sksurv.preprocessing.OneHotEncoder#

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

Encode categorical features using a one-hot scheme.

This transformer only works on pandas DataFrames. It identifies columns with category or object data type as categorical features. The features are encoded using a one-hot (or dummy) encoding scheme, which creates a binary column for each category. By default, one category per feature is dropped. a column with M categories is encoded as M-1 integer columns according to the one-hot scheme.

The order of non-categorical columns is preserved. Encoded columns are inserted in place of the original column.

Parameters:

allow_drop (bool, optional, default: True) – Whether to allow dropping categorical columns that only consist of a single category.

feature_names_#

Names of categorical features that were encoded.

Type:

pandas.Index

categories_#

A dictionary mapping each categorical feature name to a list of its categories.

Type:

dict

encoded_columns_#

The full list of feature names in the transformed output.

Type:

pandas.Index

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, shape = (n_features_in_,)

__init__(*, allow_drop=True)[source]#

Methods

__init__(*[, allow_drop])

fit(X[, y])

Determine which features are categorical and should be one-hot encoded.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Transform X by one-hot encoding categorical features.
fit(X, y=None)[source]#

Determine which features are categorical and should be one-hot encoded.

Parameters:
Returns:

self – Returns the instance itself.

Return type:

object

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits the transformer to X by identifying categorical features and then returns a transformed version of X with categorical features one-hot encoded.

Parameters:
Returns:

Xt – The transformed 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_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

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_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

  • ”default”: Default output format of a transformer

  • ”pandas”: DataFrame output

  • ”polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

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)#

Transform X by one-hot encoding categorical features.

Parameters:

X (pandas.DataFrame) – The data to transform.

Returns:

Xt – The transformed data.

Return type:

pandas.DataFrame