adding documentation, adding brokenbar plots, merging plots from tweetsent with density
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<li><a href="quapy.html#quapy.plot.binary_diagonal">binary_diagonal() (in module quapy.plot)</a>
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</li>
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<li><a href="quapy.method.html#quapy.method.base.BinaryQuantifier">BinaryQuantifier (class in quapy.method.base)</a>
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</li>
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<li><a href="quapy.html#quapy.plot.brokenbar_supremacy_by_drift">brokenbar_supremacy_by_drift() (in module quapy.plot)</a>
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</li>
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</ul></td>
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</tr></table>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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<title>Welcome to QuaPy’s documentation! — QuaPy 0.1.6 documentation</title>
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="welcome-to-quapy-s-documentation">
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<section id="welcome-to-quapy-s-documentation">
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<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this headline">¶</a></h1>
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<p>QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
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written in Python.</p>
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<div class="section" id="introduction">
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<section id="introduction">
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<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
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<p>QuaPy roots on the concept of data sample, and provides implementations of most important concepts
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in quantification literature, such as the most important quantification baselines, many advanced
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@ -57,7 +58,7 @@ quantification methods, quantification-oriented model selection, many evaluation
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used for evaluating quantification methods.
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QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and
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interpretation of results.</p>
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<div class="section" id="a-quick-example">
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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 headline">¶</a></h3>
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<p>The following script fetchs a Twitter dataset, trains and evaluates an
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<cite>Adjusted Classify & Count</cite> model in terms of the <cite>Mean Absolute Error</cite> (MAE)
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@ -87,8 +88,8 @@ class prevalence of the training set. For this reason, any Quantification model
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should be tested across samples characterized by different class prevalences.
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QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
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See the <a class="reference internal" href="Evaluation.html"><span class="doc">Evaluation</span></a> for detailed examples.</p>
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</div>
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<div class="section" id="features">
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</section>
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<section id="features">
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<h3>Features<a class="headerlink" href="#features" title="Permalink to this headline">¶</a></h3>
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<ul class="simple">
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<li><p>Implementation of most popular quantification methods (Classify-&-Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).</p></li>
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</li>
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</ul>
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</div>
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</div>
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</div>
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</div>
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<div class="section" id="indices-and-tables">
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</section>
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</section>
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</section>
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<section id="indices-and-tables">
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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<ul class="simple">
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<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
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<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
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<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
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</ul>
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</div>
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</section>
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<html>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>quapy — QuaPy 0.1.6 documentation</title>
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="quapy">
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<section id="quapy">
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<h1>quapy<a class="headerlink" href="#quapy" title="Permalink to this headline">¶</a></h1>
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<div class="toctree-wrapper compound">
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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<title>quapy.classification package — QuaPy 0.1.6 documentation</title>
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="quapy-classification-package">
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<section id="quapy-classification-package">
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<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="submodules">
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<section id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="module-quapy.classification.methods">
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</section>
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<section id="module-quapy.classification.methods">
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<span id="quapy-classification-methods-module"></span><h2>quapy.classification.methods module<a class="headerlink" href="#module-quapy.classification.methods" title="Permalink to this headline">¶</a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression">
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@ -165,8 +166,8 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
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</dd></dl>
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</div>
