CyclicalFeatures#

class feature_engine.creation.CyclicalFeatures(variables=None, max_values=None, drop_original=False)[source]#

CyclicalFeatures() applies cyclical transformations to numerical variables, returning 2 new features per variable, according to:

var_sin = sin(variable * (2. * pi / max_value))
var_cos = cos(variable * (2. * pi / max_value))

where max_value is the maximum value in the variable, and pi is 3.14…

CyclicalFeatures() works only with numerical variables. A list of variables to transform can be passed as an argument. Alternatively, the transformer will automatically select and transform all numerical variables.

Missing data should be imputed before using this transformer.

More details in the User Guide.

Parameters

variables: list, default=None: The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.
max_values: dict, default=None: A dictionary with the maximum value of each variable to transform. Useful when the maximum value is not present in the dataset. If None, the transformer will automatically find the maximum value of each variable.
drop_original: bool, default=False: If True, the original variables to transform will be dropped from the dataframe.

Attributes

max_values_:: The feature’s maximum values.
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.

References

Debaditya Chakraborty & Hazem Elzarka (2019), Advanced machine learning techniques for building performance simulation: a comparative analysis, Journal of Building Performance Simulation, 12:2, 193-207

Examples

>>> import pandas as pd
>>> from feature_engine.creation import CyclicalFeatures
>>> X = pd.DataFrame(dict(x= [1,4,3,3,4,2,1,2]))
>>> cf = CyclicalFeatures()
>>> cf.fit(X)
>>> cf.transform(X)
   x         x_sin         x_cos
0  1  1.000000e+00  6.123234e-17
1  4 -2.449294e-16  1.000000e+00
2  3 -1.000000e+00 -1.836970e-16
3  3 -1.000000e+00 -1.836970e-16
4  4 -2.449294e-16  1.000000e+00
5  2  1.224647e-16 -1.000000e+00
6  1  1.000000e+00  6.123234e-17
7  2  1.224647e-16 -1.000000e+00

Methods

fit:	Learns the variable’s maximum values.
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]#

Learns the maximum value of each variable.

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, default=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_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, 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_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]#

Creates new features using the cyclical transformations.

Parameters

X: Pandas DataFrame of shame = [n_samples, n_features]: The data to be transformed.

Returns

X_new: Pandas dataframe.: The original dataframe plus the additional features.