FastCan#

class fastcan.FastCan(n_features_to_select=1, *, indices_include=None, indices_exclude=None, eta=False, tol=0.01, beam_width=1, verbose=1)#

Forward feature selector according to the sum of squared canonical correlation coefficients (SSC).

Parameters:

  • n_features_to_select (int, default=1) – The parameter is the absolute number of features to select.

  • indices_include (array-like of shape (n_inclusions,), default=None) – The indices of the prerequisite features.

  • indices_exclude (array-like of shape (n_exclusions,), default=None) – The indices of the excluded features.

  • eta (bool, default=False) – Whether to use eta-cosine method.

  • tol (float, default=0.01) –

    Tolerance for linear dependence check.

    When abs(w.T*x) > tol, the modified Gram-Schmidt is failed as the feature x is linear dependent to the selected features, and mask for that feature will True.

  • beam_width (int, default=1) –

    The beam width for beam search. When beam_width = 1, use greedy search. When beam_width > 1, use beam search.

    Added in version 0.5.0.

  • verbose (int, default=1) – The verbosity level.

n_features_in_#

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

Type:

int

feature_names_in_#

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type:

ndarray of shape (n_features_in_,)

indices_#

The indices of the selected features. The order of the indices is corresponding to the feature selection process.

Type:

ndarray of shape (n_features_to_select,), dtype=int

support_#

The mask of selected features.

Type:

ndarray of shape (n_features,), dtype=bool

scores_#

The h-correlation/eta-cosine of selected features. The order of the scores is corresponding to the feature selection process.

Type:

ndarray of shape (n_features_to_select,), dtype=float

X_transformed_#

Transformed feature matrix. When h-correlation method is used, n_samples_ = n_samples. When eta-cosine method is used, n_samples_ = n_features+n_outputs.

Type:

ndarray of shape (n_samples_, n_features), dtype=float, order=’F’

y_transformed_#

Transformed target matrix. When h-correlation method is used, n_samples_ = n_samples. When eta-cosine method is used, n_samples_ = n_features+n_outputs.

Type:

ndarray of shape (n_samples_, n_outputs), dtype=float, order=’F’

indices_include_#

The indices of the prerequisite features.

Type:

ndarray of shape (n_inclusions,), dtype=int

indices_exclude_#

The indices of the excluded features.

Type:

array-like of shape (n_exclusions,), dtype=int

References

  • Zhang, S., & Lang, Z. Q. (2022).

    Orthogonal least squares based fast feature selection for linear classification. Pattern Recognition, 123, 108419.

  • Zhang, S., Wang, T., Worden, K., Sun L., & Cross, E. J. (2025).

    Canonical-correlation-based fast feature selection for structural health monitoring. Mechanical Systems and Signal Processing, 223, 111895.

Examples

>>> from fastcan import FastCan
>>> X = [[1, 0], [0, 1]]
>>> y = [1, 0]
>>> FastCan(verbose=0).fit(X, y).get_support()
array([ True, False])
fit(X, y)#

Prepare data for h-correlation or eta-cosine methods and select features.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Feature matrix.

  • y (array-like of shape (n_samples, n_outputs)) – Target matrix.

Returns:

self – Returns the instance itself.

Return type:

object

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 – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

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 – Transformed feature names.

Return type:

ndarray of str objects

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

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 – Parameter names mapped to their values.

Return type:

dict

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 – 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.

Return type:

array

inverse_transform(X)#

Reverse the transformation operation.

Parameters:

X (array of shape [n_samples, n_selected_features]) – The input samples.

Returns:

X_original – X with columns of zeros inserted where features would have been removed by transform().

Return type:

array of shape [n_samples, n_original_features]

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API 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:

self – Estimator instance.

Return type:

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

transform(X)#

Reduce X to the selected features.

Parameters:

X (array of shape [n_samples, n_features]) – The input samples.

Returns:

X_r – The input samples with only the selected features.

Return type:

array of shape [n_samples, n_selected_features]