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<div class="section" id="module-quapy.classification.neural">
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</section>
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<section id="module-quapy.classification.neural">
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<span id="quapy-classification-neural-module"></span><h2>quapy.classification.neural module<a class="headerlink" href="#module-quapy.classification.neural" title="Permalink to this headline">¶</a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
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</dd></dl>
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</div>
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<div class="section" id="module-quapy.classification.svmperf">
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</section>
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<section id="module-quapy.classification.svmperf">
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<span id="quapy-classification-svmperf-module"></span><h2>quapy.classification.svmperf module<a class="headerlink" href="#module-quapy.classification.svmperf" title="Permalink to this headline">¶</a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
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@ -653,11 +654,11 @@ for further details.</p>
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</dd></dl>
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</div>
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<div class="section" id="module-quapy.classification">
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</section>
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<section id="module-quapy.classification">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this headline">¶</a></h2>
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</div>
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</div>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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<title>quapy.data package — QuaPy 0.1.6 documentation</title>
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="quapy-data-package">
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<section id="quapy-data-package">
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<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="submodules">
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<section id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="module-quapy.data.base">
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</section>
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<section id="module-quapy.data.base">
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<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Permalink to this headline">¶</a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.data.base.Dataset">
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<span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">isbinary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.isbinary" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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</div>
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<div class="section" id="module-quapy.data.datasets">
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</section>
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<section id="module-quapy.data.datasets">
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<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Permalink to this headline">¶</a></h2>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.data.datasets.df_replace">
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<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.datasets.warn" title="Permalink to this definition">¶</a></dt>
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<dd></dd></dl>
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<div class="section" id="module-quapy.data.preprocessing">
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</section>
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<section id="module-quapy.data.preprocessing">
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<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Permalink to this headline">¶</a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer">
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where the instances are stored in a csr_matrix of real-valued tfidf scores</p>
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</dd></dl>
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</div>
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<div class="section" id="module-quapy.data.reader">
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</section>
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<section id="module-quapy.data.reader">
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<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Permalink to this headline">¶</a></h2>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.data.reader.binarize">
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:return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.</p>
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</dd></dl>
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</div>
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<div class="section" id="module-quapy.data">
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</section>
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<section id="module-quapy.data">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Permalink to this headline">¶</a></h2>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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<title>quapy package — QuaPy 0.1.6 documentation</title>
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<section id="quapy-package">
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<h1>quapy package<a class="headerlink" href="#quapy-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="subpackages">
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<section id="subpackages">
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<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2>
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-quapy.error">
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</section>
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<span id="quapy-error-module"></span><h2>quapy.error module<a class="headerlink" href="#module-quapy.error" title="Permalink to this headline">¶</a></h2>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.error.absolute_error">
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<span id="quapy-evaluation-module"></span><h2>quapy.evaluation module<a class="headerlink" href="#module-quapy.evaluation" title="Permalink to this headline">¶</a></h2>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_prediction">
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|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metrics</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'mae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.natural_prevalence_report" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.functional">
|
||||
</section>
|
||||
<section id="module-quapy.functional">
|
||||
<span id="quapy-functional-module"></span><h2>quapy.functional module<a class="headerlink" href="#module-quapy.functional" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.HellingerDistance">
