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