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)

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_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
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_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
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_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.

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.