init

X_reshape(X)

Reshape X to 2D array.

Parameters:

Name	Type	Description	Default
X	array	The input array.	required

Returns:

Name	Type	Description
X	array	The reshaped input array.

Examples:

>>> from spotPy.utils.init import X_reshape
>>> X = np.array([1,2,3])
>>> X_reshape(X)
array([[1, 2, 3]])

Source code in spotpython/utils/init.py

def X_reshape(X) -> np.array:
    """Reshape X to 2D array.

    Args:
        X (np.array):
            The input array.

    Returns:
        X (np.array):
            The reshaped input array.

    Examples:
        >>> from spotPy.utils.init import X_reshape
        >>> X = np.array([1,2,3])
        >>> X_reshape(X)
        array([[1, 2, 3]])
    """
    if not isinstance(X, np.ndarray):
        X = np.array(X)
    X = np.atleast_2d(X)
    return X

check_and_create_dir(path)

Check if the path exists and create it if it does not.

Parameters:

Name	Type	Description	Default
path	str	Path to the directory.	required

Returns:

Type	Description
noneType	None

Examples:

>>> fromspotPy.utils.init import check_and_create_dir
>>> check_and_create_dir("data/")

Source code in spotpython/utils/init.py

def check_and_create_dir(path) -> None:
    """Check if the path exists and create it if it does not.

    Args:
        path (str): Path to the directory.

    Returns:
        (noneType): None

    Examples:
        >>> fromspotPy.utils.init import check_and_create_dir
        >>> check_and_create_dir("data/")
    """
    if not isinstance(path, str):
        raise Exception("path must be a string")
    if not os.path.exists(path):
        os.makedirs(path)

create_spot_tensorboard_path(tensorboard_log, prefix)

Creates the spot_tensorboard_path and returns it.

Parameters:

Name	Type	Description	Default
tensorboard_log	bool	If True, the path to the folder where the tensorboard files are saved is created.	required
prefix	str	The prefix for the experiment name.	required

Returns:

Name	Type	Description
spot_tensorboard_path	str	The path to the folder where the tensorboard files are saved.

Source code in spotpython/utils/init.py

def create_spot_tensorboard_path(tensorboard_log, prefix) -> str:
    """Creates the spot_tensorboard_path and returns it.

    Args:
        tensorboard_log (bool):
            If True, the path to the folder where the tensorboard files are saved is created.
        prefix (str):
            The prefix for the experiment name.

    Returns:
        spot_tensorboard_path (str):
            The path to the folder where the tensorboard files are saved.
    """
    if tensorboard_log:
        experiment_name = get_experiment_name(prefix=prefix)
        spot_tensorboard_path = get_spot_tensorboard_path(experiment_name)
        os.makedirs(spot_tensorboard_path, exist_ok=True)
        print(f"Created spot_tensorboard_path: {spot_tensorboard_path} for SummaryWriter()")
    else:
        spot_tensorboard_path = None
    return spot_tensorboard_path

design_control_init(init_size=10, repeats=1)

Initialize design_control dictionary.

Parameters:

Name	Type	Description	Default
init_size	int	The initial size of the experimental design.	10
repeats	int	The number of repeats of the design.	1

Returns:

Name	Type	Description
design_control	dict	A dictionary containing the information about the design of experiments.

Source code in spotpython/utils/init.py

def design_control_init(init_size=10, repeats=1) -> dict:
    """Initialize design_control dictionary.

    Args:
        init_size (int): The initial size of the experimental design.
        repeats (int): The number of repeats of the design.

    Returns:
        design_control (dict):
            A dictionary containing the information about the design of experiments.

    """
    design_control = {"init_size": init_size, "repeats": repeats}
    return design_control

fun_control_init(_L_in=None, _L_out=None, _L_cond=None, _torchmetric=None, PREFIX='00', TENSORBOARD_CLEAN=False, accelerator='auto', converters=None, core_model=None, core_model_name=None, data=None, data_dir='./data', data_module=None, data_set=None, data_set_name=None, db_dict_name=None, design=None, device=None, devices='auto', enable_progress_bar=False, EXPERIMENT_NAME=None, eval=None, fun_evals=15, fun_repeats=1, horizon=None, hyperdict=None, infill_criterion='y', log_level=50, lower=None, max_time=1, max_surrogate_points=30, metric_sklearn=None, metric_sklearn_name=None, noise=False, n_points=1, n_samples=None, n_total=None, num_workers=0, num_nodes=1, ocba_delta=0, oml_grace_period=None, optimizer=None, precision='32', prep_model=None, prep_model_name=None, progress_file=None, save_experiment=False, scaler=None, scaler_name=None, scenario=None, seed=123, show_models=False, show_progress=True, shuffle=None, sigma=0.0, strategy='auto', surrogate=None, target_column=None, target_type=None, task=None, tensorboard_log=False, tensorboard_start=False, tensorboard_stop=False, test=None, test_seed=1234, test_size=0.4, tkagg=False, train=None, tolerance_x=0, upper=None, var_name=None, var_type=['num'], verbosity=0, weights=1.0, weight_coeff=0.0, weights_entry=None)

Initialize fun_control dictionary.