|
||||
|
|
@ -668,8 +669,8 @@ and with the limits smoothed, i.e.:
|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_simplex_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.uniform_simplex_sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.model_selection">
|
||||
</section>
|
||||
<section id="module-quapy.model_selection">
|
||||
<span id="quapy-model-selection-module"></span><h2>quapy.model_selection module<a class="headerlink" href="#module-quapy.model_selection" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ">
|
||||
|
|
@ -783,27 +784,92 @@ a float in [0,1] indicating the proportion of labelled data to extract from the
|
|||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.plot">
|
||||
</section>
|
||||
<section id="module-quapy.plot">
|
||||
<span id="quapy-plot-module"></span><h2>quapy.plot module<a class="headerlink" href="#module-quapy.plot" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_bias_bins">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_bias_bins</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">method_names</span></em>, <em class="sig-param"><span class="pre">true_prevs</span></em>, <em class="sig-param"><span class="pre">estim_prevs</span></em>, <em class="sig-param"><span class="pre">pos_class=1</span></em>, <em class="sig-param"><span class="pre">title=None</span></em>, <em class="sig-param"><span class="pre">nbins=5</span></em>, <em class="sig-param"><span class="pre">colormap=<matplotlib.colors.ListedColormap</span> <span class="pre">object></span></em>, <em class="sig-param"><span class="pre">vertical_xticks=False</span></em>, <em class="sig-param"><span class="pre">legend=True</span></em>, <em class="sig-param"><span class="pre">savepath=None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_bias_bins" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>method_names</strong> – array-like with the method names for each experiment</p></li>
|
||||
<li><p><strong>true_prevs</strong> – array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment</p></li>
|
||||
<li><p><strong>estim_prevs</strong> – array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment</p></li>
|
||||
<li><p><strong>pos_class</strong> – index of the positive class</p></li>
|
||||
<li><p><strong>title</strong> – the title to be displayed in the plot</p></li>
|
||||
<li><p><strong>nbins</strong> – number of bins</p></li>
|
||||
<li><p><strong>colormap</strong> – the matplotlib colormap to use (default cm.tab10)</p></li>
|
||||
<li><p><strong>vertical_xticks</strong> – </p></li>
|
||||
<li><p><strong>legend</strong> – whether or not to display the legend (default is True)</p></li>
|
||||
<li><p><strong>savepath</strong> – path where to save the plot. If not indicated (as default), the plot is shown.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_bias_global">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_bias_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_bias_global" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
<dd><p>Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)
|
||||
for each quantification method with respect to a given positive class.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>method_names</strong> – array-like with the method names for each experiment</p></li>
|
||||
<li><p><strong>true_prevs</strong> – array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment</p></li>
|
||||
<li><p><strong>estim_prevs</strong> – array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment</p></li>
|
||||
<li><p><strong>pos_class</strong> – index of the positive class</p></li>
|
||||
<li><p><strong>title</strong> – the title to be displayed in the plot</p></li>
|
||||
<li><p><strong>savepath</strong> – path where to save the plot. If not indicated (as default), the plot is shown.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_diagonal">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_diagonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_prev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_diagonal" title="Permalink to this definition">¶</a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_diagonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_prev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_diagonal" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence
|
||||
values (along the x-axis). The optimal quantifier is described by the diagonal (0,0)-(1,1) of the plot (hence the
|
||||
name). It is convenient for binary quantification problems, though it can be used for multiclass problems by
|
||||
indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots
|
||||
like the <a class="reference internal" href="#quapy.plot.error_by_drift" title="quapy.plot.error_by_drift"><code class="xref py py-meth docutils literal notranslate"><span class="pre">error_by_drift()</span></code></a> might be preferable though).</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>method_names</strong> – array-like with the method names for each experiment</p></li>
|
||||
<li><p><strong>true_prevs</strong> – array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment</p></li>
|
||||
<li><p><strong>estim_prevs</strong> – array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment</p></li>
|
||||
<li><p><strong>pos_class</strong> – index of the positive class</p></li>
|
||||
<li><p><strong>title</strong> – the title to be displayed in the plot</p></li>
|
||||
<li><p><strong>show_std</strong> – whether or not to show standard deviations (represented by color bands). This might be inconvenient
|
||||
for cases in which many methods are compared, or when the standard deviations are high – default True)</p></li>
|
||||
<li><p><strong>legend</strong> – whether or not to display the leyend (default True)</p></li>
|
||||
<li><p><strong>train_prev</strong> – if indicated (default is None), the training prevalence (for the positive class) is hightlighted
|
||||
in the plot. This is convenient when all the experiments have been conducted in the same dataset.</p></li>
|
||||
<li><p><strong>savepath</strong> – path where to save the plot. If not indicated (as default), the plot is shown.</p></li>
|
||||
<li><p><strong>method_order</strong> – if indicated (default is None), imposes the order in which the methods are processed (i.e.,
|
||||
listed in the legend and associated with matplotlib colors).</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.brokenbar_supremacy_by_drift">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">brokenbar_supremacy_by_drift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tr_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binning</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'isomerous'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ttest_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.005</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tail_density_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.005</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.brokenbar_supremacy_by_drift" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.error_by_drift">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">error_by_drift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tr_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Quantification</span> <span class="pre">error</span> <span class="pre">as</span> <span class="pre">a</span> <span class="pre">function</span> <span class="pre">of</span> <span class="pre">distribution</span> <span class="pre">shift'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.error_by_drift" title="Permalink to this definition">¶</a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">error_by_drift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tr_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Quantification</span> <span class="pre">error</span> <span class="pre">as</span> <span class="pre">a</span> <span class="pre">function</span> <span class="pre">of</span> <span class="pre">distribution</span> <span class="pre">shift'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vlines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.error_by_drift" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
