ArcSinhTransformer

class feature_engine.transformation.ArcSinhTransformer(variables=None, loc=0.0, scale=1.0)

The ArcSinhTransformer() applies the inverse hyperbolic sine transformation (arcsinh) to numerical variables. Also known as the pseudo-logarithm, this transformation is useful for data that contains both positive and negative values.

The transformation is: x → arcsinh((x - loc) / scale)

For large values of x, arcsinh(x) behaves like ln(x) + ln(2), providing similar variance-stabilizing properties as the log transformation. For small values of x, it behaves approximately linearly (i.e., arcsinh(x) ≈ x). This makes it ideal for variables like net worth, profit/loss, or any metric that can be positive or negative.

A list of variables can be passed as an argument. Alternatively, the transformer will automatically select and transform all variables of type numeric.

More details in the User Guide.

Parameters

variables: list, default=None

The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.

loc: float, default=0.0

Location parameter for shifting the data before transformation. The transformation becomes: arcsinh((x - loc) / scale)

scale: float, default=1.0

Scale parameter for normalizing the data before transformation. Must be greater than 0. The transformation becomes: arcsinh((x - loc) / scale)

Attributes

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

See also

feature_engine.transformation.LogTransformer

Applies log transformation (only for positive values).

feature_engine.transformation.YeoJohnsonTransformer

Applies Yeo-Johnson transformation.

References

1

Burbidge, J. B., Magee, L., & Robb, A. L. (1988). Alternative transformations to handle extreme values of the dependent variable. Journal of the American Statistical Association, 83(401), 123-127.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from feature_engine.transformation import ArcSinhTransformer
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x = np.random.randn(100) * 1000))
>>> ast = ArcSinhTransformer()
>>> ast.fit(X)
>>> X = ast.transform(X)
>>> X.head()
          x
0  7.516076
1 -6.330816
2  7.780254
3  8.825252
4 -6.995893

Methods

fit:	This transformer does not learn parameters.
fit_transform:	Fit to data, then transform it.
get_feature_names_out:	Get output feature names for transformation.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
inverse_transform:	Convert the data back to the original representation.
transform:	Transform the variables using the arcsinh function.

fit(X, y=None)

Selects the numerical variables and stores feature names.

Parameters

X: Pandas DataFrame of shape = [n_samples, n_features].

The training input samples. Can be the entire dataframe, not just the variables to transform.

y: pandas Series, default=None

It is not needed in this transformer. You can pass y or None.

Returns

self: ArcSinhTransformer

The fitted transformer.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

inverse_transform(X)

Convert the data back to the original representation.

Parameters

X: Pandas DataFrame of shape = [n_samples, n_features]

The data to be inverse transformed.

Returns

X_tr: pandas dataframe

The dataframe with the inverse transformed variables.

rtype

DataFrame ..

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Transform the variables using the arcsinh function.

Parameters

X: Pandas DataFrame of shape = [n_samples, n_features]

The data to be transformed.

Returns

X_new: pandas dataframe

The dataframe with the transformed variables.

rtype

DataFrame ..