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

# skfolio.pre_selection.SelectComplete

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

### *class* skfolio.pre_selection.SelectComplete(drop_assets_with_internal_nan=False)

Transformer to select assets with complete data across the entire observation
period.

This transformer removes assets (columns) that have missing values (NaNs) at the
beginning or end of the period.

This transformer is especially useful for financial datasets where assets
(e.g., stocks, bonds) may have data gaps due to late inception (assets that started
trading later), early expiry or default (assets that stopped trading before the
end of the period).

If missing values are not at the beginning or end but occur between non-missing
values, the asset is not removed unless `drop_assets_with_internal_nan` is set to
`True`.

* **Parameters:**
  **drop_assets_with_internal_nan** *bool, default=False*
  : If set to True, assets with missing values (NaNs) that appear between
    non-missing values (i.e., internal NaNs) will also be removed. By default,
    only assets with leading or trailing NaNs are 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.SelectComplete.fit)(X[, y])                             | Run the SelectComplete transformer and get the appropriate assets.   |
|------------------------------------------------------------------------------------------|----------------------------------------------------------------------|
| [`fit_transform`](#skfolio.pre_selection.SelectComplete.fit_transform)(X[, y])                   | Fit to data, then transform it.                                      |
| [`get_feature_names_out`](#skfolio.pre_selection.SelectComplete.get_feature_names_out)([input_features]) | Mask feature names according to selected features.                   |
| [`get_metadata_routing`](#skfolio.pre_selection.SelectComplete.get_metadata_routing)()                  | Get metadata routing of this object.                                 |
| [`get_params`](#skfolio.pre_selection.SelectComplete.get_params)([deep])                      | Get parameters for this estimator.                                   |
| [`get_support`](#skfolio.pre_selection.SelectComplete.get_support)([indices])                  | Get a mask, or integer index, of the features selected.              |
| [`inverse_transform`](#skfolio.pre_selection.SelectComplete.inverse_transform)(X)                    | Reverse the transformation operation.                                |
| [`set_output`](#skfolio.pre_selection.SelectComplete.set_output)(\*[, transform])             | Set output container.                                                |
| [`set_params`](#skfolio.pre_selection.SelectComplete.set_params)(\*\*params)                  | Set the parameters of this estimator.                                |
| [`transform`](#skfolio.pre_selection.SelectComplete.transform)(X)                            | Reduce X to the selected features.                                   |

### Examples

```pycon
>>> import numpy as np
>>> import pandas as pd
>>> from skfolio.pre_selection import SelectComplete
>>> X = pd.DataFrame({
...     'asset1': [np.nan, np.nan, 2, 3, 4],    # Starts late (inception)
...     'asset2': [1, 2, 3, 4, 5],         # Complete data
...     'asset3': [1, 2, 3, np.nan, 5], # Missing values within data
...     'asset4': [1, 2, 3, 4, np.nan]      # Ends early (expiration)
... })
>>> selector = SelectComplete()
>>> selector.fit_transform(X)
 array([[ 1.,  1.],
        [ 2.,  2.],
        [ 3.,  3.],
        [ 4., nan],
        [ 5.,  5.]])
>>> selector = SelectComplete(drop_assets_with_internal_nan=True)
>>> selector.fit_transform(X)
 array([[1.],
       [2.],
       [3.],
       [4.],
       [5.]])
```

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

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

Run the SelectComplete transformer and get the appropriate assets.

* **Parameters:**
  **X** *array-like of shape (n_observations, n_assets)*
  : Returns of the assets.

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

<a id="skfolio.pre_selection.SelectComplete.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.SelectComplete.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.SelectComplete.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.SelectComplete.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.SelectComplete.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.SelectComplete.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.SelectComplete.transform).

<a id="skfolio.pre_selection.SelectComplete.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.SelectComplete.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.SelectComplete.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.