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_,)
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