skfolio.pre_selection.SelectNonDominated

class skfolio.pre_selection.SelectNonDominated(min_n_assets=None, threshold=-0.5, fitness_measures=None)

Transformer for selecting non dominated assets.

Pre-selection based on the Assets Preselection Process 2 [1].

Good single asset (for example with high return and low risk) is likely to contribute to the final optimized portfolio. Each asset is considered as a portfolio and these assets are ranked using the non-domination sorting method. The selection is based on the ranks assigned to each asset based on their fitness until the number of selected assets reaches the user-defined number.

Considering only the fitness of individual asset is insufficient because a pair of negatively correlated assets has the potential to reduce the risk. Therefore, negatively correlated pairs of assets are also considered.

Parameters:

min_n_assetsint, optional

The minimum number of assets to select. If min_n_assets is reached before the end of the current non-dominated front, we return the remaining assets of this front. This is because all assets in the same front have same rank. The default (None) is to select the first front.

thresholdfloat, default=0.0

Asset pair with a correlation below this threshold are included in the non-domination sorting. The default value is 0.0.

fitness_measureslist[Measure], optional

A list of measure used to compute the portfolio fitness. The fitness is used to compare portfolios in terms of domination, compute the pareto fronts and run the portfolio selection using non-denominated sorting. The default (None) is to use the list [PerfMeasure.MEAN, RiskMeasure.VARIANCE]

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.

References

[1]

“Large-Scale Portfolio Optimization Using Multi-objective Evolutionary Algorithms and Preselection Methods”, B.Y. Qu and Q.Zhou (2017).

Methods

fit(X[, y])	Run the Non Dominated 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)

Run the Non Dominated 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:

selfSelectNonDominated

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.