Time-Aware Tabular Prototype

A practical prototype for leakage-safe auto feature engineering on time-aware tabular data.

Why this example matters

Most real feature engineering failures are not caused by weak transformations. They come from:

• random train/test splits on temporal data
• future information leaking into features
• offline features that cannot be reproduced later

This example shows a safer baseline:

1. sort by time
2. split by time
3. fit features on the training slice only
4. transform the holdout slice separately
5. compare against a plain model baseline

Script

See:

examples/time_aware_tabular_prototype.py

Core pattern

engineer = AutoFeatureEngineer(
    engines=["tabular"],
    max_features=30,
    selection_methods=["mutual_info", "importance"],
    correlation_threshold=0.9,
    leakage_guard="warn",
)

X_train_fe = engineer.fit_transform(
    X_train,
    y_train,
    target_name="churned",
    apply_selection=True,
)
X_test_fe = engineer.transform(X_test)

Leakage guard

AutoFeatureEngineer now supports a lightweight leakage_guard option:

• "warn" — default, warns if suspicious columns are present
• "raise" — fail fast when likely leakage columns are detected
• "off" — disable the check

This is intentionally conservative. It does not prove your pipeline is safe. It just catches obvious foot-guns such as columns named like:

• target
• label
• outcome
• future_*

Recommendation

For a real project, start with this workflow before trying more advanced LLM or agent-based feature generation. If the time-aware baseline is not trustworthy, more automation only makes the mistake faster.