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]¶: 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.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`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

transform(X)[source]¶

Convert categorical columns to numeric values.

Parameters: X (pandas.DataFrame) – Data to encode.
Returns: Xt – Encoded data.
Return type: pandas.DataFrame

Pre-Processing sksurv.column.categorical_to_numeric