WindowFeatures#

class feature_engine.timeseries.forecasting.WindowFeatures(variables=None, window=3, min_periods=None, functions='mean', periods=1, freq=None, sort_index=True, missing_values='raise', drop_original=False)[source]#

WindowFeatures adds new features to a dataframe based on window operations. Window operations are operations that perform an aggregation over a sliding partition of past values. A window feature is, in other words, a feature created after computing statistics (e.g., mean, min, max, etc.) using a window over the past data. For example, the mean value of the previous 3 months of data is a window feature. The maximum value of the previous three rows of data is another window feature.

WindowFeatures uses pandas functions rolling(), agg() and shift(). With rolling(), it creates rolling windows. With agg() it applies multiple functions within those windows. With shift() it allocates the values to the correct rows.

For supported aggregation functions, see Rolling Window Functions.

With pandas rolling() we can perform rolling operations over 1 window size at a time. WindowFeatures builds on top of pandas rolling() in that new features can be derived from multiple window sizes, and the created features will be automatically concatenated to the original dataframe.

To be compatible with WindowFeatures, the dataframe’s index must have unique values and no missing data.

WindowFeatures works only with numerical variables. You can pass a list of variables to use as input for the windows. Alternatively, WindowFeatures will automatically select all numerical variables in the training set.

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.

window: int, offset, BaseIndexer subclass, or list, default=3

Size of the moving window. If an integer, the fixed number of observations used for each window. If an offset (recommended), the time period of each window. It can also take a function. See parameter windows in pandas rolling() documentation for more details.

In addition to pandas normal input values, window can also take a list with the above specified values, in which case, features will be created for each one of the windows specified in the list.

min_periods: int, default None.

Minimum number of observations in the window required to have a value; otherwise, the result is np.nan. See parameter min_periods in pandas rolling() documentation for more details.

functions: string or list of strings, default = ‘mean’

The functions to apply within the window. Valid functions can be found here.

periods: int, list of ints, default=1

Number of periods to shift. Can be a positive integer. See param periods in pandas shift().

freq: str, list of str, default=None

Offset to use from the tseries module or time rule. See parameter freq in pandas shift().

sort_index: bool, default=True

Whether to order the index of the dataframe before creating the features.

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 used to create the window features.

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.

See also

pandas.rolling
pandas.aggregate
pandas.shift

Methods

fit:

This transformer does not learn parameters.

transform:

Add window 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.

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

The training dataset.

y: pandas Series, default=None

y is not needed in this transformer. You can pass None or y.

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.

Parameters
input_features: list, default=None

Input features. If input_features is None, then the names of all the variables in the transformed dataset (original + new variables) is returned. Alternatively, only the names for the window features derived from input_features will be returned.

Returns
feature_names_out: list

The feature names.

:rtype:py:class:~typing.List
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]#

Adds window features.

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

The data to transform.

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

The dataframe with the original plus the new variables.

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