Localized Randomized Affine Shadowsampling (LoRAS) oversampling technique
The latest version is available on PyPi and installable with the command: pip install loras
There is just one method fit_resample(maj_class_points, min_class_points, k, num_shadow_points, list_sigma_f, num_generated_points, num_aff_comb, random_state=42)
There are two mandatory inputs:
maj_class_points : Majority class parent data points which is a non-empty list containing numpy arrays acting as pointsmin_class_points : Minority class parent data points which is a non-empty list containing numpy arrays acting as points There are also optional parameters:
k : Number of nearest neighbours to be considered per parent data point (default value: 8 if len(min_class_points)<100 else 30)num_shadow_points : Number of generated shadowsamples per parent data point (default value: ceil(2*num_aff_comb / k))list_sigma_f : List of standard deviations for normal distributions for adding noise to each feature (default value: [0.005, ... , 0.005])num_generated_points : Number of shadow points to be chosen for a random affine combination (default value: ceil((len(maj_class_points) + len(min_class_points)) / len(min_class_points)))num_aff_comb : Number of generated LoRAS points for each nearest neighbours group (default value: min_class_points.shape[1]) Output:
min_class_points::oversampled_set : Concatenation of original data points and oversampled ones