Preprocessing overview

The Preprocessor (pretab.preprocessor.Preprocessor) is the main entry point. It inspects a pandas.DataFrame, decides which columns are numerical and which are categorical, and builds a scikit-learn ColumnTransformer that applies a chosen strategy per feature.

How feature types are detected

By default, columns are classified automatically:

• Numerical: continuous columns, and integer columns with enough distinct values.

• Categorical: string/object columns, and low-cardinality integer columns.

The behaviour is controlled by several constructor arguments:

cat_cutoff

Threshold that decides whether an integer column is treated as categorical.

treat_all_integers_as_numerical

When True, every integer column is treated as numerical regardless of cardinality.

Choosing strategies

There are two ways to configure preprocessing:

1. Globally via numerical_method and categorical_method.

2. Per feature via the feature_preprocessing dict, which overrides the global defaults for specific columns.

from pretab import Preprocessor

# Global strategy for every column of a given type
preprocessor = Preprocessor(
    numerical_method="ple",
    categorical_method="int",
)

# Or override individual columns
preprocessor = Preprocessor(
    feature_preprocessing={
        "age": "ple",
        "income": "rbf",
        "city": "one-hot",
    },
)

Numerical strategies

Strategy	Transformer	Notes
standardization	StandardScaler	Zero mean, unit variance
minmax	MinMaxScaler	Scaled to [-1, 1]
quantile	QuantileTransformer	Rank-based normalisation
robust	RobustScaler	Robust to outliers
polynomial	PolynomialFeatures	Polynomial interactions
box-cox	PowerTransformer	Positive inputs only
yeo-johnson	PowerTransformer	Handles zero/negative values
ple	PLETransformer	Piecewise linear encoding
custombin	CustomBinTransformer	Rule- or tree-based binning
rbf	RBFExpansionTransformer	Radial basis functions
relu	ReLUExpansionTransformer	ReLU basis expansion
sigmoid	SigmoidExpansionTransformer	Sigmoid basis expansion
tanh	TanhExpansionTransformer	Tanh basis expansion
cubicspline	CubicSplineTransformer	B-spline basis
naturalspline	NaturalCubicSplineTransformer	Natural cubic spline
pspline	PSplineTransformer	Penalised B-spline
tensorspline	TensorProductSplineTransformer	Tensor-product spline
tprs	ThinPlateSplineTransformer	Thin-plate regression spline
none	NoTransformer	Pass-through

Categorical strategies

Strategy	Transformer	Notes
int	ContinuousOrdinalTransformer	Integer/ordinal encoding (default)
one-hot	OneHotEncoder	One-hot encoding
onehot_from_ordinal	OneHotFromOrdinalTransformer	One-hot from pre-encoded ordinals
pretrained	LanguageEmbeddingTransformer	Pretrained language embeddings
custombin	CustomBinTransformer	Binning of categorical codes
none	NoTransformer	Pass-through

Note

The pretrained strategy requires the optional sentence-transformers dependency. Install it with pip install "pretab[embeddings]".

Output format

fit_transform and transform return a dictionary that maps each feature to its transformed array (keys are prefixed with num_ or cat_). Pass return_array=True to transform to receive a single stacked numpy.ndarray instead.

X_dict = preprocessor.fit_transform(df, y)   # {"num_age": ..., "cat_city": ...}
X_array = preprocessor.transform(df, return_array=True)   # single ndarray

Use get_feature_info(verbose=True) to inspect the resolved strategy and output dimensionality of every feature.

Using transformers directly

Every transformer listed above is also importable from pretab.transformers and works as a standalone scikit-learn transformer, so it can be composed into any Pipeline or ColumnTransformer. See the Quickstart for examples, and the API Reference for the full parameter list of each class.