|
|
@ -811,8 +877,8 @@ a float in [0,1] indicating the proportion of labelled data to extract from the
|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">save_or_show</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">savepath</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.save_or_show" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.util">
|
||||
</section>
|
||||
<section id="module-quapy.util">
|
||||
<span id="quapy-util-module"></span><h2>quapy.util module<a class="headerlink" href="#module-quapy.util" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.util.EarlyStop">
|
||||
|
|
@ -901,16 +967,16 @@ with temp_seed(random_seed):</p>
|
|||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy">
|
||||
</section>
|
||||
<section id="module-quapy">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.isbinary">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.</span></span><span class="sig-name descname"><span class="pre">isbinary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.isbinary" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
|
||||
|
||||
<title>quapy.method package — QuaPy 0.1.6 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
|
||||
|
|
@ -47,12 +48,12 @@
|
|||
<div class="bodywrapper">
|
||||
<div class="body" role="main">
|
||||
|
||||
<div class="section" id="quapy-method-package">
|
||||
<section id="quapy-method-package">
|
||||
<h1>quapy.method package<a class="headerlink" href="#quapy-method-package" title="Permalink to this headline">¶</a></h1>
|
||||
<div class="section" id="submodules">
|
||||
<section id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method.aggregative">
|
||||
</section>
|
||||
<section id="module-quapy.method.aggregative">
|
||||
<span id="quapy-method-aggregative-module"></span><h2>quapy.method.aggregative module<a class="headerlink" href="#module-quapy.method.aggregative" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC">
|
||||
|
|
@ -599,8 +600,8 @@ LabelledCollection, represents the validation split itself
|
|||
or None otherwise) to be used as a validation set for any subsequent parameter fitting</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method.base">
|
||||
</section>
|
||||
<section id="module-quapy.method.base">
|
||||
<span id="quapy-method-base-module"></span><h2>quapy.method.base module<a class="headerlink" href="#module-quapy.method.base" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier">
|
||||
|
|
@ -674,8 +675,8 @@ or None otherwise) to be used as a validation set for any subsequent parameter f
|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">isprobabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.isprobabilistic" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method.meta">
|
||||
</section>
|
||||
<section id="module-quapy.method.meta">
|
||||
<span id="quapy-method-meta-module"></span><h2>quapy.method.meta module<a class="headerlink" href="#module-quapy.method.meta" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.method.meta.EACC">
|
||||
|
|
@ -814,8 +815,8 @@ to a first approximation of the test prevalence as made by all models in the ens
|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">get_probability_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posterior_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.get_probability_distribution" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method.neural">
|
||||
</section>
|
||||
<section id="module-quapy.method.neural">
|
||||
<span id="quapy-method-neural-module"></span><h2>quapy.method.neural module<a class="headerlink" href="#module-quapy.method.neural" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetModule">
|
||||
|
|
@ -912,8 +913,8 @@ fit_learner=False, the data will be split in 66/34 for training QuaNet and valid
|
|||
<span class="sig-prename descclassname"><span class="pre">quapy.method.neural.</span></span><span class="sig-name descname"><span class="pre">mae_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.mae_loss" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method.non_aggregative">
|
||||
</section>
|
||||
<section id="module-quapy.method.non_aggregative">
|
||||
<span id="quapy-method-non-aggregative-module"></span><h2>quapy.method.non_aggregative module<a class="headerlink" href="#module-quapy.method.non_aggregative" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">
|
||||
|
|
@ -946,11 +947,11 @@ fit_learner=False, the data will be split in 66/34 for training QuaNet and valid
|
|||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.method">
|
||||
</section>
|
||||
<section id="module-quapy.method">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.method" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
328
quapy/plot.py
328
quapy/plot.py
|
|
@ -1,34 +1,58 @@
|
|||
from collections import defaultdict
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.cm import get_cmap
|
||||
import numpy as np
|
||||
from matplotlib import cm
|
||||
from scipy.stats import ttest_ind_from_stats
|
||||
|
||||
import quapy as qp
|
||||
from matplotlib.font_manager import FontProperties
|
||||
|
||||
plt.rcParams['figure.figsize'] = [12, 8]
|
||||
plt.rcParams['figure.dpi'] = 200
|
||||
plt.rcParams['font.size'] = 16
|
||||
|
||||
|
||||
def _set_colors(ax, n_methods):
|
||||
NUM_COLORS = n_methods
|
||||
cm = plt.get_cmap('tab20')
|
||||
ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
|
||||
|
||||
|
||||
def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
|
||||
train_prev=None, savepath=None):
|
||||
train_prev=None, savepath=None, method_order=None):
|
||||
"""
|
||||
The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence
|
||||
values (along the x-axis). The optimal quantifier is described by the diagonal (0,0)-(1,1) of the plot (hence the
|
||||
name). It is convenient for binary quantification problems, though it can be used for multiclass problems by
|
||||
indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots
|
||||
like the :meth:`error_by_drift` might be preferable though).
|
||||
|
||||
:param method_names: array-like with the method names for each experiment
|
||||
:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment
|
||||
:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment
|
||||
:param pos_class: index of the positive class
|
||||
:param title: the title to be displayed in the plot
|
||||
:param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient
|
||||
for cases in which many methods are compared, or when the standard deviations are high -- default True)
|
||||
:param legend: whether or not to display the leyend (default True)
|
||||
:param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted
|
||||
in the plot. This is convenient when all the experiments have been conducted in the same dataset.
|
||||
:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
|
||||
:param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,
|
||||
listed in the legend and associated with matplotlib colors).
