BaseN

class category_encoders.basen.BaseNEncoder(verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, base=2, handle_unknown='value', handle_missing='value')

Base-N encoder encodes the categories into arrays of their base-N representation.

A base of 1 is equivalent to one-hot encoding (not really base-1, but useful), a base of 2 is equivalent to binary encoding. N=number of actual categories is equivalent to vanilla ordinal encoding.

Parameters:

verbose: int

integer indicating verbosity of the output. 0 for none.

cols: list

a list of columns to encode, if None, all string columns will be encoded.

drop_invariant: bool

boolean for whether or not to drop columns with 0 variance.

return_df: bool

boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).

base: int

when the downstream model copes well with nonlinearities (like decision tree), use higher base.

handle_unknown: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’. Warning: if indicator is used, an extra column will be added in if the transform matrix has unknown categories. This can cause unexpected changes in dimension in some cases.

handle_missing: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’. Warning: if indicator is used, an extra column will be added in if the transform matrix has nan values. This can cause unexpected changes in dimension in some cases.

Methods

basen_encode(X_in[, cols])	Basen encoding encodes the integers as basen code with one column per digit.
basen_to_integer(X, cols, base)	Convert basen code as integers.
calc_required_digits(values)	Figure out how many digits we need to represent the classes present.
col_transform(col, digits)	The lambda body to transform the column values.
fit(X[, y])	Fits the encoder according to X and y.
fit_base_n_encoding()	Fit the base n encoder.
fit_transform(X[, y])	Fit to data, then transform it.
get_feature_names()	Deprecated method to get feature names.
get_feature_names_in()	Get the names of all input columns present when fitting.
get_feature_names_out([input_features])	Get the names of all transformed / added columns.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
inverse_transform(X_in)	Perform the inverse transformation to encoded data.
number_to_base(n, b, limit)	Convert number to base n representation (as list of digits).
set_inverse_transform_request(*[, X_in])	Request metadata passed to the inverse_transform method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, override_return_df])	Request metadata passed to the transform method.
transform(X[, override_return_df])	Perform the transformation to new categorical data.

basen_encode(X_in: DataFrame, cols=None)

Basen encoding encodes the integers as basen code with one column per digit.

Parameters:

X_in: DataFrame

cols: list-like, default None

Column names in the DataFrame to be encoded

Returns:

dummiesDataFrame

basen_to_integer(X: DataFrame, cols, base)

Convert basen code as integers.

Parameters:

XDataFrame

encoded data

colslist-like

Column names in the DataFrame that be encoded

baseint

The base of transform

Returns:

numerical: DataFrame

calc_required_digits(values: list) → int

Figure out how many digits we need to represent the classes present.

Parameters:

values: list

list of values.

Returns:

int

number of digits necessary for encoding.

col_transform(col, digits)

The lambda body to transform the column values.

fit(X: ndarray | DataFrame | list | generic | csr_matrix, y: list | Series | ndarray | tuple | DataFrame | None = None, **kwargs)

Fits the encoder according to X and y.

Parameters:

Xarray-like, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape = [n_samples]

Target values.

Returns:

selfencoder

Returns self.

fit_base_n_encoding() → list[dict[str, Any]]

Fit the base n encoder.

Returns:

list[dict[str, Any]]

List containing encoding mappings for each column.

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names() → ndarray

Deprecated method to get feature names. Use get_feature_names_out instead.

get_feature_names_in() → ndarray

Get the names of all input columns present when fitting.

These columns are necessary for the transform step.

get_feature_names_out(input_features=None) → ndarray

Get the names of all transformed / added columns.

Note that in sklearn the get_feature_names_out function takes the feature_names_in as an argument and determines the output feature names using the input. A fit is usually not necessary and if so a NotFittedError is raised. We just require a fit all the time and return the fitted output columns.

Returns:

feature_names: np.ndarray

A numpy array with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(X_in)

Perform the inverse transformation to encoded data.

Parameters:

X_inarray-like, shape = [n_samples, n_features]

Returns:

p: array, the same size of X_in

static number_to_base(n: int, b: int, limit: int) → list[int]

Convert number to base n representation (as list of digits).

The list will be of length limit.

Parameters:

n: int

number to convert

b: int

base

limit: int

length of representation.

Returns:

list[int]

base n representation as list of length limit containing the digits.

set_inverse_transform_request(*, X_in: bool | None | str = '$UNCHANGED$') → BaseNEncoder

Request metadata passed to the inverse_transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

X_instr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_in parameter in inverse_transform.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py 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:

selfestimator instance

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_transform_request(*, override_return_df: bool | None | str = '$UNCHANGED$') → BaseNEncoder

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

override_return_dfstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for override_return_df parameter in transform.

Returns:

selfobject

The updated object.

transform(X: ndarray | DataFrame | list | generic | csr_matrix, override_return_df: bool = False)

Perform the transformation to new categorical data.

Parameters:

Xarray-like, shape = [n_samples, n_features]

override_return_dfbool

override self.return_df to force to return a data frame

Returns:

parray or DataFrame, shape = [n_samples, n_features_out]

Transformed values with encoding applied.