nannyml.drift package



Module contents

NannyML drift detection module.

This module contains ways to detect both univariate (within a single continuous or categorical column) and multivariate (across multiple columns) drift.

The univariate drift detection methods include:

  • Kolmogorov-Smirnov statistic (continuous)

  • Wasserstein distance (continuous)

  • Chi-squared statistic (categorical)

  • L-infinity distance (categorical)

  • Jensen-Shannon distance

  • Hellinger distance

The multivariate drift detection methods include:

  • Data reconstruction error: detects drift by performing dimensionality reduction on the model inputs using PCA and then applying the inverse transformation on the latent (reduced) space.

  • Domain Classifer: detects drift by looking at how performance a domain classifier is at distinguising between the reference and the chunk datasets.