|
||||
"""
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_aspect('equal')
|
||||
ax.grid()
|
||||
ax.plot([0, 1], [0, 1], '--k', label='ideal', zorder=1)
|
||||
|
||||
method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
|
||||
_set_colors(ax, n_methods=len(method_names))
|
||||
|
||||
for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
|
||||
order = list(zip(method_names, true_prevs, estim_prevs))
|
||||
if method_order is not None:
|
||||
table = {method_name:[true_prev, estim_prev] for method_name, true_prev, estim_prev in order}
|
||||
order = [(method_name, *table[method_name]) for method_name in method_order]
|
||||
|
||||
cm = plt.get_cmap('tab20')
|
||||
NUM_COLORS = len(method_names)
|
||||
ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
|
||||
for method, true_prev, estim_prev in order:
|
||||
true_prev = true_prev[:,pos_class]
|
||||
estim_prev = estim_prev[:,pos_class]
|
||||
|
||||
|
|
@ -50,14 +74,32 @@ def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=No
|
|||
ax.set_xlim(0, 1)
|
||||
|
||||
if legend:
|
||||
box = ax.get_position()
|
||||
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
|
||||
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
# box = ax.get_position()
|
||||
# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
|
||||
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
|
||||
ax.legend(loc='lower center',
|
||||
bbox_to_anchor=(1, -0.5),
|
||||
ncol=(len(method_names)+1)//2)
|
||||
|
||||
save_or_show(savepath)
|
||||
|
||||
|
||||
def binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title=None, savepath=None):
|
||||
"""
|
||||
Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)
|
||||
for each quantification method with respect to a given positive class.
|
||||
|
||||
:param method_names: array-like with the method names for each experiment
|
||||
:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment
|
||||
:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment
|
||||
:param pos_class: index of the positive class
|
||||
:param title: the title to be displayed in the plot
|
||||
:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
|
||||
"""
|
||||
|
||||
method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
|
|
@ -79,33 +121,47 @@ def binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title
|
|||
|
||||
def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=None, nbins=5, colormap=cm.tab10,
|
||||
vertical_xticks=False, legend=True, savepath=None):
|
||||
"""
|
||||
|
||||
:param method_names: array-like with the method names for each experiment
|
||||
:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
|
||||
each experiment
|
||||
:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
|
||||
for each experiment
|
||||
:param pos_class: index of the positive class
|
||||
:param title: the title to be displayed in the plot
|
||||
:param nbins: number of bins
|
||||
:param colormap: the matplotlib colormap to use (default cm.tab10)
|
||||
:param vertical_xticks:
|
||||
:param legend: whether or not to display the legend (default is True)
|
||||
:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
|
||||
"""
|
||||
from pylab import boxplot, plot, setp
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.grid()
|
||||
|
||||
method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
|
||||
_set_colors(ax, n_methods=len(method_names))
|
||||
|
||||
bins = np.linspace(0, 1, nbins+1)
|
||||
binwidth = 1/nbins
|
||||
data = {}
|
||||
for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
|
||||
true_prev = true_prev[:, pos_class]
|
||||
estim_prev = estim_prev[:, pos_class]
|
||||
true_prev = true_prev[:,pos_class]
|
||||
estim_prev = estim_prev[:,pos_class]
|
||||
|
||||
data[method] = []
|
||||
inds = np.digitize(true_prev, bins[1:], right=True)
|
||||
inds = np.digitize(true_prev, bins, right=True)
|
||||
for ind in range(len(bins)):
|
||||
selected = inds==ind
|
||||
data[method].append(estim_prev[selected] - true_prev[selected])
|
||||
|
||||
nmethods = len(method_names)
|
||||
boxwidth = binwidth/(nmethods+4)
|
||||
for i,bin in enumerate(bins):
