scikit-extensions 0.1.3

A collection of Scikit-learn-compatible transformers for feature engineering, preprocessing, and pipeline utilities.

Scikit-extensions

A collection of Scikit-learn-compatible transformers for feature engineering, preprocessing, and pipeline utilities.

Installation

pip install scikit-extensions

Or with Poetry:

poetry add scikit-extensions

Features

1. StatsModelsOLS

A scikit-learn compatible wrapper for statsmodels OLS regression that provides additional statistical insights.

from skext.linear_model import OLSRegressor

# Create and fit model
model = OLSRegressor(fit_intercept=True)
model.fit(X, y)

# Get predictions
y_pred = model.predict(X_test)

# Access statsmodels summary
print(model.summary())

2. MulticollinearityRemover

Removes highly correlated features using various strategies.

from skext.multicolinerity import MulticollinearityRemover

# Initialize transformer
remover = MulticollinearityRemover(
    correlation_threshold=0.8,
    strategy='variance'  # Options: 'first', 'last', 'variance', 'missing_ratio', 'random'
)

# Fit and transform
X_transformed = remover.fit_transform(X)

# Get selected features
selected_features = remover.selected_features_

3. MultiLabelBinarizerTransformer

Transform multi-label columns in pandas DataFrames to binary indicator matrices.

from skext.multilabels import MultiLabelBinarizerTransformer

# Sample data
data = pd.DataFrame({
    'tags': [['python', 'ml'], ['python'], ['ml', 'deep-learning']],
    'categories': [['tech'], ['tech', 'tutorial'], ['tutorial']]
})

# Initialize and transform
mlb = MultiLabelBinarizerTransformer(sparse_output=False)
transformed = mlb.fit_transform(data)

Development

1. Clone the repository:

git clone https://github.com/sh99-git/scikit-extensions.git
cd scikit-extensions

2. Install dependencies:

poetry install

Running Tests

make test  # Run tests with coverage
make build  # Run tests and build package

License

MIT License. See LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests (make test)
5. Submit a pull request

Requirements

• Python ≥ 3.10
• scikit-learn ≥ 1.7.2
• pandas ≥ 2.3.2
• statsmodels ≥ 0.14.5