manager.configurator.config_entsoe.ConfigEntsoe

manager.configurator.config_entsoe.ConfigEntsoe(
    api_country_code='DE',
    periods=None,
    lags_consider=None,
    train_size=None,
    end_train_default='2025-12-31 00:00+00:00',
    delta_val=None,
    predict_size=24,
    refit_size=7,
    random_state=314159,
    n_hyperparameters_trials=20,
    data_filename='interim/energy_load.csv',
)

Configuration for the ENTSO-E forecasting pipeline.

This class manages all configuration parameters for the ENTSO-E task, including API settings, training/prediction intervals, and feature engineering specifications. All parameters can be customized during initialization or used with sensible defaults.

Parameters

Name	Type	Description	Default
api_country_code	str	ISO country code for ENTSO-E API queries.	`'DE'`
periods	Optional[List[`Period`]]	List of Period objects defining cyclical feature encodings.	`None`
lags_consider	Optional[List[int]]	List of lag values to consider for feature selection.	`None`
train_size	Optional[pd.Timedelta]	Time window for training data.	`None`
end_train_default	str	Default end date for training period (ISO format with timezone).	`'2025-12-31 00:00+00:00'`
delta_val	Optional[pd.Timedelta]	Validation window size.	`None`
predict_size	int	Number of hours to predict ahead.	`24`
refit_size	int	Number of days between model refits.	`7`
random_state	int	Random seed for reproducibility.	`314159`
n_hyperparameters_trials	int	Number of trials for hyperparameter optimization.	`20`
data_filename	str	Path to the interim merged data file.	`'interim/energy_load.csv'`

Attributes

Name	Type	Description
API_COUNTRY_CODE	str	ISO country code for API queries.
periods	List[`Period`]	Cyclical feature encoding specifications.
lags_consider	List[int]	Lag values for autoregressive features.
train_size	pd.Timedelta	Training data window.
end_train_default	str	Default training end date.
delta_val	pd.Timedelta	Validation window.
predict_size	int	Prediction horizon in hours.
refit_size	int	Refit interval in days.
random_state	int	Random seed.
n_hyperparameters_trials	int	Hyperparameter tuning trials.

Notes

The default period configurations use specific n_periods to balance resolution and smoothing: - Daily: n_periods=12 (24h) provides ~2h resolution, smoothing hourly noise and halving dimensionality. - Weekly: n_periods typically matches range (1:1) to distinguish day-of-week patterns. - Yearly: n_periods=12 (365d) provides ~1 month resolution, capturing broad seasonal trends without overfitting.

See docs/PERIOD_CONFIGURATION_RATIONALE.md for a detailed analysis.

Examples

>>> from spotforecast2_safe import Config
>>> import pandas as pd
>>>
>>> # Use default configuration
>>> config = Config()
>>> config.API_COUNTRY_CODE
'DE'
>>> config.predict_size
24
>>> config.random_state
314159
>>>
>>> # Create custom configuration
>>> custom_config = Config(
...     api_country_code='FR',
...     predict_size=48,
...     random_state=42
... )
>>> custom_config.API_COUNTRY_CODE
'FR'
>>> custom_config.predict_size
48
>>>
>>> # Verify training window
>>> config.train_size == pd.Timedelta(days=3 * 365)
True
>>>
>>> # Check default periods
>>> len(config.periods)
5
>>> config.periods[0].name
'daily'

Methods

Name	Description
get_params	Get parameters for this configuration object.
set_params	Set the parameters of this configuration object.

get_params

manager.configurator.config_entsoe.ConfigEntsoe.get_params(deep=True)

Get parameters for this configuration object.

Parameters

Name	Type	Description	Default
deep	bool	If True, will return the parameters for this configuration and contained sub-objects that are estimators.	`True`

Returns

Name	Type	Description
params	Dict[str, object]	Dictionary of parameter names mapped to their values.

Examples

>>> from spotforecast2_safe.manager.configurator.config_entsoe import ConfigEntsoe
>>> config = ConfigEntsoe(api_country_code="FR")
>>> p = config.get_params()
>>> p["api_country_code"]
'FR'
>>> p["predict_size"]
24

set_params

manager.configurator.config_entsoe.ConfigEntsoe.set_params(
    params=None,
    **kwargs,
)

Set the parameters of this configuration object.

Parameters

Name	Type	Description	Default
params	Dict[str, object]	Optional dictionary of parameter names mapped to their new values.	`None`
**kwargs	object	Additional parameter names mapped to their new values. It supports configuring nested ‘Period’ objects using the `periods__<name>__<param>` notation.	`{}`

Returns

Name	Type	Description
ConfigEntsoe	ConfigEntsoe	The configuration instance with updated parameters (supports method chaining).

Examples

>>> from spotforecast2_safe.manager.configurator.config_entsoe import ConfigEntsoe
>>> config = ConfigEntsoe()
>>> _ = config.set_params(api_country_code="FR", predict_size=48)
>>> config.API_COUNTRY_CODE
'FR'
>>> config.predict_size
48

>>> # Deep parameter setting
>>> _ = config.set_params(periods__daily__n_periods=24)
>>> next(p.n_periods for p in config.periods if p.name == "daily")
24