`skfolio.pre_selection`.SelectNonExpiring#

class skfolio.pre_selection.SelectNonExpiring(expiration_dates=None, expiration_lookahead=None)[source]#

Transformer to select assets that do not expire within a specified lookahead period after the end of the observation period.

This transformer removes assets (columns) that have expiration dates within a given lookahead period from the end of the dataset, allowing only assets that remain active beyond this lookahead period to be selected.

This is useful when an exit strategy is needed before asset expiration, such as for bonds or options with known end dates, or when applying WalkForward cross-validation. It ensures that assets expiring during the test period are excluded, so that only live assets are included in each training and test period.

Parameters:

expiration_datesdict[str, dt.datetime | pd.Timestamp], optional: Dictionary with asset names as keys and expiration dates as values. Used to check if each asset expires within the date offset. Assets with no expiration date will be retained by default.
expiration_lookaheadpd.offsets.BaseOffset | dt.timedelta, optional: The lookahead period after the end of the dataset within which assets with expiration dates will be removed.

Attributes:

to_keep_ndarray of shape (n_assets, ): Boolean array indicating which assets are remaining.
n_features_in_int: Number of assets seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

Notes

This transformer only supports DataFrames with a DateTime index.

Examples

>>> import pandas as pd
>>> import datetime as dt
>>> from sklearn import set_config
>>> set_config(transform_output="pandas")
>>> X = pd.DataFrame(
...    {
...        'asset1': [1, 2, 3, 4],
...        'asset2': [2, 3, 4, 5],
...        'asset3': [3, 4, 5, 6],
...        'asset4': [4, 5, 6, 7]
...    }, index=pd.date_range("2023-01-01", periods=4, freq="D")
...)
>>> expiration_dates = {
...    'asset1': pd.Timestamp("2023-01-10"),
...    'asset2': pd.Timestamp("2023-01-02"),
...    'asset3': pd.Timestamp("2023-01-06"),
...    'asset4': dt.datetime(2023, 5, 1)
... }
>>> selector = SelectNonExpiring(
...    expiration_dates=expiration_dates,
...    expiration_lookahead=pd.DateOffset(days=5)
...)
>>> selector.fit_transform(X)
           asset1  asset4
2023-01-01      1      4
2023-01-02      2      5
2023-01-03      3      6
2023-01-04      4      7

Methods

`fit`(X[, y])	Run the SelectNonExpiring transformer and get the appropriate assets.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Mask feature names according to selected features.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_support`([indices])	Get a mask, or integer index, of the features selected.
`inverse_transform`(X)	Reverse the transformation operation.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Reduce X to the selected features.

fit(X, y=None)[source]#

Run the SelectNonExpiring transformer and get the appropriate assets.

Parameters:

Xpd.DataFrame of shape (n_observations, n_assets): Returns of the assets.
yIgnored: Not used, present for API consistency by convention.

Returns:

selfSelectNonExpiring: Fitted estimator.

fit_transform(X, y=None, **fit_params)#

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)#

Mask feature names according to selected features.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].
If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

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.

get_support(indices=False)#

Get a mask, or integer index, of the features selected.

Parameters:

indicesbool, default=False: If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

supportarray: An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)#

Reverse the transformation operation.

Parameters:

Xarray of shape [n_samples, n_selected_features]: The input samples.

Returns:

X_rarray of shape [n_samples, n_original_features]: X with columns of zeros inserted where features would have been removed by transform.

set_output(*, transform=None)#

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)#

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)#

Reduce X to the selected features.

Parameters:

Xarray of shape [n_samples, n_features]: The input samples.

Returns:

X_rarray of shape [n_samples, n_selected_features]: The input samples with only the selected features.

skfolio.pre_selection.SelectNonExpiring#

`skfolio.pre_selection`.SelectNonExpiring#