Quantile Encoder

class category_encoders.quantile_encoder.QuantileEncoder(verbose: int = 0, cols: list[str] = None, drop_invariant: bool = False, return_df: bool = True, handle_missing: str = 'value', handle_unknown: str = 'value', quantile: float = 0.5, m: float = 1.0)[source]

Quantile Encoding for categorical features.

This a statistically modified version of target MEstimate encoder where selected features are replaced by the statistical quantile instead of the mean. Replacing with the median is a particular case where self.quantile = 0.5. In comparison to MEstimateEncoder it has two tunable parameter m and quantile

Parameters:

verbose: int: integer indicating verbosity of the output. 0 for none.
quantile: float: float indicating statistical quantile. ´0.5´ for median.
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.

Methods

`fit`(X[, y])	Fits the encoder according to X and y.
`fit_quantile_encoding`(X, y)	Calculate the quantile encoding mapping.
`fit_transform`(X[, y])	Fit and transform using the target information.
`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.
`quantile_encode`(X_in)	Apply quantile encoding.
`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[, y, override_return_df])	Perform the transformation to new categorical data.

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/

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_quantile_encoding(X: DataFrame, y: Series) → dict[str, Series][source]

Calculate the quantile encoding mapping.

Parameters:

X: training data.
y: target data.

Returns:

mapping col-name -> series with category-label -> quantile mapping.

Fit and transform using the target information.

This also uses the target for transforming, not only for training.

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.

quantile_encode(X_in: DataFrame) → DataFrame[source]: Apply quantile encoding.

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 = '$UNCHANGED$') → QuantileEncoder

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.

Perform the transformation to new categorical data.

Some encoders behave differently on whether y is given or not. This is mainly due to regularisation in order to avoid overfitting. On training data transform should be called with y, on test data without.

Parameters:

Xarray-like, shape = [n_samples, n_features]
yarray-like, shape = [n_samples] or None
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.