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 them with a rich set of custom encoders, splines, and neural basis expansions.

Note

These docs are for PreTab 0.1.0. The project is under active development and the public API may evolve while the major version is 0.

Highlights

🔢 Numerical preprocessing

Spline expansions (B-splines, natural cubic, thin-plate, tensor-product, P-splines), neural basis maps (RBF, ReLU, sigmoid, tanh), custom binning, and Piecewise Linear Encoding (PLE).

🌤 Categorical preprocessing

Ordinal and one-hot encodings, pretrained language embeddings, and helpers such as OneHotFromOrdinalTransformer.

🔧 Composable pipelines

Fully compatible with sklearn.pipeline.Pipeline and sklearn.compose.ColumnTransformer; accepts any sklearn-native transformer and its hyperparameters.

🧠 Smart defaults

Automatic feature-type detection (numerical vs. categorical) with support for both pandas.DataFrame and numpy.ndarray inputs.

See it in action

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

df = pd.DataFrame({
    "age": np.random.randint(18, 65, size=100),
    "income": np.random.normal(60_000, 15_000, size=100).astype(int),
    "city": np.random.choice(["Berlin", "Munich", "Hamburg"], size=100),
})
y = np.random.randn(100)

# One strategy per feature type: PLE for numerics, integer codes for categoricals
pre = Preprocessor(numerical_method="ple", categorical_method="int")
X = pre.fit_transform(df, y)          # dict of model-ready feature blocks

{k: v.shape for k, v in X.items()}
# {'num_age': (100, 7), 'num_income': (100, 7), 'cat_city': (100, 1)}

Preprocessor detects the column types, fits a strategy per column, and returns model-ready arrays, either as a dict of blocks or, with return_array=True, a single stacked matrix. Inspect the resolved layout at any time with get_feature_info(verbose=True):

feature  kind         pipeline                        dim   cats
----------------------------------------------------------------
age      numerical    imputer -> minmax -> ple          7      -
income   numerical    imputer -> minmax -> ple          7      -
city     categorical  imputer -> continuous_ordinal     1      4

Mix strategies per column

Columns rarely want the same treatment. Pass a feature_preprocessing map to give each column its own strategy, and a single fit still returns one coherent feature set:

pre = Preprocessor(feature_preprocessing={
    "age": "ple",       # piecewise-linear encoding
    "income": "rbf",    # radial-basis expansion
    "city": "one-hot",  # one-hot categorical
})
X = pre.fit_transform(df, y)

{k: v.shape for k, v in X.items()}
# {'num_age': (100, 7), 'num_income': (100, 7), 'cat_city': (100, 3)}