AsymptoticIndependenceTestStop#

class skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting.AsymptoticIndependenceTestStop(significance=0.01)[source]#

Stop when the selected point is independent from the target.

It uses an asymptotic test based on the chi-squared distribution described in [1]. The test rejects independence if

\[\frac{n \mathcal{V}_n}{T_2} \geq \mathcal{X}_{1-\alpha}^2,\]

where \(n\) is the number of samples, \(\mathcal{V}_n\) is the sample distance correlation between the selected point and the target, \(\mathcal{X}_{1-\alpha}^2\) is the \(1-\alpha\) quantile of a chi-squared variable with 1 degree of freedom. \(T_2\) is the product of the means of the distance matrices of the selected point and the target, a term which is involved in the standard computation of the sample distance covariance.

Parameters:: significance (float) – Significance used in the independence test. By default is 0.01 (1%).

References

Methods

`chi_bound`(x, y, significance)
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

chi_bound(x, y, significance)[source]#

Parameters:

x (ndarray[Any, dtype[float64]]) –
y (ndarray[Any, dtype[float64]]) –
significance (float) –

Return type:

float

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

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