EqualFrequencyDiscretiser#

class feature_engine.discretisation.EqualFrequencyDiscretiser(variables=None, q=10, return_object=False, return_boundaries=False)[source]#

The EqualFrequencyDiscretiser() divides continuous numerical variables into contiguous equal frequency intervals, that is, intervals that contain approximately the same proportion of observations.

The EqualFrequencyDiscretiser() works only with numerical variables. A list of variables can be passed as argument. Alternatively, the discretiser will automatically select and transform all numerical variables.

The EqualFrequencyDiscretiser() first finds the boundaries for the intervals or quantiles for each variable. Then it transforms the variables, that is, it sorts the values into the intervals.

More details in the User Guide.

Parameters
variables: list, default=None

The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.

q: int, default=10

Desired number of equal frequency intervals / bins.

return_object: bool, default=False

Whether the the discrete variable should be returned as numeric or as object. If you would like to proceed with the engineering of the variable as if it was categorical, use True. Alternatively, keep the default to False.

return_boundaries: bool, default=False

Whether the output should be the interval boundaries. If True, it returns the interval boundaries. If False, it returns integers.

Attributes
binner_dict_:

Dictionary with the interval limits per variable.

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

See also

pandas.qcut
sklearn.preprocessing.KBinsDiscretizer

References

1

Kotsiantis and Pintelas, “Data preprocessing for supervised leaning,” International Journal of Computer Science, vol. 1, pp. 111 117, 2006.

2

Dong. “Beating Kaggle the easy way”. Master Thesis. https://www.ke.tu-darmstadt.de/lehre/arbeiten/studien/2015/Dong_Ying.pdf

Methods

fit:

Find the interval limits.

fit_transform:

Fit to data, then transform it.

get_feature_names_out:

Get output feature names for transformation.

get_params:

Get parameters for this estimator.

set_params:

Set the parameters of this estimator.

transform:

Sort continuous variable values into the intervals.

fit(X, y=None)[source]#

Learn the limits of the equal frequency intervals.

Parameters
X: pandas dataframe of shape = [n_samples, n_features]

The training dataset. Can be the entire dataframe, not just the variables to be transformed.

y: None

y is not needed in this encoder. You can pass y or None.

fit_transform(X, y=None, **fit_params)[source]#

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.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters
input_features: str, list, default=None

If None, then the names of all the variables in the transformed dataset is returned. If list with feature names, the features in the list will be returned. This parameter exists mostly for compatibility with the Scikit-learn Pipeline.

Returns
feature_names_out: list

The feature names.

:rtype:py:class:~typing.List[Union[str, int]]
get_params(deep=True)[source]#

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)[source]#

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)[source]#

Sort the variable values into the intervals.

Parameters
X: pandas dataframe of shape = [n_samples, n_features]

The data to transform.

Returns
X_new: pandas dataframe of shape = [n_samples, n_features]

The transformed data with the discrete variables.

:rtype:py:class:~pandas.core.frame.DataFrame