MathFeatures#
- class feature_engine.creation.MathFeatures(variables, func, new_variables_names=None, missing_values='raise', drop_original=False)[source]#
MathFeatures(() applies functions across multiple features returning one or more additional features as a result. It uses
pandas.agg()
to create the features, settingaxis=1
.For supported aggregation functions, see pandas documentation.
Note that if some of the variables have missing data and
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: list
The list of input variables. Variables must be numerical and there must be at least 2 different variables in the list.
- func: function, string, list
Functions to use for aggregating the data. Same functionality as parameter
func
inpandas.agg()
. If a function, it must either work when passed a DataFrame or when passed to DataFrame.apply. Accepted combinations are:function
string function name
list of functions and/or function names, e.g. [np.sum, ‘mean’]
Each function 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 names (recommended), enter one name per function. If None, the transformer will assign arbitrary names, starting with the function and followed by the variables 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 tofit
ortransform
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 features with any functions, we recommend using it to create features based on domain knowledge. Typical examples in finance are:
Sum debt across financial products, i.e., credit cards, to obtain the total debt.
Take the average payments to various financial products.
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.
Examples
>>> import pandas as pd >>> from feature_engine.creation import MathFeatures >>> X = pd.DataFrame(dict(x1 = [1,2,3], x2 = [4,5,6])) >>> mf = MathFeatures(variables = ["x1","x2"], func = "sum") >>> mf.fit(X) >>> mf.transform(X) x1 x2 sum_x1_x2 0 1 4 5 1 2 5 7 2 3 6 9
>>> mf = MathFeatures(variables = ["x1","x2"], func = "prod") >>> mf.fit(X) >>> mf.transform(X) x1 x2 prod_x1_x2 0 1 4 4 1 2 5 10 2 3 6 18
>>> mf = MathFeatures(variables = ["x1","x2"], func = "mean") >>> mf.fit(X) >>> mf.transform(X)) x1 x2 mean_x1_x2 0 1 4 2.5 1 2 5 3.5 2 3 6 4.5
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)[source]#
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)[source]#
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and 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_feature_names_out(input_features=None)[source]#
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
, thenfeature_names_in_
is used as feature names in.If an array or list, then
input_features
must matchfeature_names_in_
.
- Returns
- feature_names_out: list
Transformed feature names.
- 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.