|
||||
for i,bin in enumerate(bins[:-1]):
|
||||
boxdata = [data[method][i] for method in method_names]
|
||||
positions = [bin+(i*boxwidth)+2*boxwidth for i,_ in enumerate(method_names)]
|
||||
box = boxplot(boxdata, showmeans=False, positions=positions, widths=boxwidth, sym='+', patch_artist=True)
|
||||
box = boxplot(boxdata, showmeans=False, positions=positions, widths = boxwidth, sym='+', patch_artist=True)
|
||||
for boxid in range(len(method_names)):
|
||||
c = colormap.colors[boxid%len(colormap.colors)]
|
||||
setp(box['fliers'][boxid], color=c, marker='+', markersize=3., markeredgecolor=c)
|
||||
|
|
@ -118,7 +174,7 @@ def binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title=N
|
|||
major_xticks_positions.append(b)
|
||||
minor_xticks_positions.append(b + binwidth / 2)
|
||||
major_xticks_labels.append('')
|
||||
minor_xticks_labels.append(f'[{bins[i]:.2f}-{bins[i + 1]:.2f}' + (')' if i < len(bins)-2 else ']'))
|
||||
minor_xticks_labels.append(f'[{bins[i]:.2f}-{bins[i + 1]:.2f})')
|
||||
ax.set_xticks(major_xticks_positions)
|
||||
ax.set_xticks(minor_xticks_positions, minor=True)
|
||||
ax.set_xticklabels(major_xticks_labels)
|
||||
|
|
@ -166,10 +222,19 @@ def _merge(method_names, true_prevs, estim_prevs):
|
|||
return method_order, true_prevs_, estim_prevs_
|
||||
|
||||
|
||||
def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=True,
|
||||
def _set_colors(ax, n_methods):
|
||||
NUM_COLORS = n_methods
|
||||
cm = plt.get_cmap('tab20')
|
||||
ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
|
||||
|
||||
|
||||
def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=False,
|
||||
show_density=True,
|
||||
logscale=False,
|
||||
title=f'Quantification error as a function of distribution shift',
|
||||
savepath=None):
|
||||
savepath=None,
|
||||
vlines=None,
|
||||
method_order=None):
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.grid()
|
||||
|
|
@ -177,28 +242,17 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
|
|||
x_error = qp.error.ae
|
||||
y_error = getattr(qp.error, error_name)
|
||||
|
||||
# join all data, and keep the order in which the methods appeared for the first time
|
||||
data = defaultdict(lambda:{'x':np.empty(shape=(0)), 'y':np.empty(shape=(0))})
|
||||
method_order = []
|
||||
|
||||
for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
|
||||
tr_prev_i = np.repeat(tr_prev_i.reshape(1,-1), repeats=test_prevs_i.shape[0], axis=0)
|
||||
|
||||
tr_test_drifts = x_error(test_prevs_i, tr_prev_i)
|
||||
data[method]['x'] = np.concatenate([data[method]['x'], tr_test_drifts])
|
||||
|
||||
method_drifts = y_error(test_prevs_i, estim_prevs_i)
|
||||
data[method]['y'] = np.concatenate([data[method]['y'], method_drifts])
|
||||
|
||||
if method not in method_order:
|
||||
method_order.append(method)
|
||||
# get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same
|
||||
# order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to
|
||||
# x_error function) and 'y' is the estim-test shift (computed as according to y_error)
|
||||
data = __join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
|
||||
|
||||
_set_colors(ax, n_methods=len(method_order))
|
||||
|
||||
bins = np.linspace(0, 1, n_bins+1)
|
||||
inds_histogram_global = np.zeros(n_bins, dtype=np.float) # we use this to keep track of how many datapoits contribute to each bin
|
||||
binwidth = 1 / n_bins
|
||||
min_x, max_x = None, None
|
||||
min_x, max_x, min_y, max_y = None, None, None, None
|
||||
npoints = np.zeros(len(bins), dtype=float)
|
||||
for method in method_order:
|
||||
tr_test_drifts = data[method]['x']
|
||||
method_drifts = data[method]['y']
|
||||
|
|
@ -206,40 +260,194 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
|
|||
method_drifts=np.log(1+method_drifts)
|
||||
|
||||
inds = np.digitize(tr_test_drifts, bins, right=True)
|
||||
inds_histogram_global += np.histogram(tr_test_drifts, density=True, bins=bins)[0]
|
||||
|
||||
xs, ys, ystds = [], [], []
|
||||
for ind in range(len(bins)):
|
||||
for p,ind in enumerate(range(len(bins))):
|
||||
selected = inds==ind
|
||||
if selected.sum() > 0:
|
||||
