QuaPy: A Python-Based Framework for Quantification

Abstract

QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of interest in a sample of unlabelled data. While quantification can be trivially performed by applying a standard classifier to each unlabelled data item and counting how many data items have been assigned to each class, it has been shown that this "classify and count" method is outperformed by methods specifically designed for quantification. QuaPy provides implementations of a number of baseline methods and advanced quantification methods, of routines for quantificationoriented model selection, of several broadly accepted evaluation measures, and of robust evaluation protocols routinely used in the field. QuaPy also makes available datasets commonly used for testing quantifiers, and offers visualization tools for facilitating the analysis and interpretation of the results.The software is open-source and publicly available under a BSD-3 licence via GitHub 2 , and can be installed via pip 3 .

Minimising quantifier variance under prior probability shift

“…A lot of different approaches to quantification of prior class probabilities has been proposed and analysed (see, e.g. [7,9,15]), but it appears that the following question has not yet received very much attention:…”