RankHotEncoder

class category_encoders.rankhot.RankHotEncoder(verbose: int = 0, cols: list[str] = None, drop_invariant: bool = False, return_df: bool = True, handle_missing: str = 'value', handle_unknown: str = 'value', use_cat_names: bool = False)

Rank Hot Encoder.

The rank-hot encoder is similar to a one-hot encoder, except every feature up to and including the current rank is hot. This is also called thermometer 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.

use_cat_names: bool

if True, category values will be included in the encoded column names.

Since this can result in duplicate column names, duplicates are suffixed with ‘#’ symbol until a unique name is generated.

If False, category indices will be used instead of the category values.

handle_unknown: str

options are ‘error’, ‘value’, ‘return_nan’. The default is ‘value’. ‘value’: If an unknown label occurrs, it is represented as 0 array. ‘error’: If an unknown label occurrs, error message is displayed. ‘return_nan’: If an unknown label occurrs, np.nan is returned in all columns.

handle_missing: str

options are ‘error’, ‘value’ and ‘return_nan’. The default is ‘value’. Missing value also considered as unknown value in the final data set.

Methods

fit(X[, y])	Fits the encoder according to X and y.
fit_transform(X[, y])	Fit to data, then transform it.
generate_mapping()	Generate the mapping for rankhot encoding.
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)	Inverse transformation.
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.

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_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.

generate_mapping() → list[dict[str, str | DataFrame]]

Generate the mapping for rankhot encoding.

Returns:

List of dict containing colnames and their respective encoding.

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: DataFrame) → DataFrame

Inverse transformation.

This takes encoded data and gives back non-encoded data.

Parameters:

X_in: data frame with rank-hot-encoded data.

Returns:

non-encoded data as a data frame.

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

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 = '$UNCHANGED$') → RankHotEncoder

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.