multitask.defaults.execute_defaults

multitask.defaults.execute_defaults(task, show=False)

Execute defaults fitting for all targets on task.

Thin wrapper around BaseTask._run_strategy using DefaultsStrategy.

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`

Returns

Name	Type	Description
	Dict[str, Any]	Aggregated prediction package (weighted combination of all targets,
	Dict[str, Any]	or the single-target package when `len(config.targets) == 1`).
	Dict[str, Any]	Per-target packages are stored on `task.results["defaults"]`.
	Dict[str, Any]	When `task.config.auto_save_models` is `True` (the default), fitted
	Dict[str, Any]	models are saved to disk so `PredictTask(task_name="defaults")` can
	Dict[str, Any]	load them directly.

Examples

import tempfile
import numpy as np
import pandas as pd
from pathlib import Path
from spotforecast2_safe.multitask.defaults import DefaultsTask, execute_defaults
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,
        auto_save_models=False,
        number_folds=2,
        cache_home=Path(tmp),
        verbose=False,
    )
    task = DefaultsTask(cfg, dataframe=df)
    task.prepare_data().detect_outliers().impute().build_exogenous_features()
    result = execute_defaults(task)

print(f"Future predictions: {len(result['future_pred'])} steps")
assert isinstance(result["future_pred"], pd.Series)
assert len(result["future_pred"]) == 6

Future predictions: 6 steps