xs.append(ind*binwidth)
|
||||
xs.append(ind*binwidth-binwidth/2)
|
||||
ys.append(np.mean(method_drifts[selected]))
|
||||
ystds.append(np.std(method_drifts[selected]))
|
||||
npoints[p] += len(method_drifts[selected])
|
||||
|
||||
xs = np.asarray(xs)
|
||||
ys = np.asarray(ys)
|
||||
ystds = np.asarray(ystds)
|
||||
|
||||
min_x_method, max_x_method = xs.min(), xs.max()
|
||||
min_x_method, max_x_method, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.max()
|
||||
min_x = min_x_method if min_x is None or min_x_method < min_x else min_x
|
||||
max_x = max_x_method if max_x is None or max_x_method > max_x else max_x
|
||||
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
|
||||
min_y = min_y_method if min_y is None or min_y_method < min_y else min_y
|
||||
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
|
||||
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', color='w', markersize=8, linewidth=4, zorder=1)
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, zorder=2)
|
||||
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=3, zorder=2)
|
||||
if show_std:
|
||||
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
|
||||
|
||||
# xs = bins[:-1]
|
||||
# ys = inds_histogram_global
|
||||
# print(xs.shape, ys.shape)
|
||||
# ax.errorbar(xs, ys, label='density')
|
||||
if show_density:
|
||||
ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
|
||||
max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
|
||||
|
||||
ax.set(xlabel=f'Distribution shift between training set and test sample',
|
||||
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
|
||||
title=title)
|
||||
box = ax.get_position()
|
||||
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
|
||||
if vlines:
|
||||
for vline in vlines:
|
||||
ax.axvline(vline, 0, 1, linestyle='--', color='k')
|
||||
ax.set_xlim(0, max_x)
|
||||
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
ax.set_xlim(min_x, max_x)
|
||||
|
||||
save_or_show(savepath)
|
||||
|
||||
|
||||
def brokenbar_supremacy_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, binning='isomerous',
|
||||
x_error='ae', y_error='ae', ttest_alpha=0.005, tail_density_threshold=0.005,
|
||||
method_order=None,
|
||||
savepath=None):
|
||||
assert binning in ['isomerous', 'isometric'], 'unknown binning type; valid types are "isomerous" and "isometric"'
|
||||
|
||||
x_error = getattr(qp.error, x_error)
|
||||
y_error = getattr(qp.error, y_error)
|
||||
|
||||
# get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same
|
||||
# order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to
|
||||
# x_error function) and 'y' is the estim-test shift (computed as according to y_error)
|
||||
data = __join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
|
||||
|
||||
if binning == 'isomerous':
|
||||
# take bins containing the same amount of examples
|
||||
tr_test_drifts = np.concatenate([data[m]['x'] for m in method_order])
|
||||
bins = np.quantile(tr_test_drifts, q=np.linspace(0, 1, n_bins+1)).flatten()
|
||||
else:
|
||||
# take equidistant bins
|
||||
bins = np.linspace(0, 1, n_bins+1)
|
||||
bins[0] = -0.001
|
||||
bins[-1] += 0.001
|
||||
|
||||
# we use this to keep track of how many datapoits contribute to each bin
|
||||
inds_histogram_global = np.zeros(n_bins, dtype=np.float)
|
||||
n_methods = len(method_order)
|
||||
buckets = np.zeros(shape=(n_methods, n_bins, 3))
|
||||
for i, method in enumerate(method_order):
|
||||
tr_test_drifts = data[method]['x']
|
||||
method_drifts = data[method]['y']
|
||||
|
||||
inds = np.digitize(tr_test_drifts, bins, right=False)
|
||||
inds_histogram_global += np.histogram(tr_test_drifts, density=False, bins=bins)[0]
|
||||
|
||||
for j in range(len(bins)):
|
||||
selected = inds == j
|
||||
if selected.sum() > 0:
|
||||
buckets[i, j-1, 0] = np.mean(method_drifts[selected])
|
||||
buckets[i, j-1, 1] = np.std(method_drifts[selected])
|
||||
buckets[i, j-1, 2] = selected.sum()
|
||||
|
||||
# cancel last buckets with low density
|
||||
histogram = inds_histogram_global / inds_histogram_global.sum()
|
||||
for tail in reversed(range(len(histogram))):
|
||||