Parameters:

Name	Type	Description	Default
_L_in	int	The number of input features.	None
_L_out	int	The number of output features.	None
_L_cond	int	The number of conditional features.	None
_torchmetric	str	The metric to be used by the Lighting Trainer. For example “mean_squared_error”, see https://lightning.ai/docs/torchmetrics/stable/regression/mean_squared_error.html	None
accelerator	str	The accelerator to be used by the Lighting Trainer. It can be either “auto”, “dp”, “ddp”, “ddp2”, “ddp_spawn”, “ddp_cpu”, “gpu”, “tpu”. Default is “auto”.	'auto'
converters	dict	A dictionary containing the converters. Default is None.	None
core_model	object	The core model object. Default is None.	None
core_model_name	str	The name of the core model. Default is None.	None
data	object	The data object. Default is None.	None
data_dir	str	The directory to save the data. Default is “./data”.	'./data'
data_module	object	The data module object. Default is None.	None
data_set	object	The data set object. Default is None.	None
data_set_name	str	The name of the data set. Default is None.	None
db_dict_name	str	The name of the database dictionary. Default is None.	None
device	str	The device to use for the training. It can be either “cpu”, “mps”, or “cuda”.	None
devices	str or int	The number of devices to use for the training/validation/testing. Default is 1. Can be “auto” or an integer.	'auto'
design	object	The experimental design object. Default is None.	None
enable_progress_bar	bool	Whether to enable the progress bar or not.	False
eval	str	evaluation method used in sklearn taintest.py. Can be “eval_test”, “eval_oon_score”, “train_cv” or None. Default is None.	None
EXPERIMENT_NAME	str	The name of the experiment. Default is None. If None, the experiment name is generated based on the current date and time.	None
fun_evals	int	The number of function evaluations.	15
fun_repeats	int	The number of function repeats during the optimization. this value does not affect the number of the repeats in the initial design (this value can be set in the design_control). Default is 1.	1
horizon	int	The horizon of the time series data. Default is None.	None
hyperdict	dict	A dictionary containing the hyperparameters. Default is None. For example: spotriver.hyperdict.river_hyper_dict import RiverHyperDict	None
infill_criterion	str	Can be "y", "s", "ei" (negative expected improvement), or "all". Default is “y”.	'y'
log_level	int	log level with the following settings: NOTSET (0), DEBUG (10: Detailed information, typically of interest only when diagnosing problems.), INFO (20: Confirmation that things are working as expected.), WARNING (30: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.), ERROR (40: Due to a more serious problem, the software has not been able to perform some function.), and CRITICAL (50: A serious error, indicating that the program itself may be unable to continue running.)	50
lower	array	lower bound	None
max_time	int	The maximum time in minutes.	1
max_surrogate_points	int	The maximum number of points in the surrogate model. Default is inf.	30
metric_sklearn	object	The metric object from the scikit-learn library. Default is None.	None
metric_sklearn_name	str	The name of the metric object from the scikit-learn library. Default is None.	None
noise	bool	Whether the objective function is noiy or not. Default is False. Affects the repeat of the function evaluations.	False
n_points	int	The number of infill points to be generated by the surrogate in each iteration.	1
n_samples	int	The number of samples in the dataset. Default is None.	None
n_total	int	The total number of samples in the dataset. Default is None.	None
num_nodes	int	The number of GPU nodes to use for the training/validation/testing. Default is 1.	1
num_workers	int	The number of workers to use for the data loading. Default is 0.	0
ocba_delta	int	The number of additional, new points (only used if noise==True) generated by the OCBA infill criterion. Default is 0.	0
oml_grace_period	int	The grace period for the OML algorithm. Default is None.	None
optimizer	object	The optimizer object used for the search on surrogate. Default is None.	None
precision	str	The precision of the data. Default is “32”. Can be e.g., “16-mixed” or “16-true”.	'32'
PREFIX	str	The prefix of the experiment name. If the PREFIX is not None, a spotWriter that us an instance of a SummaryWriter(), is created. Default is “00”.	'00'
prep_model	object	The preprocessing model object. Used for river. Default is None.	None
prep_model_name	str	The name of the preprocessing model. Default is None.	None
progress_file	str	The name of the progress file. Default is None.	None
save_experiment	bool	Whether to save the experiment or not. Default is False.	False
scaler	object	The scaler object, e.g., the TorchStandard scaler from spot.utils.scaler.py. Default is None.	None
scaler_name	str	The name of the scaler object. Default is None.	None
scenario	str	The scenario to use. Default is None. Can be “river”, “sklearn”, or “lightning”.	None
seed	int	The seed to use for the random number generator. Default is 123.	123
sigma	float	The standard deviation of the noise of the objective function.	0.0
show_progress	bool	Whether to show the progress or not. Default is True.	True
show_models	bool	Plot model each generation. Currently only 1-dim functions are supported. Default is False.	False
shuffle	bool	Whether the data were shuffled or not. Default is None.	None
surrogate	object	The surrogate model object. Default is None.	None
strategy	str	The strategy to use. Default is “auto”.	'auto'
target_column	str	The name of the target column. Default is None.	None
target_type	str	The type of the target column. Default is None.	None
task	str	The task to perform. It can be either “classification” or “regression”. Default is None.	None
TENSORBOARD_CLEAN	bool	Whether to clean (delete) the tensorboard folder or not. Default is False.	False
tensorboard_log	bool	Whether to log the tensorboard or not. Starts the SummaryWriter. Default is False.	False
tensorboard_start	bool	Whether to start the tensorboard or not. Default is False.	False
tensorboard_stop	bool	Whether to stop the tensorboard or not. Default is False.	False
test	object	The test data set for spotriver. Default is None.	None
test_seed	int	The seed to use for the test set. Default is 1234.	1234
test_size	float	The size of the test set. Default is 0.4, i.e., 60% of the data is used for training and 40% for testing.	0.4
tkagg	bool	Whether to use matplotlib TkAgg or not. Default is False.	False
tolerance_x	float	tolerance for new x solutions. Minimum distance of new solutions, generated by suggest_new_X, to already existing solutions. If zero (which is the default), every new solution is accepted.	0
train	object	The training data set for spotriver. Default is None.	None
upper	array	upper bound	None
var_name	list	A list containing the name of the variables, e.g., [“x1”, “x2”]. Default is None.	None
var_type	List[str]	list of type information, can be either “int”, “num” or “factor”. Default is [“num”].	['num']
verbosity	int	The verbosity level. Determines print output to console. Higher values result in more output. Default is 0.	0
weights	float	The weight coefficient of the objective function. Positive values mean minimization. If set to -1, scores that are better when maximized will be minimized, e.g, accuracy. Can be an array, so that different weights can be used for different (multiple) objectives. Default is 1.0.	1.0
weight_coeff	float	Determines how to weight older measures. Default is 1.0. Used in the OML algorithm eval_oml.py. Default is 0.0.	0.0
weights_entry	str	The weights entry used in the GUI. Default is None.	None

Returns:

Name	Type	Description
fun_control	dict	A dictionary containing the information about the core model, loss function, metrics, and the hyperparameters.

