<a id="skfolio-pre-selection-selectnonexpiring"></a>

# skfolio.pre_selection.SelectNonExpiring

<a id="skfolio.pre_selection.SelectNonExpiring"></a>

### *class* skfolio.pre_selection.SelectNonExpiring(expiration_dates=None, expiration_lookahead=None)

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_dates** *dict[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_lookahead** *pd.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.

### Methods

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

### Notes

This transformer only supports DataFrames with a DateTime index.

### Examples

```pycon
>>> 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
```

<a id="skfolio.pre_selection.SelectNonExpiring.fit"></a>

#### fit(X, y=None)

Run the SelectNonExpiring transformer and get the appropriate assets.

* **Parameters:**
  **X** *pd.DataFrame of shape (n_observations, n_assets)*
  : Returns of the assets.

  **y** *Ignored*
  : Not used, present for API consistency by convention.
* **Returns:**
  **self** *SelectNonExpiring*
  : Fitted estimator.

<a id="skfolio.pre_selection.SelectNonExpiring.fit_transform"></a>

#### 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:**
  **X** *array-like of shape (n_samples, n_features)*
  : Input samples.

  **y** *array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None*
  : Target values (None for unsupervised transformations).

  **\*\*fit_params** *dict*
  : Additional fit parameters.
    Pass only if the estimator accepts additional params in its `fit` method.
* **Returns:**
  **X_new** *ndarray array of shape (n_samples, n_features_new)*
  : Transformed array.

<a id="skfolio.pre_selection.SelectNonExpiring.get_feature_names_out"></a>

#### get_feature_names_out(input_features=None)

Mask feature names according to selected features.

* **Parameters:**
  **input_features** *array-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_out** *ndarray of str objects*
  : Transformed feature names.

<a id="skfolio.pre_selection.SelectNonExpiring.get_metadata_routing"></a>

#### get_metadata_routing()

Get metadata routing of this object.

Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.

* **Returns:**
  **routing** *MetadataRequest*
  : A `MetadataRequest` encapsulating
    routing information.

<a id="skfolio.pre_selection.SelectNonExpiring.get_params"></a>

#### get_params(deep=True)

Get parameters for this estimator.

* **Parameters:**
  **deep** *bool, default=True*
  : If True, will return the parameters for this estimator and
    contained subobjects that are estimators.
* **Returns:**
  **params** *dict*
  : Parameter names mapped to their values.

<a id="skfolio.pre_selection.SelectNonExpiring.get_support"></a>

#### get_support(indices=False)

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

* **Parameters:**
  **indices** *bool, default=False*
  : If True, the return value will be an array of integers, rather
    than a boolean mask.
* **Returns:**
  **support** *array*
  : 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.

<a id="skfolio.pre_selection.SelectNonExpiring.inverse_transform"></a>

#### inverse_transform(X)

Reverse the transformation operation.

* **Parameters:**
  **X** *array of shape [n_samples, n_selected_features]*
  : The input samples.
* **Returns:**
  **X_original** *array of shape [n_samples, n_original_features]*
  : `X` with columns of zeros inserted where features would have
    been removed by [`transform`](#skfolio.pre_selection.SelectNonExpiring.transform).

<a id="skfolio.pre_selection.SelectNonExpiring.set_output"></a>

#### set_output(\*, transform=None)

Set output container.

Refer to the user guide for more details
and 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
    <br/>
    #### Versionadded
    Added in version 1.4: `"polars"` option was added.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.

<a id="skfolio.pre_selection.SelectNonExpiring.set_params"></a>

#### 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:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.

<a id="skfolio.pre_selection.SelectNonExpiring.transform"></a>

#### transform(X)

Reduce X to the selected features.

* **Parameters:**
  **X** *array of shape [n_samples, n_features]*
  : The input samples.
* **Returns:**
  **X_r** *array of shape [n_samples, n_selected_features]*
  : The input samples with only the selected features.