autofepg 0.1.3

AutoFE - Playground: Automatic Feature Engineering & Selection for Kaggle Playground Competitions

🧪 AutoFE-PG

Automatic Feature Engineering & Selection for Kaggle Playground Competitions

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

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✨ Key Features

Feature	Description
Auto column detection	Automatically identifies categorical vs. numerical columns
20+ feature strategies	Target encoding, count encoding, digit extraction, arithmetic interactions, group statistics, and more
Zero target leakage	All target-dependent features use strict out-of-fold encoding
Greedy forward selection	Adds features one-by-one, keeping only those that improve CV score
Optional backward pruning	Removes redundant features after forward selection
GPU acceleration	Automatically uses XGBoost GPU if available
Time budget	Set a wall-clock limit; the search stops gracefully
Sampling support	Evaluate on a subsample for faster iteration
Custom XGBoost params	Pass your own hyperparameters
Score variance tracking	Reports mean ± std across folds
Classification & regression	Supports both tasks with auto-detection

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🚀 Quick Start

Installation

pip install autofepg .

Or install dependencies directly:

pip install -r requirements.txt

Minimal Example

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,
)

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

print(f"Baseline AUC: {result['base_score']:.6f}")
print(f"Best AUC:     {result['best_score']:.6f}")
print(f"Features added: {len(result['selected_features'])}")

Using the Class API

from autofepg import AutoFE

autofe = AutoFE(
    task="classification",
    n_folds=5,
    time_budget=1800,
    improvement_threshold=0.0001,
    backward_selection=True,
    sample=10000,
    xgb_params={
        "n_estimators": 1000,
        "max_depth": 8,
        "learning_rate": 0.05,
    },
)

X_train_new, X_test_new = autofe.fit_select(
    X_train, y_train, X_test,
    aux_target_cols=["employment_status", "debt_to_income_ratio"],
)

# Inspect results
print(autofe.get_selected_feature_names())
history_df = autofe.get_history()

---

📖 How It Works

1. Feature Generation

AutoFE-PG generates candidates from a hardcoded priority sequence ordered by expected impact:

Priority	Strategy	Leakage-free?
1	Target Encoding (single columns)	✅ OOF
2	Count Encoding (single columns)	✅ No target
3	Target Encoding on pairs	✅ OOF
4	Count Encoding on pairs	✅ No target
5	Frequency Encoding	✅ No target
6	Missing Indicators	✅ No target
7	TE with auxiliary targets	✅ OOF
8	Unary transforms (log, sqrt, etc.)	✅ No target
9	Arithmetic interactions	✅ No target
10	Polynomial features	✅ No target
11	Pairwise label-encoded interactions	✅ No target
12	TE/CE on digit features	✅ OOF / No target
13	Digit × Category TE	✅ OOF
14	Quantile binning	✅ No target
15	Raw digit extraction	✅ No target
16	Digit interactions	✅ No target
17	Rounding features	✅ No target
18	Num-to-Cat conversion	✅ No target
19	Group statistics & deviations	✅ No target

2. Greedy Forward Selection

Each candidate is evaluated by adding it to the current feature set and running XGBoost K-fold CV. A feature is kept only if it improves the score beyond the configured threshold.

3. Optional Backward Pruning

After forward selection, features are tested for removal. If removing a feature improves (or maintains) the score, it is permanently dropped.

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⚙️ Configuration

Parameter	Type	Default	Description
task	str	"auto"	"classification", "regression", or "auto"
n_folds	int	5	Number of CV folds
time_budget	float	None	Max seconds (wall clock)
improvement_threshold	float	1e-7	Min score delta to keep a feature
sample	int	None	Subsample rows for faster CV
backward_selection	bool	False	Run backward pruning after forward
max_pair_cols	int	20	Max columns for pairwise features
max_digit_positions	int	4	Max digit positions to extract
xgb_params	dict	None	Custom XGBoost hyperparameters
metric_fn	callable	None	Custom metric (y_true, y_pred) -> float
metric_direction	str	None	"maximize" or "minimize"
random_state	int	42	Random seed
verbose	bool	True	Print progress

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📊 Output

The select_features() function returns a dictionary:

{
    "X_train": pd.DataFrame,          # Augmented training data
    "X_test": pd.DataFrame,           # Augmented test data (if provided)
    "autofe": AutoFE,                 # Fitted AutoFE object
    "history": pd.DataFrame,          # Full selection history
    "selected_features": List[str],   # Names of kept features
    "base_score": float,              # Baseline CV mean
    "base_score_std": float,          # Baseline CV std
    "best_score": float,              # Final CV mean
    "best_score_std": float,          # Final CV std
}

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🧪 Running Tests

pytest tests/ -v

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📁 Project Structure

autofepg/
├── autofepg/
│   ├── __init__.py          # Public API
│   ├── utils.py             # GPU detection, task inference, metrics
│   ├── generators.py        # All feature generator classes
│   ├── builder.py           # FeatureCandidateBuilder
│   ├── engine.py            # XGBoost CV engine
│   └── core.py              # AutoFE class + select_features()
├── tests/
│   ├── __init__.py
│   └── test_autofepg.py     # Unit and integration tests
├── examples/
│   ├── example_classification.py
│   └── example_regression.py
├── .github/
│   └── workflows/
│       └── ci.yml
├── .gitignore
├── LICENSE
├── README.md
├── CHANGELOG.md
├── CONTRIBUTING.md
├── Makefile
├── pyproject.toml
├── setup.py
└── requirements.txt