Examples:

>>> from spotpython.utils.init import fun_control_init
    fun_control = fun_control_init(_L_in=64, _L_out=11, num_workers=0, device=None)
    fun_control
    {'CHECKPOINT_PATH': 'saved_models/',
        'DATASET_PATH': 'data/',
        'RESULTS_PATH': 'results/',
        'TENSORBOARD_PATH': 'runs/',
        '_L_in': 64,
        '_L_out': 11,
        '_L_cond': None,
        'accelerator': "auto",
        'core_model': None,
        'core_model_name': None,
        'data': None,
        'data_dir': './data',
        'db_dict_name': None,
        'device': None,
        'devices': "auto",
        'enable_progress_bar': False,
        'eval': None,
        'horizon': 7,
        'infill_criterion': 'y',
        'k_folds': None,
        'loss_function': None,
        'lower': None,
        'max_surrogate_points': 100,
        'metric_river': None,
        'metric_sklearn': None,
        'metric_sklearn_name': None,
        'metric_torch': None,
        'metric_params': {},
        'model_dict': {},
        'noise': False,
        'n_points': 1,
        'n_samples': None,
        'num_workers': 0,
        'ocba_delta': 0,
        'oml_grace_period': None,
        'optimizer': None,
        'path': None,
        'prep_model': None,
        'prep_model_name': None,
        'save_model': False,
        'scenario': "lightning",
        'seed': 1234,
        'show_batch_interval': 1000000,
        'shuffle': None,
        'sigma': 0.0,
        'target_column': None,
        'target_type': None,
        'train': None,
        'test': None,
        'task': 'classification',
        'tensorboard_path': None,
        'upper': None,
        'weights': 1.0,
        'writer': None}

Source code in spotpython/utils/init.py

def fun_control_init(
    _L_in=None,
    _L_out=None,
    _L_cond=None,
    _torchmetric=None,
    PREFIX="00",
    TENSORBOARD_CLEAN=False,
    accelerator="auto",
    converters=None,
    core_model=None,
    core_model_name=None,
    data=None,
    data_dir="./data",
    data_module=None,
    data_set=None,
    data_set_name=None,
    db_dict_name=None,
    design=None,
    device=None,
    devices="auto",
    enable_progress_bar=False,
    EXPERIMENT_NAME=None,
    eval=None,
    fun_evals=15,
    fun_repeats=1,
    horizon=None,
    hyperdict=None,
    infill_criterion="y",
    log_level=50,
    lower=None,
    max_time=1,
    max_surrogate_points=30,
    metric_sklearn=None,
    metric_sklearn_name=None,
    noise=False,
    n_points=1,
    n_samples=None,
    n_total=None,
    num_workers=0,
    num_nodes=1,
    ocba_delta=0,
    oml_grace_period=None,
    optimizer=None,
    precision="32",
    prep_model=None,
    prep_model_name=None,
    progress_file=None,
    save_experiment=False,
    scaler=None,
    scaler_name=None,
    scenario=None,
    seed=123,
    show_models=False,
    show_progress=True,
    shuffle=None,
    sigma=0.0,
    strategy="auto",
    surrogate=None,
    target_column=None,
    target_type=None,
    task=None,
    tensorboard_log=False,
    tensorboard_start=False,
    tensorboard_stop=False,
    test=None,
    test_seed=1234,
    test_size=0.4,
    tkagg=False,
    train=None,
    tolerance_x=0,
    upper=None,
    var_name=None,
    var_type=["num"],
    verbosity=0,
    weights=1.0,
    weight_coeff=0.0,
    weights_entry=None,
):
    """Initialize fun_control dictionary.

