Similarity Stratified Split

Implementation of the Similarity-Based Stratified Splitting algorithm described in Similarity Based Stratified Splitting: an approach to train better classifiers.

Overview

The authors propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split a dataset. Splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads to a more realistic performance estimation when used in real-world applications.

Install

PyPI

pip install sbss

Local

git clone https://github.com/timothyckl/similarity-stratified-split.git
cd ./similarity-stratified-split
pip install -e .

Usage

import numpy as np
from scipy.spatial import distance
from sbss import SimilarityStratifiedSplit

def get_distances(x):
    distances = distance.squareform(distance.pdist(x, metric='euclidean'))
    return distances

# inputs are recommended to be normalized
X = np.random.rand(1000, 128)
y = np.random.randint(0, 10, (1000,))

n_splits = 3
s = SimilarityStratifiedSplit(n_splits, get_distances)

for train_index, test_index in s.split(X, y):
  print(f"Train indices: {train_index}\nTest indices: {test_index}")
  print("="*100)

References

• Farias, F., Ludermir, T. and Bastos-Filho, C. (2020) Similarity based stratified splitting: An approach to train better classifiers, arXiv.org. Available at: https://arxiv.org/abs/2010.06099 (Accessed: 27 November 2023).