manager.multitask.LazyTask

manager.multitask.LazyTask(
    dataframe=None,
    data_test=None,
    data_frame_name='default',
    cache_home=None,
    agg_weights=None,
    index_name='DateTime',
    number_folds=10,
    predict_size=24,
    bounds=None,
    contamination=0.03,
    imputation_method='weighted',
    use_exogenous_features=True,
    n_trials_optuna=15,
    n_trials_spotoptim=10,
    n_initial_spotoptim=5,
    auto_save_models=True,
    train_days=365 * 2,
    val_days=7 * 2,
    log_level=logging.INFO,
    verbose=False,
    **config_overrides,
)

Task 1 — Lazy Fitting with default LightGBM parameters.

Creates an unfitted forecaster per target and fits with default hyperparameters. No cross-validation or tuning is performed.

When cached tuning results are available (saved by OptunaTask or SpotOptimTask), they are loaded and applied automatically so that the lazy task benefits from prior tuning without re-running the search.

Examples

from spotforecast2.manager.multitask import LazyTask

task = LazyTask(data_frame_name="demo10", predict_size=24)
print(f"Task: {task.TASK}")
print(f"Predict size: {task.config.predict_size}")

Task: lazy
Predict size: 24

Methods

Name	Description
run	Run lazy fitting for all targets.

run

manager.multitask.LazyTask.run(
    show=True,
    use_tuned_params=True,
    max_age_days=None,
    **kwargs,
)

Run lazy fitting for all targets.

Parameters

Name	Type	Description	Default
show	bool	If `True`, display prediction figures.	`True`
use_tuned_params	bool	If `True`, load and apply cached tuning results for each target.	`True`
max_age_days	Optional[float]	Maximum age in days for cached tuning results. `None` accepts any age.	`None`

Returns

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