RelativeFeatures

class feature_engine.creation.RelativeFeatures(variables, reference, func, fill_value=None, missing_values='ignore', drop_original=False)

RelativeFeatures() applies basic mathematical operations between a group of variables and one or more reference features. It adds the resulting features to the dataframe.

In other words, RelativeFeatures() adds, subtracts, multiplies, performs the division, true division, floor division, module or exponentiation of a group of features to / by a group of reference variables. The features resulting from these functions are added to the dataframe.

This transformer works only with numerical variables. It uses the pandas methods pd.DataFrme.add, pd.DataFrme.sub, pd.DataFrme.mul, pd.DataFrme.div, pd.DataFrme.truediv, pd.DataFrme.floordiv, pd.DataFrme.mod and pd.DataFrme.pow. Find out more in pandas documentation.

More details in the User Guide.

Parameters

variables: list

The list of numerical variables to combine with the reference variables.

reference: list

The list of reference variables that will be added, subtracted, multiplied, used as denominator for division and module, or exponent for the exponentiation.

func: list

The list of functions to be used in the transformation. The list can contain one or more of the following strings: ‘add’, ‘mul’,’sub’, ‘div’, truediv, ‘floordiv’, ‘mod’, ‘pow’.

fill_value: int, float, default=None

When dividing by zero, this value is used in place of infinity. If None, then an error will be raised when dividing by zero.

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. If 'raise' the transformer will return an error if the the datasets to fit or transform contain missing values. If 'ignore', missing data will be ignored when learning parameters or performing the transformation.

drop_original: bool, default=False

If True, the original variables to transform will be dropped from the dataframe.

Attributes

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.

Notes

Although the transformer allows us to combine any feature with any function, we recommend its use to create domain knowledge variables. Typical examples within the financial sector are:

• Ratio between income and debt to create the debt_to_income_ratio.

• Subtraction of rent from income to obtain the disposable_income.

Examples

>>> import pandas as pd
>>> from feature_engine.creation import RelativeFeatures
>>> X = pd.DataFrame(dict(x1 = [1,2,3], x2 = [4,5,6], x3 = [3,4,5]))
>>> rf = RelativeFeatures(variables = ["x1","x2"],
>>>                     reference = ["x3"],
>>>                     func = ["div"])
>>> rf.fit(X)
>>> rf.transform(X)
   x1  x2  x3  x1_div_x3  x2_div_x3
0   1   4   3   0.333333   1.333333
1   2   5   4   0.500000   1.250000
2   3   6   5   0.600000   1.200000

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:	Create new features.

fit(X, y=None)

This transformer does not learn parameters.

Parameters

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

The training input samples.

y: pandas Series, or np.array. Defaults to None.

It 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)

Add new features.

Parameters

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

The data to transform.

Returns

X_new: Pandas dataframe

The input dataframe plus the new variables.

rtype

DataFrame ..