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
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.