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')[source]

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])	Configure whether metadata should be requested to be 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])	Configure whether metadata should be requested to be passed to the `transform` method.
`transform`(X[, override_return_df])	Perform the transformation to new categorical data.

basen_encode(X_in: DataFrame, cols=None)[source]

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)[source]

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[source]

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)[source]: The lambda body to transform the column values.

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]][source]

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)[source]

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][source]

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

Configure whether metadata should be requested to be passed to the inverse_transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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.