    Args:
        _L_in (int):
            The number of input features.
        _L_out (int):
            The number of output features.
        _L_cond (int):
            The number of conditional features.
        _torchmetric (str):
            The metric to be used by the Lighting Trainer.
            For example "mean_squared_error",
            see https://lightning.ai/docs/torchmetrics/stable/regression/mean_squared_error.html
        accelerator (str):
            The accelerator to be used by the Lighting Trainer.
            It can be either "auto", "dp", "ddp", "ddp2", "ddp_spawn", "ddp_cpu", "gpu", "tpu".
            Default is "auto".
        converters (dict):
            A dictionary containing the converters. Default is None.
        core_model (object):
            The core model object. Default is None.
        core_model_name (str):
            The name of the core model. Default is None.
        data (object):
            The data object. Default is None.
        data_dir (str):
            The directory to save the data. Default is "./data".
        data_module (object):
            The data module object. Default is None.
        data_set (object):
            The data set object. Default is None.
        data_set_name (str):
            The name of the data set. Default is None.
        db_dict_name (str):
            The name of the database dictionary. Default is None.
        device (str):
            The device to use for the training. It can be either "cpu", "mps", or "cuda".
        devices (str or int):
            The number of devices to use for the training/validation/testing.
            Default is 1. Can be "auto" or an integer.
        design (object):
            The experimental design object. Default is None.
        enable_progress_bar (bool):
            Whether to enable the progress bar or not.
        eval (str):
            evaluation method used in sklearn taintest.py.
            Can be "eval_test", "eval_oon_score", "train_cv" or None. Default is None.
        EXPERIMENT_NAME (str):
            The name of the experiment.
            Default is None. If None, the experiment name is generated based on the
            current date and time.
        fun_evals (int):
            The number of function evaluations.
        fun_repeats (int):
            The number of function repeats during the optimization. this value does not affect
            the number of the repeats in the initial design (this value can be set in the
            design_control). Default is 1.
        horizon (int):
            The horizon of the time series data. Default is None.
        hyperdict (dict):
            A dictionary containing the hyperparameters. Default is None.
            For example: `spotriver.hyperdict.river_hyper_dict import RiverHyperDict`
        infill_criterion (str):
            Can be `"y"`, `"s"`, `"ei"` (negative expected improvement), or `"all"`. Default is "y".
        log_level (int):
            log level with the following settings:
            `NOTSET` (`0`),
            `DEBUG` (`10`: Detailed information, typically of interest only when diagnosing problems.),
            `INFO` (`20`: Confirmation that things are working as expected.),
            `WARNING` (`30`: An indication that something unexpected happened, or indicative of some problem in the near
                future (e.g. ‘disk space low’). The software is still working as expected.),
            `ERROR` (`40`: Due to a more serious problem, the software has not been able to perform some function.), and
            `CRITICAL` (`50`: A serious error, indicating that the program itself may be unable to continue running.)
        lower (np.array):
            lower bound
        max_time (int):
            The maximum time in minutes.
        max_surrogate_points (int):
            The maximum number of points in the surrogate model. Default is inf.
        metric_sklearn (object):
            The metric object from the scikit-learn library. Default is None.
        metric_sklearn_name (str):
            The name of the metric object from the scikit-learn library. Default is None.
        noise (bool):
            Whether the objective function is noiy or not. Default is False.
            Affects the repeat of the function evaluations.
        n_points (int):
            The number of infill points to be generated by the surrogate in each iteration.
        n_samples (int):
            The number of samples in the dataset. Default is None.
        n_total (int):
            The total number of samples in the dataset. Default is None.
        num_nodes (int):
            The number of GPU nodes to use for the training/validation/testing. Default is 1.
        num_workers (int):
            The number of workers to use for the data loading. Default is 0.
        ocba_delta (int):
            The number of additional, new points (only used if noise==True) generated by
            the OCBA infill criterion. Default is 0.
        oml_grace_period (int):
            The grace period for the OML algorithm. Default is None.
        optimizer (object):
            The optimizer object used for the search on surrogate. Default is None.
        precision (str):
            The precision of the data. Default is "32". Can be e.g., "16-mixed" or "16-true".
        PREFIX (str):
            The prefix of the experiment name. If the PREFIX is not None, a spotWriter
            that us an instance of a SummaryWriter(), is created. Default is "00".
        prep_model (object):
            The preprocessing model object. Used for river. Default is None.
        prep_model_name (str):
            The name of the preprocessing model. Default is None.
        progress_file (str):
            The name of the progress file. Default is None.
        save_experiment (bool):
            Whether to save the experiment or not. Default is False.
        scaler (object):
            The scaler object, e.g., the TorchStandard scaler from spot.utils.scaler.py.
            Default is None.
        scaler_name (str):
            The name of the scaler object. Default is None.
        scenario (str):
            The scenario to use. Default is None. Can be "river", "sklearn", or "lightning".
        seed (int):
            The seed to use for the random number generator. Default is 123.
        sigma (float):
            The standard deviation of the noise of the objective function.
        show_progress (bool):
            Whether to show the progress or not. Default is `True`.
        show_models (bool):
            Plot model each generation.
            Currently only 1-dim functions are supported. Default is `False`.
        shuffle (bool):
            Whether the data were shuffled or not. Default is None.
        surrogate (object):
            The surrogate model object. Default is None.
        strategy (str):
            The strategy to use. Default is "auto".
        target_column (str):
            The name of the target column. Default is None.
        target_type (str):
            The type of the target column. Default is None.
        task (str):
            The task to perform. It can be either "classification" or "regression".
            Default is None.
        TENSORBOARD_CLEAN (bool):
            Whether to clean (delete) the tensorboard folder or not. Default is False.
        tensorboard_log (bool):
            Whether to log the tensorboard or not. Starts the SummaryWriter.
            Default is False.
        tensorboard_start (bool):
            Whether to start the tensorboard or not. Default is False.
        tensorboard_stop (bool):
            Whether to stop the tensorboard or not. Default is False.
        test (object):
            The test data set for spotriver. Default is None.
        test_seed (int):
            The seed to use for the test set. Default is 1234.
        test_size (float):
            The size of the test set. Default is 0.4, i.e.,
            60% of the data is used for training and 40% for testing.
        tkagg (bool):
            Whether to use matplotlib TkAgg or not. Default is False.
        tolerance_x (float):
            tolerance for new x solutions. Minimum distance of new solutions,
            generated by `suggest_new_X`, to already existing solutions.
            If zero (which is the default), every new solution is accepted.
        train (object):
            The training data set for spotriver. Default is None.
        upper (np.array):
            upper bound
        var_name (list):
            A list containing the name of the variables, e.g., ["x1", "x2"]. Default is None.
        var_type (List[str]):
            list of type information, can be either "int", "num" or "factor".
            Default is ["num"].
        verbosity (int):
            The verbosity level. Determines print output to console. Higher values
            result in more output. Default is 0.
        weights (float):
            The weight coefficient of the objective function. Positive values mean minimization.
            If set to -1, scores that are better when maximized will be minimized, e.g, accuracy.
            Can be an array, so that different weights can be used for different (multiple) objectives.
            Default is 1.0.
        weight_coeff (float):
            Determines how to weight older measures. Default is 1.0. Used in the OML algorithm eval_oml.py.
            Default is 0.0.
        weights_entry (str):
            The weights entry used in the GUI. Default is None.

    Returns:
        fun_control (dict):
            A dictionary containing the information about the core model,
            loss function, metrics, and the hyperparameters.

