🧪 AutoFE-PG

Automatic Feature Engineering & Selection for Kaggle Playground Competitions

AutoFE-PG is a powerful library that automatically generates, evaluates, and selects engineered features to boost your tabular ML models — with zero target leakage.

Version 0.3.0 is a complete refactoring focused on general-purpose strategies that work across any tabular competition, featuring advanced binning, digit-based features, Cyclical encoding, Weight of Evidence, and Genetic Programming interactions.

✨ Key Features

🚀 Quick Start

Installation

pip install autofepg
# Optional: for Genetic Programming features
pip install gplearn

Basic Usage

import pandas as pd
from autofepg import select_features

train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")

X_train = train.drop(columns=["id", "target"])
y_train = train["target"]
X_test = test.drop(columns=["id"])

result = select_features(
    X_train, y_train, X_test,
    task="classification",
    time_budget=3600  # 1 hour limit
)

X_train_new = result["X_train"]
X_test_new = result["X_test"]

print(f"Features added: {len(result['selected_features'])}")
print(f"CV Improvement: {result['base_score']:.6f} -> {result['best_score']:.6f}")

Injecting Historical Signals (Original Data)

If you have access to a "real world" dataset (common in Kaggle Playground synthetic competitions), you can inject its signals without leakage:

result = select_features(
    X_train, y_train, X_test,
    original_df=original_df,
    original_target=original_target,
    task="classification"
)

📖 Feature Strategies (v0.3.0)

1. Digits & Discretization

• Digit Extraction: Integer positions (units, tens, etc.) and decimal positions.
• Digit Interactions: Column-wise and cross-column interactions between digits.
• Binning: Discretize continuous variables via Quantile (qcut) or Equal-width (cut) bins.
• Rounding: Rounding to various decimal places or magnitudes to find structural splits.

2. Specialized Encoding

• Cyclical Encoding: Sin/Cos transforms for periodic data.
• Target Encoding (OOF): Out-of-fold mean target per category.
• Weight of Evidence (WoE): OOF WoE scores for binary classification.
• Entropy: OOF target entropy per value group.
• OOF Aggregation: Mean, Std, and Skew of the target grouped by feature values.

3. Non-Linear Interactions

• Genetic Programming: Evolves mathematical expressions using the base features (requires gplearn).
• Pair Interactions: Categorical label-encoding of bigrams.
• Numerical Products: NaN-safe products of bigram numerical features.
• Digit × Category: Target encoding on the interaction of a column's digit and another category.

4. External Data Signals

• Bayesian Priors: Historical P(target|value) from the original dataset.
• External WoE: WoE scores pre-computed from the original dataset.
• External Entropy: Group purity/impurity derived from the original dataset.

⚙️ Configuration

📝 Changelog

v0.3.0 (Current)

• Refactoring: Removed competition-specific features (Domain Alignment, Dataset Frequency, Rarity).
• New Features: Cyclical Features, OOF/External WoE, OOF/External Entropy, Genetic Programming (gplearn).
• Enhanced Digits: Added Decimal Digit extraction.
• Enhanced Aggregation: Added Skewness support to OOF Target Aggregation.
• Simplified API: Decoupled from specific dataset patterns; focused on universal engineering.

v0.2.0

• Added original dataset support (Domain Alignment, Bayesian Priors).
• Introduced Cross-Dataset Frequency and Rarity features.

📄 License

MIT License — Copyright (c) 2026 Thomas Tschinkel.