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 that will be discretised. If None, the EqualFrequencyDiscretiser() will 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 variables that will be discretised.
- n_features_in_:
The number of features in the train set used in fit.
See also
pandas.qcutsklearn.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.
transform:
Sort continuous variable values into the intervals.
fit_transform:
Fit to the data, then transform it.
- 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
Xandywith optional parametersfit_paramsand returns a transformed version ofX.- 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_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