manager.persistence

manager.persistence

Functions

Name	Description
save_forecaster	Save a single trained forecaster to disk using joblib.

save_forecaster

manager.persistence.save_forecaster(
    forecaster,
    model_dir,
    target,
    task_name='',
    verbose=False,
)

Save a single trained forecaster to disk using joblib.

Public single-model counterpart to :func:_save_forecasters. When task_name is provided the file is named {task_name}_{target}.joblib; otherwise the standard convention forecaster_{target}.joblib (identical to :func:_get_model_filepath) is used.

Parameters

Name	Type	Description	Default
forecaster	object	Trained forecaster object (any joblib-serialisable model).	required
model_dir	Union[str, Path]	Directory to save the model. Created if it doesn’t exist.	required
target	str	Target variable name used in the filename.	required
task_name	str	Optional task identifier prepended to the filename (e.g. "task_1_lazy"). Defaults to "" (standard naming).	''
verbose	bool	Print a confirmation message. Default: False.	False

Returns

Name	Type	Description
Path	Path	Full filepath of the saved model.

Raises

Name	Type	Description
	OSError	If the model cannot be written to disk.

Examples

>>> import tempfile
>>> from pathlib import Path
>>> from sklearn.linear_model import LinearRegression
>>> from spotforecast2_safe.manager.persistence import save_forecaster
>>> model = LinearRegression()
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     path = save_forecaster(model, tmpdir, "power")
...     print(path.name)
forecaster_power.joblib
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     path = save_forecaster(model, tmpdir, "power", task_name="task_1")
...     print(path.name)
task_1_power.joblib