sksurv.preprocessing.OneHotEncoder

class sksurv.preprocessing.OneHotEncoder(allow_drop=True)[source]

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.

feature_names_

List of encoded columns.

Type

pandas.Index

categories_

Categories of encoded columns.

Type

dict

encoded_columns_

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

Type

list

__init__(allow_drop=True)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([allow_drop])

Initialize self.

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)[source]

Retrieve categorical columns.

Parameters
  • X (pandas.DataFrame) – Data to encode.

  • y – Ignored. For compatibility with Pipeline.

Returns

self – Returns self

Return type

object

fit_transform(X, y=None, **fit_params)[source]

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 – Encoded data.

Return type

pandas.DataFrame

transform(X)[source]

Convert categorical columns to numeric values.

Parameters

X (pandas.DataFrame) – Data to encode.

Returns

Xt – Encoded data.

Return type

pandas.DataFrame