    Examples:
        >>> from spotpython.utils.init import fun_control_init
            fun_control = fun_control_init(_L_in=64, _L_out=11, num_workers=0, device=None)
            fun_control
            {'CHECKPOINT_PATH': 'saved_models/',
                'DATASET_PATH': 'data/',
                'RESULTS_PATH': 'results/',
                'TENSORBOARD_PATH': 'runs/',
                '_L_in': 64,
                '_L_out': 11,
                '_L_cond': None,
                'accelerator': "auto",
                'core_model': None,
                'core_model_name': None,
                'data': None,
                'data_dir': './data',
                'db_dict_name': None,
                'device': None,
                'devices': "auto",
                'enable_progress_bar': False,
                'eval': None,
                'horizon': 7,
                'infill_criterion': 'y',
                'k_folds': None,
                'loss_function': None,
                'lower': None,
                'max_surrogate_points': 100,
                'metric_river': None,
                'metric_sklearn': None,
                'metric_sklearn_name': None,
                'metric_torch': None,
                'metric_params': {},
                'model_dict': {},
                'noise': False,
                'n_points': 1,
                'n_samples': None,
                'num_workers': 0,
                'ocba_delta': 0,
                'oml_grace_period': None,
                'optimizer': None,
                'path': None,
                'prep_model': None,
                'prep_model_name': None,
                'save_model': False,
                'scenario': "lightning",
                'seed': 1234,
                'show_batch_interval': 1000000,
                'shuffle': None,
                'sigma': 0.0,
                'target_column': None,
                'target_type': None,
                'train': None,
                'test': None,
                'task': 'classification',
                'tensorboard_path': None,
                'upper': None,
                'weights': 1.0,
                'writer': None}
    """
    # Setting the seed
    L.seed_everything(seed)

    CHECKPOINT_PATH, DATASET_PATH, RESULTS_PATH, TENSORBOARD_PATH = setup_paths(TENSORBOARD_CLEAN)
    spot_tensorboard_path = create_spot_tensorboard_path(tensorboard_log, PREFIX)

    if metric_sklearn is None and metric_sklearn_name is not None:
        metric_sklearn = get_metric_sklearn(metric_sklearn_name)

    fun_control = {
        "PREFIX": PREFIX,
        "CHECKPOINT_PATH": CHECKPOINT_PATH,
        "DATASET_PATH": DATASET_PATH,
        "RESULTS_PATH": RESULTS_PATH,
        "TENSORBOARD_PATH": TENSORBOARD_PATH,
        "TENSORBOARD_CLEAN": TENSORBOARD_CLEAN,
        "_L_in": _L_in,
        "_L_out": _L_out,
        "_L_cond": _L_cond,
        "_torchmetric": _torchmetric,
        "accelerator": accelerator,
        "converters": converters,
        "core_model": core_model,
        "core_model_name": core_model_name,
        "counter": 0,
        "data": data,
        "data_dir": data_dir,
        "data_module": data_module,
        "data_set": data_set,
        "data_set_name": data_set_name,
        "db_dict_name": db_dict_name,
        "design": design,
        "device": device,
        "devices": devices,
        "enable_progress_bar": enable_progress_bar,
        "eval": eval,
        "fun_evals": fun_evals,
        "fun_repeats": fun_repeats,
        "horizon": horizon,
        "hyperdict": hyperdict,
        "infill_criterion": infill_criterion,
        "k_folds": 3,
        "log_graph": False,
        "log_level": log_level,
        "loss_function": None,
        "lower": lower,
        "max_time": max_time,
        "max_surrogate_points": max_surrogate_points,
        "metric_river": None,
        "metric_sklearn": metric_sklearn,
        "metric_sklearn_name": metric_sklearn_name,
        "metric_torch": None,
        "metric_params": {},
        "model_dict": {},
        "noise": noise,
        "n_points": n_points,
        "n_samples": n_samples,
        "n_total": n_total,
        "num_nodes": num_nodes,
        "num_workers": num_workers,
        "ocba_delta": ocba_delta,
        "oml_grace_period": oml_grace_period,
        "optimizer": optimizer,
        "path": None,
        "precision": precision,
        "prep_model": prep_model,
        "prep_model_name": prep_model_name,
        "progress_file": progress_file,
        "save_experiment": save_experiment,
        "save_model": False,
        "scaler": scaler,
        "scaler_name": scaler_name,
        "scenario": scenario,
        "seed": seed,
        "show_batch_interval": 1_000_000,
        "show_models": show_models,
        "show_progress": show_progress,
        "shuffle": shuffle,
        "sigma": sigma,
        "spot_tensorboard_path": spot_tensorboard_path,
        "strategy": strategy,
        "target_column": target_column,
        "target_type": target_type,
        "task": task,
        "tensorboard_log": tensorboard_log,
        "tensorboard_start": tensorboard_start,
        "tensorboard_stop": tensorboard_stop,
        "test": test,
        "test_seed": test_seed,
        "test_size": test_size,
        "tkagg": tkagg,
        "tolerance_x": tolerance_x,
        "train": train,
        "upper": upper,
        "var_name": var_name,
        "var_type": var_type,
        "verbosity": verbosity,
        "weights": weights,
        "weight_coeff": weight_coeff,
        "weights_entry": weights_entry,
    }
    if hyperdict is not None and core_model_name is not None:
        # check if hyperdict implements the methods get_scenario:
        if hasattr(hyperdict, "get_scenario"):
            scenario = hyperdict().get_scenario()
        else:
            scenario = None
        if fun_control["hyperdict"].__name__ == RiverHyperDict.__name__ or scenario == "river":
            coremodel, core_model_instance = get_river_core_model_from_name(core_model_name)
            if prep_model is None and prep_model_name is not None:
                prep_model = get_river_prep_model(prep_model_name)
        else:
            coremodel, core_model_instance = get_core_model_from_name(core_model_name)
            if prep_model is None and prep_model_name is not None:
                prep_model = get_prep_model(prep_model_name)
        fun_control.update({"prep_model": prep_model})
        add_core_model_to_fun_control(
            core_model=core_model_instance,
            fun_control=fun_control,
            hyper_dict=hyperdict,
            filename=None,
        )
    if hyperdict is not None and core_model is not None:
        add_core_model_to_fun_control(
            core_model=core_model,
            fun_control=fun_control,
            hyper_dict=hyperdict,
            filename=None,
        )
    return fun_control

get_experiment_name(prefix='00')

Returns a unique experiment name with a given prefix.

