Feature Configuration

This section documents configuration objects used to control panel feature engineering behavior in eb-features.

Configuration objects define which features are built, how leakage is handled, and how missing history is treated.

All content below is generated automatically from NumPy-style docstrings in the source code.

Configuration API

eb_features.panel.config

Configuration objects for panel time-series feature engineering.

This module defines declarative configuration used by the panel feature engineering pipeline. The configuration is designed to be:

• frequency-agnostic (lags/windows are expressed in index steps, not wall-clock time)
• stateless (config describes what to compute; no fitted state is stored)
• explicit (feature families are enabled/disabled via lists/flags)

Notes

This configuration is consumed by FeatureEngineer and related helper modules (lags, rolling, calendar, encoders).

FeatureConfig dataclass

Configuration for panel time-series feature engineering.

The feature engineering pipeline assumes a long-form panel DataFrame with an entity identifier column, a timestamp column, and a numeric target column. This configuration describes which feature families to generate and which passthrough columns to include.

Notes

• Lag steps and rolling windows are expressed in index steps (rows) at the input frequency, not in wall-clock units.
• Lags and rolling windows are computed within each entity.
• If dropna=True, rows lacking sufficient lag/rolling history are dropped after feature construction.

Attributes:

Name	Type	Description
lag_steps	Sequence[int] | None	Positive lag offsets (in steps) applied to the target. For each k in lag_steps, the feature lag_{k} is added.
rolling_windows	Sequence[int] | None	Positive rolling window lengths (in steps) applied to the target. For each w in rolling_windows and each stat in rolling_stats, the feature roll_{w}_{stat} is added.
rolling_stats	Sequence[str]	Rolling statistics to compute. Allowed values are: {"mean", "std", "min", "max", "sum", "median"}.
calendar_features	Sequence[str]	Calendar features derived from the timestamp column. Allowed values are: {"hour", "dow", "dom", "month", "is_weekend"}.
use_cyclical_time	bool	If True, add sine/cosine encodings for hour and day-of-week when those base calendar columns are present.
regressor_cols	Sequence[str] | None	Numeric external regressors to pass through. If None, numeric columns may be auto-detected by the calling pipeline (excluding entity/timestamp/target and static_cols).
static_cols	Sequence[str] | None	Entity-level metadata columns already present on the input DataFrame. These are passed through directly as features.
dropna	bool	If True, drop rows with NaNs in any engineered feature columns (typically caused by lags and rolling windows).