multitask.predict.PredictTask

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

Task 5 — Predict-only using previously saved models.

Loads fitted forecasters that were persisted by a prior LazyTask or DefaultsTask run (or by any task in the spotforecast2 sibling package) and produces predictions for all configured targets. No training or tuning is performed.

If no saved models exist in the cache directory the run method raises RuntimeError with an informative message.

Examples

import tempfile
from pathlib import Path
from spotforecast2_safe.multitask import PredictTask
from spotforecast2_safe.configurator.config_multi import ConfigMulti

with tempfile.TemporaryDirectory() as tmp:
    cfg = ConfigMulti(data_frame_name="demo10", predict_size=24, cache_home=Path(tmp))
    task = PredictTask(cfg)
    print(f"Task: {task.TASK}")
    print(f"Predict size: {task.config.predict_size}")

Task: predict
Predict size: 24

Methods

Name	Description
run	Run prediction using previously saved models.

run

multitask.predict.PredictTask.run(
    show=False,
    task_name=None,
    max_age_days=None,
    **kwargs,
)

Run prediction using previously saved models.

Parameters

Name	Type	Description	Default
show	bool	If `True`, invoke the visualisation hooks.	`False`
task_name	Optional[str]	Restrict model loading to a specific source task (`"lazy"`, `"defaults"`, `"optuna"`, or `"spotoptim"`). `None` loads the most recent model regardless of source.	`None`
max_age_days	Optional[float]	Maximum age in days for saved models. Models older than this are ignored. `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["predict"]`.

Raises

Name	Type	Description
	RuntimeError	If no saved models are found in the cache directory, or if a target has no matching model.

Examples

import tempfile
import warnings
from pathlib import Path

import numpy as np
import pandas as pd

from spotforecast2_safe.configurator.config_multi import ConfigMulti
from spotforecast2_safe.multitask.lazy import LazyTask
from spotforecast2_safe.multitask.predict import PredictTask

warnings.filterwarnings("ignore")

rng = np.random.default_rng(0)
idx = pd.date_range("2023-01-01", periods=300, freq="h", tz="UTC")
df = pd.DataFrame(
    {"total_load_actual": rng.normal(10_000, 500, 300).clip(8_000, 12_000)},
    index=idx,
)
df.index.name = "DateTime"

with tempfile.TemporaryDirectory() as tmp:
    cfg = ConfigMulti(
        data_frame_name="demo",
        predict_size=12,
        auto_save_models=True,
        cache_home=Path(tmp),
        targets=["total_load_actual"],
        verbose=False,
        use_outlier_detection=False,
        use_exogenous_features=False,
        number_folds=2,
    )
    # Train and save a model with LazyTask first.
    lazy = LazyTask(cfg, dataframe=df)
    lazy.prepare_data()
    lazy.run()

    # Run PredictTask to load the saved model and forecast.
    task = PredictTask(cfg, dataframe=df)
    task.prepare_data()
    result = task.run()

    print(f"Future predictions shape: {result['future_pred'].shape}")
    assert result["future_pred"].shape == (12,)
    assert "metrics_train" in result

Future predictions shape: (12,)