Parameters:

Name	Type	Description	Default
prefix	str	Prefix for the experiment name. Defaults to “00”.	'00'

Returns:

Name	Type	Description
str	str	Unique experiment name.

Examples:

>>> from spotpython.utils.init import get_experiment_name
>>> get_experiment_name(prefix="00")
00_ubuntu_2021-08-31_14-30-00

Source code in spotpython/utils/init.py

def get_experiment_name(prefix: str = "00") -> str:
    """Returns a unique experiment name with a given prefix.

    Args:
        prefix (str, optional): Prefix for the experiment name. Defaults to "00".

    Returns:
        str: Unique experiment name.

    Examples:
        >>> from spotpython.utils.init import get_experiment_name
        >>> get_experiment_name(prefix="00")
        00_ubuntu_2021-08-31_14-30-00
    """
    start_time = datetime.datetime.now(tzlocal())
    HOSTNAME = socket.gethostname().split(".")[0]
    experiment_name = prefix + "_" + HOSTNAME + "_" + str(start_time).split(".", 1)[0].replace(" ", "_")
    experiment_name = experiment_name.replace(":", "-")
    return experiment_name

get_feature_names(fun_control)

Get the feature names from the fun_control dictionary.

Parameters:

Name	Type	Description	Default
fun_control	dict	The function control dictionary. Must contain a “data_set” key.	required

Returns:

Type	Description
List[str]	List[str]: List of feature names.

Raises:

Type	Description
ValueError	If “data_set” is not in fun_control.
ValueError	If “data_set” is None.

Examples:

>>> from spotpython.utils.init import get_feature_names
    get_feature_names(fun_control)

Source code in spotpython/utils/init.py

def get_feature_names(fun_control: Dict[str, Any]) -> List[str]:
    """
    Get the feature names from the fun_control dictionary.

    Args:
        fun_control (dict): The function control dictionary. Must contain a "data_set" key.

    Returns:
        List[str]: List of feature names.

    Raises:
        ValueError: If "data_set" is not in fun_control.
        ValueError: If "data_set" is None.

    Examples:
        >>> from spotpython.utils.init import get_feature_names
            get_feature_names(fun_control)
    """
    data_set = fun_control.get("data_set")

    if data_set is None:
        raise ValueError("'data_set' key not found or is None in 'fun_control'")

    return data_set.names

get_spot_tensorboard_path(experiment_name)

Get the path to the spot tensorboard files.

Parameters:

Name	Type	Description	Default
experiment_name	str	The name of the experiment.	required

Returns:

Name	Type	Description
spot_tensorboard_path	str	The path to the folder where the spot tensorboard files are saved.

Examples:

>>> from spotpython.utils.init import get_spot_tensorboard_path
>>> get_spot_tensorboard_path("00_ubuntu_2021-08-31_14-30-00")
runs/spot_logs/00_ubuntu_2021-08-31_14-30-00

Source code in spotpython/utils/init.py

def get_spot_tensorboard_path(experiment_name) -> str:
    """Get the path to the spot tensorboard files.

    Args:
        experiment_name (str): The name of the experiment.

    Returns:
        spot_tensorboard_path (str): The path to the folder where the spot tensorboard files are saved.

    Examples:
        >>> from spotpython.utils.init import get_spot_tensorboard_path
        >>> get_spot_tensorboard_path("00_ubuntu_2021-08-31_14-30-00")
        runs/spot_logs/00_ubuntu_2021-08-31_14-30-00

    """
    spot_tensorboard_path = os.environ.get("PATH_TENSORBOARD", "runs/spot_logs/")
    spot_tensorboard_path = os.path.join(spot_tensorboard_path, experiment_name)
    return spot_tensorboard_path

get_tensorboard_path(fun_control)

Get the path to the tensorboard files.

Parameters:

Name	Type	Description	Default
fun_control	dict	The function control dictionary.	required

Returns:

Name	Type	Description
tensorboard_path	str	The path to the folder where the tensorboard files are saved.

Examples:

>>> from spotpython.utils.init import get_tensorboard_path
>>> get_tensorboard_path(fun_control)
runs/

Source code in spotpython/utils/init.py

def get_tensorboard_path(fun_control) -> str:
    """Get the path to the tensorboard files.

    Args:
        fun_control (dict): The function control dictionary.

    Returns:
        tensorboard_path (str): The path to the folder where the tensorboard files are saved.

    Examples:
        >>> from spotpython.utils.init import get_tensorboard_path
        >>> get_tensorboard_path(fun_control)
        runs/
    """
    return fun_control["TENSORBOARD_PATH"]

optimizer_control_init(max_iter=1000, seed=125)

Initialize optimizer_control dictionary.

Parameters:

Name	Type	Description	Default
max_iter	int	The maximum number of iterations. This will be used for the optimization of the surrogate model. Default is 1000.	1000
seed	int	The seed to use for the random number generator. Default is 125.	125

Notes

• Differential evaluation uses maxiter = 1000 and sets the number of function evaluations to (maxiter + 1) * popsize * N, which results in 1000 * 15 * k, because the default popsize is 15 and N is the number of parameters. This is already sufficient for many situations. For example, for k=2 these are 30 000 iterations. Therefore we set this value to 1000.
• This value will be passed to the surrogate model in the Spot class.

Returns:

Name	Type	Description
optimizer_control	dict	A dictionary containing the information about the optimizer.

Source code in spotpython/utils/init.py

def optimizer_control_init(
    max_iter=1000,
    seed=125,
) -> dict:
    """Initialize optimizer_control dictionary.

