manager.features.get_target_data

manager.features.get_target_data(
    target,
    df_pipeline,
    config,
    data_with_exog=None,
    exog_feature_names=None,
    exo_pred=None,
)

Extract the training series and exogenous slices for one target column.

Clips the target column of df_pipeline to the training window defined by config.start_train_ts and config.end_train_ts. When exogenous features are enabled (config.use_exogenous_features is True) and data_with_exog is provided, the matching exogenous training slice and forecast-horizon slice are also returned; otherwise both are None.

This function is the canonical way to extract per-target data from the shared pipeline state so that outlier removal, imputation, and feature engineering are applied consistently across all forecasting tasks.

Parameters

Name	Type	Description	Default
target	str	Name of the target column to extract from df_pipeline.	required
df_pipeline	pd.DataFrame	DataFrame with a tz-aware :class:`~pandas.DatetimeIndex` containing all target columns produced by the preprocessing pipeline.	required
config	'ConfigMulti'	Pipeline configuration object. Must have the following attributes set before calling this function: - `start_train_ts` — inclusive start of the training window (:class:`~pandas.Timestamp`, tz-aware). - `end_train_ts` — inclusive end of the training window (:class:`~pandas.Timestamp`, tz-aware). - `use_exogenous_features` — `bool` flag controlling whether exogenous features are used.	required
data_with_exog	Optional[pd.DataFrame]	Merged DataFrame of target and exogenous columns covering at least `[config.start_train_ts, config.end_train_ts]`. Required when `config.use_exogenous_features` is `True`. Pass `None` (default) to skip exogenous slicing.	`None`
exog_feature_names	Optional[List[str]]	Column names to select from data_with_exog and exo_pred. Required when data_with_exog is not `None`. Pass `None` (default) when exogenous features are disabled.	`None`
exo_pred	Optional[pd.DataFrame]	Exogenous feature DataFrame covering the forecast horizon `(config.end_train_ts, config.cov_end]`. Required when data_with_exog is not `None`. Pass `None` (default) when exogenous features are disabled.	`None`

Returns

Name	Type	Description
	pd.Series	Tuple[pd.Series, Optional[pd.DataFrame], Optional[pd.DataFrame]]:
	Optional[pd.DataFrame]	A three-tuple `(y_train, exog_train, exog_future)` where:
	Optional[pd.DataFrame]	- y_train — 1-D Series with the target values over the training window `[config.start_train_ts, config.end_train_ts]`, squeezed to a plain :class:`~pandas.Series`.
	Tuple[pd.Series, Optional[pd.DataFrame], Optional[pd.DataFrame]]	- exog_train — DataFrame of selected exogenous features over the training window, cast to `float32`. `None` when exogenous features are disabled or data_with_exog is `None`.
	Tuple[pd.Series, Optional[pd.DataFrame], Optional[pd.DataFrame]]	- exog_future — DataFrame of selected exogenous features covering the forecast horizon, cast to `float32`. `None` when exogenous features are disabled or exo_pred is `None`.

Examples

Extract training data for a single target without exogenous features:

import pandas as pd
import numpy as np
from spotforecast2_safe.manager.features import get_target_data
from spotforecast2_safe.manager.configurator.config_multi import ConfigMulti

idx = pd.date_range("2024-01-01", periods=168, freq="h", tz="UTC")
df_pipeline = pd.DataFrame({"load": np.random.default_rng(0).normal(100, 10, 168)}, index=idx)

config = ConfigMulti(
    targets=["load"],
    use_exogenous_features=False,
)
config.start_train_ts = pd.Timestamp("2024-01-01 00:00", tz="UTC")
config.end_train_ts   = pd.Timestamp("2024-01-07 23:00", tz="UTC")

y_train, exog_train, exog_future = get_target_data(
    target="load",
    df_pipeline=df_pipeline,
    config=config,
)
print(f"y_train length: {len(y_train)}")
print(f"exog_train:     {exog_train}")
print(f"exog_future:    {exog_future}")

y_train length: 168
exog_train:     None
exog_future:    None

Extract training data with exogenous features enabled:

import pandas as pd
import numpy as np
from spotforecast2_safe.manager.features import get_target_data
from spotforecast2_safe.manager.configurator.config_multi import ConfigMulti

rng = np.random.default_rng(1)
idx_train = pd.date_range("2024-01-01", periods=168, freq="h", tz="UTC")
idx_future = pd.date_range("2024-01-08", periods=24, freq="h", tz="UTC")

df_pipeline = pd.DataFrame({"load": rng.normal(100, 10, 168)}, index=idx_train)

data_with_exog = pd.DataFrame(
    {
        "load": df_pipeline["load"],
        "hour_sin": np.sin(2 * np.pi * idx_train.hour / 24),
        "hour_cos": np.cos(2 * np.pi * idx_train.hour / 24),
    },
    index=idx_train,
)
exo_pred = pd.DataFrame(
    {
        "hour_sin": np.sin(2 * np.pi * idx_future.hour / 24),
        "hour_cos": np.cos(2 * np.pi * idx_future.hour / 24),
    },
    index=idx_future,
)

config = ConfigMulti(targets=["load"], use_exogenous_features=True)
config.start_train_ts = pd.Timestamp("2024-01-01 00:00", tz="UTC")
config.end_train_ts   = pd.Timestamp("2024-01-07 23:00", tz="UTC")

y_train, exog_train, exog_future = get_target_data(
    target="load",
    df_pipeline=df_pipeline,
    config=config,
    data_with_exog=data_with_exog,
    exog_feature_names=["hour_sin", "hour_cos"],
    exo_pred=exo_pred,
)
print(f"y_train length:     {len(y_train)}")
print(f"exog_train shape:   {exog_train.shape}")
print(f"exog_future shape:  {exog_future.shape}")
print(f"exog_train dtype:   {exog_train.dtypes.iloc[0]}")

y_train length:     168
exog_train shape:   (168, 2)
exog_future shape:  (24, 2)
exog_train dtype:   float32