multitask.SpotOptimTask

multitask.SpotOptimTask(
    config=None,
    *,
    dataframe=None,
    data_test=None,
    cache_home=None,
    log_level=logging.INFO,
    **overrides,
)

Task 4 — SpotOptim surrogate-model Bayesian tuning.

Uses spotoptim for surrogate-model-based Bayesian optimisation. Effective with small trial budgets.

Examples

from spotforecast2.multitask import SpotOptimTask

task = SpotOptimTask(n_trials_spotoptim=10, predict_size=24)
print(f"Task: {task.TASK}")
print(f"SpotOptim trials: {task.config.n_trials_spotoptim}")

Task: spotoptim
SpotOptim trials: 10

Methods

Name	Description
run	Run SpotOptim surrogate-model tuning for all targets.

run

multitask.SpotOptimTask.run(show=True, search_space=None, **kwargs)

Run SpotOptim surrogate-model tuning for all targets.

Parameters

Name	Type	Description	Default
show	bool	If True, display prediction figures.	True
search_space	Optional[Dict[str, Any]]	Dictionary defining the SpotOptim search space. None uses the built-in default.	None

Returns

Name	Type	Description
	Dict[str, Any]	Aggregated prediction package. Per-target packages are stored
	Dict[str, Any]	on self.results["spotoptim"].

Examples

# Construct the task and verify configuration before running.
# A full run requires prepared data (prepare_data, impute, etc.);
# this example demonstrates construction and config inspection.
from spotforecast2.multitask.spotoptim import SpotOptimTask

task = SpotOptimTask(
    n_trials_spotoptim=5,
    n_initial_spotoptim=3,
    predict_size=24,
    auto_save_models=False,
)
print(f"Task type: {task.TASK}")
print(f"Trials: {task.config.n_trials_spotoptim}")
print(f"Initial evaluations: {task.config.n_initial_spotoptim}")
assert task.config.n_trials_spotoptim == 5
assert task.config.auto_save_models is False

Task type: spotoptim
Trials: 5
Initial evaluations: 3