Instance Hardness

This file contains the implementation of different measures of instance hardness.

deslib.util.instance_hardness.hardness_region_competence(neighbors_idx, labels, safe_k)

Calculate the Instance hardness of the sample based on its neighborhood. The sample is deemed hard to classify when there is overlap between different classes in the region of competence. This method does not takes into account the target label of the test sample

This hardness measure is used to select whether use DS or use the KNN for the classification of a given query sample

Parameters:

neighbors_idx : array of shape = [n_samples_test, k]

Indices of the nearest neighbors for each considered sample

labels : array of shape = [n_samples_train]

labels associated with each training sample

safe_k : int

Number of neighbors used to estimate the hardness of the corresponding region

Returns:

hardness : array of shape = [n_samples_test]

The Hardness level associated with each example.

References

Smith, M.R., Martinez, T. and Giraud-Carrier, C., 2014. An instance level analysis of data complexity. Machine learning, 95(2), pp.225-256

deslib.util.instance_hardness.kdn_score(X, y, k)

Calculates the K-Disagreeing Neighbors score (KDN) of each sample in the input dataset.

Parameters:

X : array of shape (n_samples, n_features)

The input data.

y : array of shape (n_samples)

class labels of each example in X.

k : int

Neighborhood size for calculating the KDN score.

Returns:

score : array of shape = [n_samples,1]

KDN score of each sample in X.

neighbors : array of shape = [n_samples,k]

Indexes of the k neighbors of each sample in X.

References

M. R. Smith, T. Martinez, C. Giraud-Carrier, An instance level analysis of data complexity, Machine Learning 95 (2) (2014) 225-256.