Installation

QuaPy can be easily installed via pip

pip install quapy

See pip page for older versions.

Requirements

  • scikit-learn, numpy, scipy

  • pytorch (for QuaNet)

  • svmperf patched for quantification (see below)

  • joblib

  • tqdm

  • pandas, xlrd

  • matplotlib

SVM-perf with quantification-oriented losses

In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD), SVM(AE), or SVM(RAE), you have to first download the svmperf package, apply the patch svm-perf-quantification-ext.patch, and compile the sources. The script prepare_svmperf.sh, does all the job. Simply run:

./prepare_svmperf.sh

The resulting directory ./svm_perf_quantification contains the patched version of svmperf with quantification-oriented losses.

The svm-perf-quantification-ext.patch is an extension of the patch made available by Esuli et al. 2015 that allows SVMperf to optimize for the Q measure as proposed by Barranquero et al. 2015 and for the KLD and NKLD as proposed by Esuli et al. 2015 for quantification. This patch extends the former by also allowing SVMperf to optimize for AE and RAE.