ArbitraryDiscretiser

class feature_engine.discretisation.ArbitraryDiscretiser(binning_dict, return_object=False, return_boundaries=False, errors='ignore')[source]

The ArbitraryDiscretiser() divides numerical variables into intervals which limits are determined by the user. Thus, it works only with numerical variables.

You need to enter a dictionary with variable names as keys, and a list with the limits of the intervals as values. For example {'var1':[0, 10, 100, 1000], 'var2':[5, 10, 15, 20]}.

The ArbitraryDiscretiser() will then sort var1 values into the intervals 0-10, 10-100, 100-1000, and var2 into 5-10, 10-15 and 15-20. Similar to pandas.cut.

More details in the User Guide.

Parameters
binning_dict: dict

The dictionary with the variable to interval limits pairs. A valid dictionary looks like this: binning_dict = {'var1':[0, 10, 100, 1000], 'var2':[5, 10, 15, 20]}

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, that is the bins, should be the interval boundaries. If True, it returns the interval boundaries. If False, it returns integers.

errors: string, default=’ignore’

Indicates what to do when a value is outside the limits indicated in the ‘binning_dict’. If ‘raise’, the transformation will raise an error. If ‘ignore’, values outside the limits are returned as NaN and a warning will be raised instead.

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.cut

Methods

fit:

This transformer does not learn any parameter.

transform:

Sort variable values into the intervals.

fit_transform:

Fit to the data, then transform it.

fit(X, y=None)[source]

This transformer does not learn any parameter.

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 transformer. 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_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 dataframe to be transformed.

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

The transformed data with the discrete variables.

rtype

DataFrame ..