Gray

class category_encoders.gray.GrayEncoder(verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, *, base=2, handle_unknown='value', handle_missing='value', min_group_size: int | float | None = None, min_group_name: str | None = None, combine_min_nan_groups: bool | str | None = None)[source]

Gray encoding for categorical variables.

Gray encoding is a form of binary encoding where consecutive values only differ by a single bit. Hence, gray encoding only makes sense for ordinal features. This has benefits in privacy preserving data publishing.

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).
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 Gray-encoded columns back to their ordinal 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.
`gray_code`(n, n_bit)	Calculate the n-bit gray code for a value n.
`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.

References

[1]

https://en.wikipedia.org/wiki/Gray_code

[2]

Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao.

2017. PrivBayes: Private Data Release via Bayesian Networks. ACM Trans. Database Syst. 42, 4, Article 25 (October 2017)

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, cols, base)[source]

Convert Gray-encoded columns back to their ordinal integers.

The positional base-N decoding used by BaseNEncoder cannot invert a Gray code: consecutive code words differ only by a single bit and do not represent a positional value, so the inherited decoder recovers the wrong category. Instead, every Gray code word is looked up in the fitted mapping to recover the original ordinal value, mirroring the table lookup used in the forward transform.

Parameters:

XDataFrame: encoded data
colslist-like: Column names in the DataFrame that were encoded
baseint: The base of the transform. Unused, kept for signature compatibility with BaseNEncoder.basen_to_integer().

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.

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. Pass only if the estimator accepts additional params in its fit method.

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.

static gray_code(n: int, n_bit: int) → List[int][source]

Calculate the n-bit gray code for a value n.

Parameters:

n: int: Value to encode (ordinal value of a category).
n_bit: int: Number of bits to encode to.

Returns:

List[int]: gray encoding of the input value.

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 = '$UNCHANGED$') → GrayEncoder

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.

Refer to the user guide for more details and 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 = '$UNCHANGED$') → GrayEncoder

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.

Notes

If the encoder was fitted on a DataFrame, arraylike input (e.g. the numpy array emitted by the previous step of a scikit-learn pipeline) is accepted: the fitted column names are re-attached positionally, so the result matches transforming the equivalent DataFrame (GH #406). A DataFrame may additionally carry extra pass-through columns beyond the encoded ones (GH #367).