sksurv.preprocessing.OneHotEncoder

class sksurv.preprocessing.OneHotEncoder(allow_drop=True)

Encode categorical columns with M categories into M-1 columns according to the one-hot scheme.

The order of non-categorical columns is preserved, encoded columns are inserted inplace of the original column.

Parameters:
allow_drop : boolean, optional, default: True

Whether to allow dropping categorical columns that only consist of a single category.

Attributes:
feature_names_ : pandas.Index

List of encoded columns.

categories_ : dict

Categories of encoded columns.

encoded_columns_ : list

Name of columns after encoding. Includes names of non-categorical columns.

__init__(allow_drop=True)

Methods

__init__([allow_drop])
fit(X[, y]) Retrieve categorical columns.
fit_transform(X[, y]) Convert categorical columns to numeric values.
transform(X) Convert categorical columns to numeric values.
fit(X, y=None)

Retrieve categorical columns.

Parameters:
X : pandas.DataFrame

Data to encode.

y :

Ignored. For compatibility with Pipeline.

Returns
——-
self : object

Returns self

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

Convert categorical columns to numeric values.

Parameters:
X : pandas.DataFrame

Data to encode.

y :

Ignored. For compatibility with TransformerMixin.

fit_params :

Ignored. For compatibility with TransformerMixin.

Returns:
Xt : pandas.DataFrame

Encoded data.

transform(X)

Convert categorical columns to numeric values.

Parameters:
X : pandas.DataFrame

Data to encode.

Returns:
Xt : pandas.DataFrame

Encoded data.