multitask.lazy.execute_lazy

multitask.lazy.execute_lazy(
    task,
    show=False,
    use_tuned_params=True,
    max_age_days=None,
)

Execute lazy fitting for all targets on task.

Thin wrapper around BaseTask._run_strategy using LazyStrategy. When use_tuned_params is True (the default), previously saved tuning results are loaded from cache and applied to the forecaster. If no cached results are found the forecaster uses default parameters.

Parameters

Name	Type	Description	Default
task	BaseTask	A `BaseTask` (or subclass) instance with prepared data.	required
show	bool	If `True`, invoke the visualisation hooks.	`False`
use_tuned_params	bool	If `True`, attempt to load cached tuning results (best parameters and lags) 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 (weighted combination of all targets).
	Dict[str, Any]	Per-target packages are stored on `task.results["lazy"]`.
	Dict[str, Any]	When `task.config.auto_save_models` is `True` (the default), fitted
	Dict[str, Any]	models are saved to disk so `PredictTask` can load them directly.

Examples

import tempfile
import pandas as pd
import numpy as np
from spotforecast2_safe.multitask.lazy import LazyTask, execute_lazy
from spotforecast2_safe.configurator.config_multi import ConfigMulti

rng = np.random.default_rng(0)
idx = pd.date_range("2023-01-01", periods=24 * 14, freq="h", tz="UTC")
df = pd.DataFrame({"load": rng.normal(100, 10, len(idx))}, index=idx)
df.index.name = "DateTime"

with tempfile.TemporaryDirectory() as tmp:
    cfg = ConfigMulti(
        predict_size=6,
        use_exogenous_features=False,
        use_outlier_detection=False,
        cache_home=tmp,
        auto_save_models=False,
        number_folds=2,
    )
    task = LazyTask(cfg, dataframe=df)
    task.prepare_data()
    result = execute_lazy(task, show=False, use_tuned_params=False)
    print(f"Keys: {list(result.keys())[:4]}")
    print(f"Forecast horizon: {len(result['future_pred'])} steps")
    assert len(result["future_pred"]) == 6

Keys: ['train_actual', 'train_pred', 'future_actual', 'future_pred']
Forecast horizon: 6 steps