56 lines
1.9 KiB
ReStructuredText
56 lines
1.9 KiB
ReStructuredText
Installation
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------------
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QuaPy can be easily installed via `pip`
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::
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pip install quapy
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See `pip page <https://pypi.org/project/QuaPy/>`_ for older versions.
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Requirements
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************
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* scikit-learn, numpy, scipy
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* pytorch (for QuaNet)
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* svmperf patched for quantification (see below)
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* joblib
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* tqdm
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* pandas, xlrd
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* matplotlib
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SVM-perf with quantification-oriented losses
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********************************************
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In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
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SVM(AE), or SVM(RAE), you have to first download the
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`svmperf <http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_
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package, apply the patch
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`svm-perf-quantification-ext.patch <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_,
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and compile the sources.
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The script
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`prepare_svmperf.sh <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_,
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does all the job. Simply run:
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::
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./prepare_svmperf.sh
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The resulting directory `./svm_perf_quantification` contains the
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patched version of `svmperf` with quantification-oriented losses.
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The
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`svm-perf-quantification-ext.patch <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_
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is an extension of the patch made available by
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`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
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that allows SVMperf to optimize for
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the `Q` measure as proposed by
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`Barranquero et al. 2015 <https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X>`_
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and for the `KLD` and `NKLD` as proposed by
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`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
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for quantification.
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This patch extends the former by also allowing SVMperf to optimize for
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`AE` and `RAE`. |