`skfolio.pre_selection`.SelectKExtremes#

class skfolio.pre_selection.SelectKExtremes(k=10, measure=Sharpe Ratio, highest=True)[source]#

Transformer for selecting the k best or worst assets.

Keep the k best or worst assets according to a given measure.

Parameters:

kint, default=10: Number of assets to select. If k is higher than the number of assets, all assets are selected.
measureMeasure, default=RatioMeasure.SHARPE_RATIO: The measure used to sort the assets. The default is RatioMeasure.SHARPE_RATIO.
highestbool, default=True: If this is set to True, the k assets with the highest measure are selected, otherwise it is the k lowest.

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`(X[, y])	Run the SelectKExtremes 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 SelectKExtremes transformer and get the appropriate assets.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yIgnored: Not used, present for API consistency by convention.

Returns:

selfSelectKExtremes: 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.SelectKExtremes#

`skfolio.pre_selection`.SelectKExtremes#