MathematicalCombination

class feature_engine.creation.MathematicalCombination(**kwargs)[source]

DEPRECATED: MathematicalCombination() is deprecated in version 1.3 and will be removed in version 1.5. Use MathFeatures() instead.

MathematicalCombination() applies basic mathematical operations to multiple features, returning one or more additional features as a result. That is, it sums, multiplies, takes the average, maximum, minimum or standard deviation of a group of variables, and returns the result into new variables.

Note that if some of the variables to combine have missing data and you set missing_values='ignore', the value will be ignored in the computation. To be clear, if variables A, B and C, have values 10, 20 and NA, and we perform the sum, the result will be A + B = 30.

More details in the User Guide.

Parameters
variables_to_combine: list

The list of numerical variables to combine.

math_operations: list, default=None

The list of basic math operations to be used to create the new features.

If None, all of [‘sum’, ‘prod’, ‘mean’, ‘std’, ‘max’, ‘min’] will be performed. Alternatively, you can enter the list of operations to carry out. Each operation should be a string and must be one of the elements in ['sum', 'prod', 'mean', 'std', 'max', 'min'].

Each operation will result in a new variable that will be added to the transformed dataset.

new_variables_names: list, default=None

Names of the new variables. If passing a list with the names for the new features (recommended), you must enter one name for each mathematical transformation indicated in the math_operations parameter. The name of the new variables should coincide with the order in which the mathematical operations are initialised in the transformer.

If new_variable_names = None, the transformer will assign an arbitrary name to the newly created features starting by the name of the mathematical operation, followed by the variables combined separated by -.

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
combination_dict_:

Dictionary containing the mathematical operation to new variable name pairs.

math_operations_:

List with the mathematical operations to be applied to the variables_to_combine.

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 in essence allows us to combine any feature with any of the allowed mathematical operations, its used is intended mostly for the creation of new features based on some domain knowledge. Typical examples within the financial sector are:

  • Sum debt across financial products, i.e., credit cards, to obtain the total debt.

  • Take the average payments to various financial products per month.

  • Find the Minimum payment done at any one month.

In insurance, we can sum the damage to various parts of a car to obtain the total damage.

Methods

fit:

This transformer does not learn parameters.

transform:

Create new features.

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.

fit(X, y=None)[source]

This transformer does not learn parameters.

Perform dataframe checks. Creates dictionary of operation to new feature name pairs.

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

The training input samples. Can be the entire dataframe, not just the variables to transform.

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)[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]

Combine the variables with the mathematical operations.

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

The data to transform.

Returns
X_new: Pandas dataframe, shape = [n_samples, n_features + n_operations]

The dataframe with the original variables plus the new variables.

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