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

n_features_in_#

Number of features seen during fit.

Type

int

feature_names_in_#

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type

ndarray of shape (n_features_in_,)

__init__(allow_drop=True)[source]#

Methods

__init__([allow_drop])

fit(X[, y])

Retrieve categorical columns.

fit_transform(X[, y])

Convert categorical columns to numeric values.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

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

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters

input_features (array-like of str or None, default=None) –

Input features.

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

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns

feature_names_out – Transformed feature names.

Return type

ndarray of str objects

get_params(deep=True)#

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

transform(X)[source]#

Convert categorical columns to numeric values.

Parameters

X (pandas.DataFrame) – Data to encode.

Returns

Xt – Encoded data.

Return type

pandas.DataFrame