    Args:
        max_iter (int):
            The maximum number of iterations. This will be used for the
            optimization of the surrogate model. Default is 1000.
        seed (int):
            The seed to use for the random number generator.
            Default is 125.

    Notes:
        * Differential evaluation uses `maxiter = 1000` and sets the number of function evaluations to
          (maxiter + 1) * popsize * N, which results in 1000 * 15 * k,
          because the default popsize is 15 and N is the number of parameters. This is already sufficient
          for many situations. For example, for k=2 these are 30 000 iterations.
          Therefore we set this value to 1000.
        * This value will be passed to the surrogate model in the `Spot` class.

    Returns:
        optimizer_control (dict):
            A dictionary containing the information about the optimizer.

    """
    optimizer_control = {"max_iter": max_iter, "seed": seed}
    return optimizer_control

setup_paths(tensorboard_clean)

Setup paths for checkpoints, datasets, results, and tensorboard files. This function also handles cleaning the tensorboard path if specified.

Parameters:

Name	Type	Description	Default
tensorboard_clean	bool	If True, move the existing tensorboard folder to a timestamped backup folder to avoid overwriting old tensorboard files.	required

Returns:

Name	Type	Description
CHECKPOINT_PATH	str	The path to the folder where the pretrained models are saved.
DATASET_PATH	str	The path to the folder where the datasets are/should be downloaded.
RESULTS_PATH	str	The path to the folder where the results (plots, csv, etc.) are saved.
TENSORBOARD_PATH	str	The path to the folder where the tensorboard files are saved.

Examples:

>>> from spotpython.utils.init import setup_paths
>>> setup_paths(tensorboard_clean=True)
('runs/saved_models/', 'data/', 'results/', 'runs/')

Source code in spotpython/utils/init.py

def setup_paths(tensorboard_clean) -> tuple:
    """
    Setup paths for checkpoints, datasets, results, and tensorboard files.
    This function also handles cleaning the tensorboard path if specified.

    Args:
        tensorboard_clean (bool):
            If True, move the existing tensorboard folder to a timestamped backup
            folder to avoid overwriting old tensorboard files.

    Returns:
        CHECKPOINT_PATH (str):
            The path to the folder where the pretrained models are saved.
        DATASET_PATH (str):
            The path to the folder where the datasets are/should be downloaded.
        RESULTS_PATH (str):
            The path to the folder where the results (plots, csv, etc.) are saved.
        TENSORBOARD_PATH (str):
            The path to the folder where the tensorboard files are saved.

    Examples:
        >>> from spotpython.utils.init import setup_paths
        >>> setup_paths(tensorboard_clean=True)
        ('runs/saved_models/', 'data/', 'results/', 'runs/')

    """
    # Path to the folder where the pretrained models are saved
    CHECKPOINT_PATH = os.environ.get("PATH_CHECKPOINT", "runs/saved_models/")
    os.makedirs(CHECKPOINT_PATH, exist_ok=True)

    # Path to the folder where the datasets are/should be downloaded (e.g. MNIST)
    DATASET_PATH = os.environ.get("PATH_DATASETS", "data/")
    os.makedirs(DATASET_PATH, exist_ok=True)

    # Path to the folder where the results (plots, csv, etc.) are saved
    RESULTS_PATH = os.environ.get("PATH_RESULTS", "results/")
    os.makedirs(RESULTS_PATH, exist_ok=True)

    # Path to the folder where the tensorboard files are saved
    TENSORBOARD_PATH = os.environ.get("PATH_TENSORBOARD", "runs/")
    if tensorboard_clean:
        # if the folder "runs" exists, move it to "runs_Y_M_D_H_M_S" to avoid overwriting old tensorboard files
        if os.path.exists(TENSORBOARD_PATH):
            now = datetime.datetime.now()
            os.makedirs("runs_OLD", exist_ok=True)
            # use [:-1] to remove "/" from the end of the path
            TENSORBOARD_PATH_OLD = "runs_OLD/" + TENSORBOARD_PATH[:-1] + "_" + now.strftime("%Y_%m_%d_%H_%M_%S")
            print(f"Moving TENSORBOARD_PATH: {TENSORBOARD_PATH} to TENSORBOARD_PATH_OLD: {TENSORBOARD_PATH_OLD}")
            os.rename(TENSORBOARD_PATH[:-1], TENSORBOARD_PATH_OLD)

    os.makedirs(TENSORBOARD_PATH, exist_ok=True)

    # Ensure the figures folder exists
    if not os.path.exists("./figures"):
        os.makedirs("./figures")
    return CHECKPOINT_PATH, DATASET_PATH, RESULTS_PATH, TENSORBOARD_PATH

surrogate_control_init(log_level=50, noise=False, model_optimizer=differential_evolution, model_fun_evals=10000, min_theta=-3.0, max_theta=2.0, n_theta='anisotropic', p_val=2.0, n_p=1, optim_p=False, min_Lambda=1e-09, max_Lambda=1, seed=124, theta_init_zero=True, var_type=None, metric_factorial='canberra')

Initialize surrogate_control dictionary.

Parameters:

Name	Type	Description	Default
model_optimizer	object	The optimizer object used for the search on surrogate. Default is differential_evolution.	differential_evolution
model_fun_evals	int	The number of function evaluations. This will be used for the optimization of the surrogate model. Default is 1000.	10000
min_theta	float	The minimum value of theta. Note that the base10-logarithm is used. Default is -3.	-3.0
max_theta	float	The maximum value of theta. Note that the base10-logarithm is used. Default is 3.	2.0
noise	bool	Whether the objective function is noisy or not. If Kriging, then a nugget is added. Default is False. Note: Will be set in the Spot class.	False
n_theta	int	The number of theta values. If larger than 1 or set to the string “anisotropic”, then the k theta values are used, where k is the problem dimension. This is handled in spot.py. Default is “anisotropic”.	'anisotropic'
p_val	float	p value. Used as an initial value if optim_p = True. Otherwise as a constant. Defaults to 2.0.	2.0
n_p	int	The number of p values. Number of p values to be used. Default is 1.	1
optim_p	bool	Whether to optimize p or not.	False
min_Lambda	float	The minimum value of lambda. Default is 1e-9.	1e-09
max_Lambda	float	The maximum value of lambda. Default is 1.	1
seed	int	The seed to use for the random number generator.	124
theta_init_zero	bool	Whether to initialize theta with zero or not. Default is True.	True
var_type	list	A list containing the type of the variables. Default is None. Note: Will be set in the Spot class.	None
metric_factorial	str	The metric to be used for the factorial design. Default is “canberra”.	'canberra'

Returns:

Name	Type	Description
surrogate_control	dict	A dictionary containing the information about the surrogate model.

