Target Encoder

class category_encoders.target_encoder.TargetEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', min_samples_leaf=20, smoothing=10, hierarchy=None)[source]

Target encoding for categorical features.

Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.

For the case of categorical target: features are replaced with a blend of posterior probability of the target given particular categorical value and the prior probability of the target over all the training data.

For the case of continuous target: features are replaced with a blend of the expected value of the target given particular categorical value and the expected value of the target over all the training data.

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_missing: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.

handle_unknown: str

options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which returns the target mean.

min_samples_leaf: int

For regularization the weighted average between category mean and global mean is taken. The weight is an S-shaped curve between 0 and 1 with the number of samples for a category on the x-axis. The curve reaches 0.5 at min_samples_leaf. (parameter k in the original paper)

smoothing: float

smoothing effect to balance categorical average vs prior. Higher value means stronger regularization. The value must be strictly bigger than 0. Higher values mean a flatter S-curve (see min_samples_leaf).

hierarchy: dict or dataframe

A dictionary or a dataframe to define the hierarchy for mapping.

If a dictionary, this contains a dict of columns to map into hierarchies. Dictionary key(s) should be the column name from X which requires mapping. For multiple hierarchical maps, this should be a dictionary of dictionaries.

If dataframe: a dataframe defining columns to be used for the hierarchies. Column names must take the form:: HIER_colA_1, … HIER_colA_N, HIER_colB_1, … HIER_colB_M, …

where [colA, colB, …] are given columns in cols list. 1:N and 1:M define the hierarchy for each column where 1 is the highest hierarchy (top of the tree). A single column or multiple can be used, as relevant.

Examples

——-

>>> from category_encoders import *

>>> import pandas as pd

>>> from sklearn.datasets import fetch_openml

>>> display_cols = [“Id”, “MSSubClass”, “MSZoning”, “LotFrontage”, “YearBuilt”, “Heating”, “CentralAir”]

>>> bunch = fetch_openml(name=”house_prices”, as_frame=True)

>>> y = bunch.target > 200000

>>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)[display_cols]

>>> enc = TargetEncoder(cols=[‘CentralAir’, ‘Heating’], min_samples_leaf=20, smoothing=10).fit(X, y)

>>> numeric_dataset = enc.transform(X)

>>> print(numeric_dataset.info())

<class ‘pandas.core.frame.DataFrame’>

RangeIndex: 1460 entries, 0 to 1459

Data columns (total 7 columns):

# Column Non-Null Count Dtype

— —— ————– —–

0 Id 1460 non-null float64 1 MSSubClass 1460 non-null float64 2 MSZoning 1460 non-null object 3 LotFrontage 1201 non-null float64 4 YearBuilt 1460 non-null float64 5 Heating 1460 non-null float64 6 CentralAir 1460 non-null float64

dtypes: float64(6), object(1)

memory usage: 80.0+ KB

None

>>> from category_encoders.datasets import load_compass

>>> X, y = load_compass()

>>> hierarchical_map = {‘compass’: {‘N’: (‘N’, ‘NE’), ‘S’: (‘S’, ‘SE’), ‘W’: ‘W’}}

>>> enc = TargetEncoder(verbose=1, smoothing=2, min_samples_leaf=2, hierarchy=hierarchical_map, cols=[‘compass’]).fit(X.loc[:,[‘compass’]], y)

>>> hierarchy_dataset = enc.transform(X.loc[:,[‘compass’]])

>>> print(hierarchy_dataset[‘compass’].values)

[0.62263617 0.62263617 0.90382995 0.90382995 0.90382995 0.17660024

0.17660024 0.46051953 0.46051953 0.46051953 0.46051953 0.40332791 0.40332791 0.40332791 0.40332791 0.40332791]

>>> X, y = load_postcodes(‘binary’)

>>> cols = [‘postcode’]

>>> HIER_cols = [‘HIER_postcode_1’,’HIER_postcode_2’,’HIER_postcode_3’,’HIER_postcode_4’]

>>> enc = TargetEncoder(verbose=1, smoothing=2, min_samples_leaf=2, hierarchy=X[HIER_cols], cols=[‘postcode’]).fit(X[‘postcode’], y)

>>> hierarchy_dataset = enc.transform(X[‘postcode’])

>>> print(hierarchy_dataset.loc[0:10, ‘postcode’].values)

[0.75063473 0.90208756 0.88328833 0.77041254 0.68891504 0.85012847

0.76772574 0.88742357 0.7933824 0.63776756 0.9019973 ]

References

[1]

A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems, from

https://dl.acm.org/citation.cfm?id=507538

Methods

`fit`(X[, y])	Fits the encoder according to X and y.
`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_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[, y, override_return_df])	Perform the transformation to new categorical data.

fit_target_encoding
get_feature_names
target_encode

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.

Methods

`fit`(X[, y])	Fits the encoder according to X and y.
`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_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[, y, override_return_df])	Perform the transformation to new categorical data.

fit_target_encoding
get_feature_names
target_encode

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)

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]: 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.

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 = '$UNCHANGED$') → TargetEncoder

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, y=None, override_return_df=False)

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.