limit was 100 MB.ActiveLearning Driver: The state of the driver hyperparameters was not saved correctly after the user initiated hyperparameter optimisation using the study.driver.optimize_hyperparameters() function.study.get_observation_data() did not work correctly for vectorial observations.This is a patch release that fixes some issues of the installer. The fix introduced in v2.0.2 to use certificate authority files from the default system store lead to problems for some environments.
This is a patch release that fixes some issues of the ActiveLearning driver and the installer.
ActiveLearning driver: Fit variable introduces prior uncertainties of fit parameters for better numerical stability of the fit result.ActiveLearning driver: LinearCombination variable was not working for a combination of single-input posteriors.ActiveLearning driver: Variables that are not used in any objectives could have wrong number of Monte-Carlo samples.ActiveLearning driver: study.driver.get_minima() was not working for 1D minimization problems.This is a patch release that fixes some issues of the ScipyMinimizer and the BayesianReconstruction driver.
ScipyMinimizer driver with method Nelder-Mead: The continuation of a previous study determined a new simplex at the latest sample. Now the simplex is reconstructed from the previous study run leading to a better continuation.BayesianReconstruction driver: The sampling strategy was not taking prior information fully into account. This has been fixed.This is a patch release that fixes some issues with hyperparameter optimization, state retrieval and installation in environment with proxy servers.
study.get_state(path) with a partial path (e.g. path='suggestion') failed.This patch release improves the compatibility of the installer and the installation for different environments.
.condarc file.
Now it forces to use conda-forge channel to install python packages.requests
package for downloading micromamba.This is the first release of the standalone JCMoptimizer. It is a major upgrade of the optimizer previously shipped with JCMsuite based one a new implementation. Scripts using the old JCMsuite optimizer have to be adapted.
ActiveLearning. The driver defines a general active learning process
that can be configured for many different purposes such as optimization,
multi-objective optimization, integration or learning of the behavior of one or
more expensive black box functions.BayesianReconstruction. This is an extension of the
BayesianLeastSquares driver used to fit model parameters. The new driver allows
to maximize the full posterior probability including an error model and a prior
distribution for all parameters.Neural Network Ensemble that uses an ensemble of neural
networks to predict posterior distributions.To create a client instance, use the interface of the new optimizer. For example, in Python use
from jcmoptimizer import Server, Client, Study, Observation
server = Server()
client = Client(server.host)
The arguments for creating a new Study instance have changed. For example, the
domain argument is now called design_space in order to distinguish it from
parameters belonging to the environment:
# Definition of the search domain
design_space = [
{'name': 'x1', 'type': 'continuous', 'domain': (-1.5,1.5)},
{'name': 'x2', 'type': 'continuous', 'domain': (-1.5,1.5)},
]
# Definition of fixed environment parameter
environment = [
{'name': 'radius', 'type': 'fixed', 'domain': 1.5},
]
# Definition of a constraint on the search domain
constraints = [
{'name': 'circle', 'expression': 'sqrt(x1^2 + x2^2) <= radius'}
]
# Creation of the study object with study_id 'vanilla_bayesian_optimization'
study = client.create_study(
design_space=design_space,
environment=environment,
constraints=constraints,
driver="BayesianOptimization",
name="Standard Bayesian optimization",
study_id="vanilla_bayesian_optimization"
)
The objective function is replaced by an evaluator function in order to clarify that the output does not need to be the objective value itself but also physical outputs that can be mapped to the final objective.
# Evaluation of the black-box function for specified design parameters
def evaluate(study: Study, x1: float, x2: float, radius: float) -> Observation:
time.sleep(2) # make objective expensive
observation = study.new_observation()
observation.add(10*2
+ (x1**2-10*np.cos(2*np.pi*x1))
+ (x2**2-10*np.cos(2*np.pi*x2))
)
return observation
# Run the minimization
study.set_evaluator(evaluate)
study.run()
The method study.set_parameters() is replaced by the method study.configure().
Many methods of the Study instance are replaced by other methods with different arguments and output. For example
study.info(): The new method study.get_state() outputs the state of the Study as a nested dictionary.study.driver_info(): The new method study.driver.get_state() outputs the state of the study driver as a nested dictionary.study.get_data_table(): The new method study.get_observation_data() outputs all data of observations added to the study.study.get_minima(): The method is now part of the driver interface, study.driver.get_mimima().study.get_statistics(): The method is now part of the driver interface, study.driver.get_statistics().study.optimize_hyperparameters(): The method is now part of the driver interface, study.driver.optimize_hyperparameter().study.predict(): The method is now part of the driver interface, study.driver.predict().study.run_mcmc(): The method is now part of the driver interface, study.driver.run_mcmc().Please consult the documentation for the description of the arguments and outputs of the new methods.