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< title > Welcome to QuaPy’ — QuaPy 0.1.7 documentation< / title >
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< li class = "nav-item nav-item-0" > < a href = "#" > QuaPy 0.1.7 documentation< / a > » < / li >
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< li class = "nav-item nav-item-this" > < a href = "" > Welcome to QuaPy’ < / a > < / li >
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< h1 > Welcome to QuaPy’ < a class = "headerlink" href = "#welcome-to-quapy-s-documentation" title = "Permalink to this heading" > ¶< / a > < / h1 >
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< p > QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
written in Python.< / p >
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< section id = "introduction" >
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< h2 > Introduction< a class = "headerlink" href = "#introduction" title = "Permalink to this heading" > ¶< / a > < / h2 >
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< p > QuaPy roots on the concept of data sample, and provides implementations of most important concepts
in quantification literature, such as the most important quantification baselines, many advanced
quantification methods, quantification-oriented model selection, many evaluation measures and protocols
used for evaluating quantification methods.
QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and
interpretation of results.< / p >
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< section id = "a-quick-example" >
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< h3 > A quick example:< a class = "headerlink" href = "#a-quick-example" title = "Permalink to this heading" > ¶< / a > < / h3 >
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< p > The following script fetchs a Twitter dataset, trains and evaluates an
< cite > Adjusted Classify & Count< / cite > model in terms of the < cite > Mean Absolute Error< / cite > (MAE)
between the class prevalences estimated for the test set and the true prevalences
of the test set.< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
< span class = "kn" > from< / span > < span class = "nn" > sklearn.linear_model< / span > < span class = "kn" > import< / span > < span class = "n" > LogisticRegression< / span >
< span class = "n" > dataset< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_twitter< / span > < span class = "p" > (< / span > < span class = "s1" > ' semeval16' < / span > < span class = "p" > )< / span >
< span class = "c1" > # create an " Adjusted Classify & Count" quantifier< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > method< / span > < span class = "o" > .< / span > < span class = "n" > aggregative< / span > < span class = "o" > .< / span > < span class = "n" > ACC< / span > < span class = "p" > (< / span > < span class = "n" > LogisticRegression< / span > < span class = "p" > ())< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > dataset< / span > < span class = "o" > .< / span > < span class = "n" > training< / span > < span class = "p" > )< / span >
< span class = "n" > estim_prevalences< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > quantify< / span > < span class = "p" > (< / span > < span class = "n" > dataset< / span > < span class = "o" > .< / span > < span class = "n" > test< / span > < span class = "o" > .< / span > < span class = "n" > instances< / span > < span class = "p" > )< / span >
< span class = "n" > true_prevalences< / span > < span class = "o" > =< / span > < span class = "n" > dataset< / span > < span class = "o" > .< / span > < span class = "n" > test< / span > < span class = "o" > .< / span > < span class = "n" > prevalence< / span > < span class = "p" > ()< / span >
< span class = "n" > error< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > error< / span > < span class = "o" > .< / span > < span class = "n" > mae< / span > < span class = "p" > (< / span > < span class = "n" > true_prevalences< / span > < span class = "p" > ,< / span > < span class = "n" > estim_prevalences< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "sa" > f< / span > < span class = "s1" > ' Mean Absolute Error (MAE)=< / span > < span class = "si" > {< / span > < span class = "n" > error< / span > < span class = "si" > :< / span > < span class = "s1" > .3f< / span > < span class = "si" > }< / span > < span class = "s1" > ' < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Quantification is useful in scenarios of prior probability shift. In other
words, we would not be interested in estimating the class prevalences of the test set if
we could assume the IID assumption to hold, as this prevalence would simply coincide with the
class prevalence of the training set. For this reason, any Quantification model
should be tested across samples characterized by different class prevalences.
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
See the < a class = "reference internal" href = "Evaluation.html" > < span class = "doc" > Evaluation< / span > < / a > for detailed examples.< / p >
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< / section >
< section id = "features" >
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< h3 > Features< a class = "headerlink" href = "#features" title = "Permalink to this heading" > ¶< / a > < / h3 >
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< ul class = "simple" >
< li > < p > Implementation of most popular quantification methods (Classify-& -Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).< / p > < / li >
< li > < p > Versatile functionality for performing evaluation based on artificial sampling protocols.< / p > < / li >
< li > < p > Implementation of most commonly used evaluation metrics (e.g., MAE, MRAE, MSE, NKLD, etc.).< / p > < / li >
< li > < dl class = "simple" >
< dt > Popular datasets for Quantification (textual and numeric) available, including:< / dt > < dd > < ul >
< li > < p > 32 UCI Machine Learning datasets.< / p > < / li >
< li > < p > 11 Twitter Sentiment datasets.< / p > < / li >
< li > < p > 3 Reviews Sentiment datasets.< / p > < / li >
< / ul >
< / dd >
< / dl >
< / li >
< li > < p > Native supports for binary and single-label scenarios of quantification.< / p > < / li >
< li > < p > Model selection functionality targeting quantification-oriented losses.< / p > < / li >
< li > < p > Visualization tools for analysing results.< / p > < / li >
< / ul >
< div class = "toctree-wrapper compound" >
< p class = "caption" role = "heading" > < span class = "caption-text" > Contents:< / span > < / p >
< ul >
< li class = "toctree-l1" > < a class = "reference internal" href = "Installation.html" > Installation< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "Installation.html#requirements" > Requirements< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "Installation.html#svm-perf-with-quantification-oriented-losses" > SVM-perf with quantification-oriented losses< / a > < / li >
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< / ul >
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< li class = "toctree-l1" > < a class = "reference internal" href = "Methods.html" > Quantification Methods< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "Methods.html#aggregative-methods" > Aggregative Methods< / a > < / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "Model-Selection.html#targeting-a-quantification-oriented-loss" > Targeting a Quantification-oriented loss< / a > < / li >
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< / ul >
< / li >
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< li class = "toctree-l2" > < a class = "reference internal" href = "Plotting.html#diagonal-plot" > Diagonal Plot< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "Plotting.html#quantification-bias" > Quantification bias< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "Plotting.html#error-by-drift" > Error by Drift< / a > < / li >
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