Installation
------------

QuaPy can be easily installed via `pip`

::

   pip install quapy

See `pip page <https://pypi.org/project/QuaPy/>`_ 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 <http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_
package, apply the patch
`svm-perf-quantification-ext.patch <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_,
and compile the sources.
The script
`prepare_svmperf.sh <https://github.com/HLT-ISTI/QuaPy/blob/master/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 <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_
is an extension of the patch made available by
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
that allows SVMperf to optimize for
the `Q` measure as proposed by
`Barranquero et al. 2015 <https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X>`_
and for the `KLD` and `NKLD` as proposed by
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
for quantification.
This patch extends the former by also allowing SVMperf to optimize for
`AE` and `RAE`.