Summary Encoder

class category_encoders.quantile_encoder.SummaryEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', quantiles=(0.25, 0.75), m=1.0)[source]

Summary Encoding for categorical features.

It’s an encoder designed for creating richer representations by applying quantile encoding for a set of quantiles.

Parameters:

verbose: int: integer indicating verbosity of the output. 0 for none.
quantiles: list: list of floats indicating the statistical quantiles. Each element represent a column
m: float: this is the “m” in the m-probability estimate. Higher value of m results into stronger shrinking. M is non-negative. 0 for no smoothing.
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_missing: str: options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.
handle_unknown: str: options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target quantile.

References

[1]

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems, https://link.springer.com/chapter/10.1007%2F978-3-030-85529-1_14

[2]

A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems, equation 7, from https://dl.acm.org/citation.cfm?id=507538

[3]

On estimating probabilities in tree pruning, equation 1, from https://link.springer.com/chapter/10.1007/BFb0017010

[4]

Additive smoothing, from https://en.wikipedia.org/wiki/Additive_smoothing#Generalized_to_the_case_of_known_incidence_rates

[5]

Target encoding done the right way https://maxhalford.github.io/blog/target-encoding/

Methods

`fit`(X, y)	Fits the encoder according to X and y by fitting the individual encoders.
`fit_transform`(X[, y])	Encoders that utilize the target must make sure that the training data are transformed with:
`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.
`set_params`(**params)	Set the parameters of this estimator.
`set_transform_request`(*[, override_return_df])	Request metadata passed to the `transform` method.

get_feature_names
transform

fit(X, y)[source]

Fits the encoder according to X and y by fitting the individual encoders.

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)

Encoders that utilize the target must make sure that the training data are transformed with:: transform(X, y)
and not with:: transform(X)

get_feature_names_in() → List[str][source]: 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[source]

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 list 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.

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 = '$UNCHANGED$') → SummaryEncoder

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.