One Hot

class category_encoders.one_hot.OneHotEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', use_cat_names=False)

Onehot (or dummy) coding for categorical features, produces one feature per category, each binary.

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).

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’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.

‘error’ will raise a ValueError at transform time if there are new categories. ‘return_nan’ will encode a new value as np.nan in every dummy column. ‘value’ will encode a new value as 0 in every dummy column. ‘indicator’ will add an additional dummy column (in both training and test data).

handle_missing: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.

‘error’ will raise a ValueError if missings are encountered. ‘return_nan’ will encode a missing value as np.nan in every dummy column. ‘value’ will encode a missing value as 0 in every dummy column. ‘indicator’ will treat missingness as its own category, adding an additional dummy column (whether there are missing values in the training set or not). ‘ignore’ will encode missing values as 0 in every dummy column, NOT adding an additional category.

References

[1]

Contrast Coding Systems for Categorical Variables, from

https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/

[2]

Gregory Carey (2003). Coding Categorical Variables, from

http://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf

Attributes:

category_mapping

Methods

fit(X[, y])	Fits the encoder according to X and y.
fit_transform(X[, y])	Fit to data, then transform it.
get_dummies(X_in)	Convert numerical variable into dummy variables
get_feature_names_in()	Returns the names of all input columns present when fitting.
get_feature_names_out([input_features])	Returns 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.
reverse_dummies(X, mapping)	Convert dummy variable into numerical variables
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.

generate_mapping
get_feature_names

Parameters:

verbose: int

integer indicating verbosity of output. 0 for none.

cols: list

a list of columns to encode, if None, all string and categorical 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 and inverse transform (otherwise it will be a numpy array).

handle_missing: str

how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat nans as a countable category at fit time.

handle_unknown: str, int or dict of {columnoption, …}.

how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses nan behaviour specified at fit time. Passing an int will fill with this int value.

kwargs: dict.

additional encoder specific parameters like regularisation.

Attributes:

category_mapping

Methods

fit(X[, y])	Fits the encoder according to X and y.
fit_transform(X[, y])	Fit to data, then transform it.
get_dummies(X_in)	Convert numerical variable into dummy variables
get_feature_names_in()	Returns the names of all input columns present when fitting.
get_feature_names_out([input_features])	Returns 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.
reverse_dummies(X, mapping)	Convert dummy variable into numerical variables
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.

generate_mapping
get_feature_names

fit(X, y=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.

get_dummies(X_in)

Convert numerical variable into dummy variables

Parameters:

X_in: DataFrame

Returns:

dummiesDataFrame

get_feature_names_in() → List[str]

Returns 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

Returns 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

reverse_dummies(X, mapping)

Convert dummy variable into numerical variables

Parameters:

XDataFrame

mapping: list-like

Contains mappings of column to be transformed to it’s new columns and value represented

Returns:

numerical: DataFrame

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

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.

New 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”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• None: Transform configuration is unchanged

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 = '$UNCHANGED$') → OneHotEncoder

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.

New 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, override_return_df=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.