Note

• The surrogate_control dictionary is used in the Spot class. The following values are updated in the Spot class if they are None in the surrogate_control dictionary:
  • noise: If the surrogate model dictionary is passed to the Spot class, and the noise value is None, then the noise value is set in the Spot class based on the value of noise in the Spot class fun_control dictionary.
  • var_type: The var_type value is set in the Spot class based on the value of var_type in the Spot class fun_control dictionary and the dimension of the problem. If the Kriging model is used as a surrogate in the Spot class, the setting from surrogate_control_init() is overwritten.
  • n_theta: If self.surrogate_control[“n_theta”] > 1, use k theta values, where k is the problem dimension specified in the Spot class. The problem dimension is set in the Spot class based on the length of the lower bounds.
• This value model_fun_evals will used for the optimization of the surrogate model, e.g., theta values. Differential evaluation uses maxiter = 1000 and sets the number of function evaluations to (maxiter + 1) * popsize * N, which results in 1000 * 15 * k, because the default popsize is 15 and N is the number of parameters. This is already sufficient for many situations. For example, for k=2 these are 30 000 iterations. Therefore we set this value to 1000.

Source code in spotpython/utils/init.py

def surrogate_control_init(
    log_level: int = 50,
    noise=False,
    model_optimizer=differential_evolution,
    model_fun_evals=10000,
    min_theta=-3.0,
    max_theta=2.0,
    n_theta="anisotropic",
    p_val=2.0,
    n_p=1,
    optim_p=False,
    min_Lambda=1e-9,
    max_Lambda=1,
    seed=124,
    theta_init_zero=True,
    var_type=None,
    metric_factorial="canberra",
) -> dict:
    """Initialize surrogate_control dictionary.

    Args:
        model_optimizer (object):
            The optimizer object used for the search on surrogate.
            Default is differential_evolution.
        model_fun_evals (int):
            The number of function evaluations. This will be used for the
            optimization of the surrogate model. Default is 1000.
        min_theta (float):
            The minimum value of theta. Note that the base10-logarithm is used.
             Default is -3.
        max_theta (float): The maximum value of theta. Note that the base10-logarithm is used.
            Default is 3.
        noise (bool):
            Whether the objective function is noisy or not. If Kriging, then a nugget is added.
            Default is False. Note: Will be set in the Spot class.
        n_theta (int):
            The number of theta values. If larger than 1 or set to the string "anisotropic",
            then the k theta values are used, where k is the problem dimension.
            This is handled in spot.py. Default is "anisotropic".
        p_val (float):
                p value. Used as an initial value if optim_p = True. Otherwise as a constant. Defaults to 2.0.
        n_p (int):
            The number of p values. Number of p values to be used. Default is 1.
        optim_p (bool):
            Whether to optimize p or not.
        min_Lambda (float):
            The minimum value of lambda. Default is 1e-9.
        max_Lambda (float):
            The maximum value of lambda. Default is 1.
        seed (int):
            The seed to use for the random number generator.
        theta_init_zero (bool):
            Whether to initialize theta with zero or not. Default is True.
        var_type (list):
            A list containing the type of the variables. Default is None.
            Note: Will be set in the Spot class.
        metric_factorial (str):
            The metric to be used for the factorial design. Default is "canberra".

    Returns:
        surrogate_control (dict):
            A dictionary containing the information about the surrogate model.

    Note:
        * The surrogate_control dictionary is used in the Spot class. The following values
          are updated in the Spot class if they are None in the surrogate_control dictionary:
            * `noise`: If the surrogate model dictionary is passed to the Spot class,
              and the `noise` value is `None`, then the noise value is set in the
              Spot class based on the value of `noise` in the Spot class fun_control dictionary.
            * `var_type`: The `var_type` value is set in the Spot class based on the value
               of `var_type` in the Spot class fun_control dictionary and the dimension of the problem.
               If the Kriging model is used as a surrogate in the Spot class, the setting from
                surrogate_control_init() is overwritten.
            * `n_theta`: If self.surrogate_control["n_theta"] > 1,
               use k theta values, where k is the problem dimension specified in the Spot class.
               The problem dimension is set in the Spot class based on the
               length of the lower bounds.
        * This value `model_fun_evals` will used for the optimization of the surrogate model, e.g., theta values.
          Differential evaluation uses `maxiter = 1000` and sets the number of function evaluations to
          (maxiter + 1) * popsize * N, which results in 1000 * 15 * k,
          because the default popsize is 15 and N is the number of parameters. This is already sufficient
          for many situations. For example, for k=2 these are 30 000 iterations.
          Therefore we set this value to 1000.

    """
    surrogate_control = {
        "log_level": log_level,
        "noise": noise,
        "model_optimizer": model_optimizer,
        "model_fun_evals": model_fun_evals,
        "min_theta": min_theta,
        "max_theta": max_theta,
        "n_theta": n_theta,
        "p_val": p_val,
        "n_p": n_p,
        "optim_p": optim_p,
        "min_Lambda": min_Lambda,
        "max_Lambda": max_Lambda,
        "seed": seed,
        "theta_init_zero": theta_init_zero,
        "var_type": var_type,
        "metric_factorial": metric_factorial,
    }
    return surrogate_control