honegumi.ax package

Subpackages

• honegumi.ax.utils package
  • Submodules
  • honegumi.ax.utils.constants module
  • Module contents

Submodules

honegumi.ax._ax module

This is a skeleton file that can serve as a starting point for a Python console script. To run this script uncomment the following lines in the [options.entry_points] section in setup.cfg:

console_scripts =
     fibonacci = ax.skeleton:run

Then run pip install . (or pip install -e . for editable mode) which will install the command fibonacci inside your current environment.

Besides console scripts, the header (i.e. until _logger…) of this file can also be used as template for Python modules.

Note

This file can be renamed depending on your needs or safely removed if not needed.

References

• https://setuptools.pypa.io/en/latest/userguide/entry_point.html

• https://pip.pypa.io/en/stable/reference/pip_install

honegumi.ax._ax.add_model_specific_keys(option_names, opt)

Add model-specific keys to the options dictionary (in-place).

This function adds model-specific keys to the options dictionary opt. For example, if use_custom_gen is a hidden variable, and the model is FULLYBAYESIAN, then use_custom_gen should be True.

It also sets the value of the key model_kwargs based on the value of MODEL_OPT_KEY in opt.

Parameters:

• option_names (list) – A list of option names.

• opt (dict) – The options dictionary.

Examples

The following example is demonstrative, the range of cases may be expanded later.

>>> option_names = [
...     "objective",
...     "model",
...     "custom_gen",
...     "existing_data",
...     "sum_constraint",
...     "order_constraint",
...     "linear_constraint",
...     "composition_constraint",
...     "categorical",
...     "custom_threshold",
...     "fidelity",
...     "synchrony",
... ]
>>> opt = {
...     "objective": "single",
...     "model": "Default",
...     "existing_data": False,
...     "sum_constraint": False,
...     "order_constraint": False,
...     "linear_constraint": False,
...     "composition_constraint": False,
...     "categorical": False,
...     "custom_threshold": False,
...     "fidelity": "single",
...     "synchrony": "single",
... }

>>> add_model_specific_keys(option_names, opt)
{
    "objective": "single",
    "model": "Default",
    "existing_data": False,
    "sum_constraint": False,
    "order_constraint": False,
    "linear_constraint": False,
    "composition_constraint": False,
    "categorical": False,
    "custom_threshold": False,
    "fidelity": "single",
    "synchrony": "single",
    "custom_gen": False,
    "model_kwargs": {},
}

honegumi.ax._ax.is_incompatible(opt)

Check if the given option dictionary contains incompatible options.

An option is considered incompatible if it cannot be used together with another option. For example, if the model is fully Bayesian, it cannot use the custom generator (use_custom_gen). Similarly, if the objective is single, it cannot use the custom threshold (use_custom_threshold).

Parameters:

opt (dict) – The option dictionary to check for incompatibility.

Returns:

True if any incompatibility is found among the options, False otherwise.

Return type:

bool

honegumi.ax._ax.log_fn(x)

honegumi.ax._ax.model_kwargs_test_override(render_datum)

make sure tests run faster for SAASBO

Module contents