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< section id = "installation" >
< h1 > Installation< a class = "headerlink" href = "#installation" title = "Permalink to this heading" > ¶< / a > < / h1 >
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< p > QuaPy can be easily installed via < cite > pip< / cite > < / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > pip< / span > < span class = "n" > install< / span > < span class = "n" > quapy< / span >
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< p > See < a class = "reference external" href = "https://pypi.org/project/QuaPy/" > pip page< / a > for older versions.< / p >
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< section id = "requirements" >
< h2 > Requirements< a class = "headerlink" href = "#requirements" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< li > < p > scikit-learn, numpy, scipy< / p > < / li >
< li > < p > pytorch (for QuaNet)< / p > < / li >
< li > < p > svmperf patched for quantification (see below)< / p > < / li >
< li > < p > joblib< / p > < / li >
< li > < p > tqdm< / p > < / li >
< li > < p > pandas, xlrd< / p > < / li >
< li > < p > matplotlib< / p > < / li >
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< / section >
< section id = "svm-perf-with-quantification-oriented-losses" >
< h2 > SVM-perf with quantification-oriented losses< a class = "headerlink" href = "#svm-perf-with-quantification-oriented-losses" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
SVM(AE), or SVM(RAE), you have to first download the
< a class = "reference external" href = "http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html" > svmperf< / a >
package, apply the patch
< a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch" > svm-perf-quantification-ext.patch< / a > ,
and compile the sources.
The script
< a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh" > prepare_svmperf.sh< / a > ,
does all the job. Simply run:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "o" > ./< / span > < span class = "n" > prepare_svmperf< / span > < span class = "o" > .< / span > < span class = "n" > sh< / span >
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< p > The resulting directory < cite > ./svm_perf_quantification< / cite > contains the
patched version of < cite > svmperf< / cite > with quantification-oriented losses.< / p >
< p > The
< a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch" > svm-perf-quantification-ext.patch< / a >
is an extension of the patch made available by
< a class = "reference external" href = "https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0" > Esuli et al. 2015< / a >
that allows SVMperf to optimize for
the < cite > Q< / cite > measure as proposed by
< a class = "reference external" href = "https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X" > Barranquero et al. 2015< / a >
and for the < cite > KLD< / cite > and < cite > NKLD< / cite > as proposed by
< a class = "reference external" href = "https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0" > Esuli et al. 2015< / a >
for quantification.
This patch extends the former by also allowing SVMperf to optimize for
< cite > AE< / cite > and < cite > RAE< / cite > .< / p >
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< li > < a class = "reference internal" href = "#svm-perf-with-quantification-oriented-losses" > SVM-perf with quantification-oriented losses< / a > < / li >
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