ArbitraryDiscretiser

class feature_engine.discretisation.ArbitraryDiscretiser(binning_dict, return_object=False, return_boundaries=False, precision=3, errors='ignore')

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 the key could be the variable name ‘var1’ and the value the following list: [0, 10, 100, 1000]. 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.

return_object: bool, default=False

Whether the the discrete variable should be returned as type numeric or type object. If you would like to encode the discrete variables with Feature-engine’s categorical encoders, 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.

precision: int, default=3

The precision at which to store and display the bins labels.

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 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.cut

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from feature_engine.discretisation import ArbitraryDiscretiser
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x = np.random.randint(1,100, 100)))
>>> bins = dict(x =  [0, 25, 50, 75, 100])
>>> ad = ArbitraryDiscretiser(binning_dict = bins)
>>> ad.fit(X)
>>> ad.transform(X)["x"].value_counts()
2    31
0    27
3    25
1    17
Name: x, dtype: int64

Methods

fit:	This transformer does not learn parameters.
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)

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)

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)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

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_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)

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

DataFrame ..