📦 pretab

pretab is a modular, extensible, and scikit-learn-compatible preprocessing library for tabular data. It supports all sklearn transformers out of the box, and extends functionality with a rich set of custom encoders, splines, and neural basis expansions.

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✨ Features

• 🔢 Numerical preprocessing via:

  • Polynomial and spline expansions: B-splines, natural cubic splines, thin plate splines, tensor product splines, P-splines
  • Neural-inspired basis: RBF, ReLU, Sigmoid, Tanh
  • Custom binning: rule-based or tree-based
  • Piecewise Linear Encoding (PLE)

• 🌤 Categorical preprocessing:

  • Ordinal encodings
  • One-hot encodings
  • Language embeddings (pretrained vectorizers)
  • Custom encoders like OneHotFromOrdinalTransformer

• 🔧 Composable pipeline interface:

  • Fully compatible with sklearn.pipeline.Pipeline and sklearn.compose.ColumnTransformer
  • Accepts all sklearn-native transformers and parameters seamlessly

• 🧠 Smart preprocessing:

  • Automatically detects feature types (categorical vs numerical)
  • Supports both pandas.DataFrame and numpy.ndarray inputs

• 🧪 Comprehensive test coverage

• 🤝 Community-driven and open to contributions

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💠 Installation

Install via pip:

pip install pretab

Or install in editable mode for development:

git clone https://github.com/OpenTabular/pretab.git
cd pretab
pip install -e .

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🚀 Quickstart

import pandas as pd
import numpy as np
from pretab.preprocessor import Preprocessor

# Simulated tabular dataset
df = pd.DataFrame({
    "age": np.random.randint(18, 65, size=100),
    "income": np.random.normal(60000, 15000, size=100).astype(int),
    "job": np.random.choice(["nurse", "engineer", "scientist", "teacher", "artist", "manager"], size=100),
    "city": np.random.choice(["Berlin", "Munich", "Hamburg", "Cologne"], size=100),
    "experience": np.random.randint(0, 40, size=100)
})

y = np.random.randn(100, 1)

# Optional feature-specific preprocessing config
config = {
    "age": "ple",
    "income": "rbf",
    "experience": "quantile",
    "job": "one-hot",
    "city": "none"
}

# Initialize Preprocessor
preprocessor = Preprocessor(
    feature_preprocessing=config,
    task="regression"
)

# Fit and transform the data into a dictionary of feature arrays
X_dict = preprocessor.fit_transform(df, y)

# Optionally get a stacked array instead of a dictionary
X_array = preprocessor.transform(df, return_array=True)

# Get feature metadata
preprocessor.get_feature_info(verbose=True)

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🪰 Included Transformers

pretab includes both sklearn-native and custom-built transformers:

🌈 Splines

• CubicSplineTransformer
• NaturalCubicSplineTransformer
• PSplineTransformer
• TensorProductSplineTransformer
• ThinPlateSplineTransformer

🧠 Feature Maps

• RBFExpansionTransformer
• ReLUExpansionTransformer
• SigmoidExpansionTransformer
• TanhExpansionTransformer

📊 Encodings and Binning

• PLETransformer
• CustomBinTransformer
• OneHotFromOrdinalTransformer
• ContinuousOrdinalTransformer
• LanguageEmbeddingTransformer

🔧 Utilities

• NoTransformer
• ToFloatTransformer

Plus: any sklearn transformer can be passed directly with full support for hyperparameters.

Using Transformers

Using the transformers follows the standard sklearn.preprocessing steps. I.e. using PLE

import numpy as np
from pretab.transformers import PLETransformer

x = np.random.randn(100, 1)
y = np.random.randn(100, 1)

x_ple = PLETransformer(n_bins=15, task="regression").fit_transform(x, y)

assert x_ple.shape[1] == 15

For splines, the penalty matrices can be extracted via .get_penalty_matrix()

import numpy as np
from pretab.transformers import ThinPlateSplineTransformer

x = np.random.randn(100, 1)

tp = ThinPlateSplineTransformer(n_basis=15)

x_tp = tp.fit_transform(x)

assert x_tp.shape[1] == 15

penalty = tp.get_penalty_matrix()

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🧪 Running Tests

pytest --maxfail=2 --disable-warnings -v

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🤝 Contributing

pretab is community-driven! Whether you’re fixing bugs, adding new encoders, or improving the docs — contributions are welcome.

git clone https://github.com/OpenTabular/pretab.git
cd pretab
pip install -e ".[dev]"

Then create a pull request 🚀

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📄 License

MIT License. See LICENSE for details.

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❤️ Acknowledgements

pretab builds on the strengths of:

• scikit-learn

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