if histogram[tail] < tail_density_threshold:
|
||||
buckets[:,tail,2] = 0
|
||||
else:
|
||||
break
|
||||
|
||||
salient_methods = set()
|
||||
best_methods = []
|
||||
for bucket in range(buckets.shape[1]):
|
||||
nc = buckets[:, bucket, 2].sum()
|
||||
if nc == 0:
|
||||
best_methods.append([])
|
||||
continue
|
||||
|
||||
order = np.argsort(buckets[:, bucket, 0])
|
||||
rank1 = order[0]
|
||||
best_bucket_methods = [method_order[rank1]]
|
||||
best_mean, best_std, best_nc = buckets[rank1, bucket, :]
|
||||
for method_index in order[1:]:
|
||||
method_mean, method_std, method_nc = buckets[method_index, bucket, :]
|
||||
_, pval = ttest_ind_from_stats(best_mean, best_std, best_nc, method_mean, method_std, method_nc)
|
||||
if pval > ttest_alpha:
|
||||
best_bucket_methods.append(method_order[method_index])
|
||||
best_methods.append(best_bucket_methods)
|
||||
salient_methods.update(best_bucket_methods)
|
||||
print(best_bucket_methods)
|
||||
|
||||
if binning=='isomerous':
|
||||
fig, axes = plt.subplots(2, 1, gridspec_kw={'height_ratios': [0.2, 1]}, figsize=(20, len(salient_methods)))
|
||||
else:
|
||||
fig, axes = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 1]}, figsize=(20, len(salient_methods)))
|
||||
|
||||
ax = axes[1]
|
||||
high_from = 0
|
||||
yticks, yticks_method_names = [], []
|
||||
color = get_cmap('Accent').colors
|
||||
vlines = []
|
||||
bar_high = 1
|
||||
for method in [m for m in method_order if m in salient_methods]:
|
||||
broken_paths = []
|
||||
path_start, path_end = None, None
|
||||
for i, best_bucket_methods in enumerate(best_methods):
|
||||
if method in best_bucket_methods:
|
||||
if path_start is None:
|
||||
path_start = bins[i]
|
||||
path_end = bins[i+1]-path_start
|
||||
else:
|
||||
path_end += bins[i+1]-bins[i]
|
||||
else:
|
||||
if path_start is not None:
|
||||
broken_paths.append(tuple((path_start, path_end)))
|
||||
path_start, path_end = None, None
|
||||
if path_start is not None:
|
||||
broken_paths.append(tuple((path_start, path_end)))
|
||||
|
||||
ax.broken_barh(broken_paths, (high_from, bar_high), facecolors=color[len(yticks_method_names)])
|
||||
yticks.append(high_from+bar_high/2)
|
||||
high_from += bar_high
|
||||
yticks_method_names.append(method)
|
||||
for path_start, path_end in broken_paths:
|
||||
vlines.extend([path_start, path_start+path_end])
|
||||
|
||||
vlines = np.unique(vlines)
|
||||
vlines = sorted(vlines)
|
||||
for v in vlines[1:-1]:
|
||||
ax.axvline(x=v, color='k', linestyle='--')
|
||||
|
||||
ax.set_ylim(0, high_from)
|
||||
ax.set_xlim(vlines[0], vlines[-1])
|
||||
ax.set_xlabel('Distribution shift between training set and sample')
|
||||
|
||||
ax.set_yticks(yticks)
|
||||
ax.set_yticklabels(yticks_method_names)
|
||||
|
||||
# upper plot (explaining distribution)
|
||||
ax = axes[0]
|
||||
if binning == 'isometric':
|
||||
# show the density for each region
|
||||
bins[0]=0
|
||||
y_pos = [b+(bins[i+1]-b)/2 for i,b in enumerate(bins[:-1]) if histogram[i]>0]
|
||||
bar_width = [bins[i+1]-bins[i] for i in range(len(bins[:-1])) if histogram[i]>0]
|
||||
ax.bar(y_pos, [n for n in histogram if n>0], bar_width, align='center', alpha=0.5, color='silver')
|
||||
ax.set_ylabel('shift\ndistribution', rotation=0, ha='right', va='center')
|
||||
ax.set_xlim(vlines[0], vlines[-1])
|
||||
ax.get_xaxis().set_visible(False)
|
||||
plt.subplots_adjust(wspace=0, hspace=0.1)
|
||||
else:
|
||||
# show the percentiles of the distribution
|
||||
cumsum = np.cumsum(histogram)
|
||||
for i in range(len(bins[:-1])):
|
||||
start, width = bins[i], bins[i+1]-bins[i]
|
||||
ax.broken_barh([tuple((start, width))], (0, 1), facecolors='whitesmoke' if i%2==0 else 'silver')
|
||||
if i < len(bins)-2:
|
||||
ax.text(bins[i+1], 0.5, '$P_{'+f'{int(np.round(cumsum[i]*100))}'+'}$', ha='center')
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_xlim(vlines[0], vlines[-1])
|
||||
ax.get_yaxis().set_visible(False)
|
||||
ax.get_xaxis().set_visible(False)
|
||||
plt.subplots_adjust(wspace=0, hspace=0)
|
||||
|
||||
save_or_show(savepath)
|
||||
|
||||
|
|
@ -253,3 +461,23 @@ def save_or_show(savepath):
|
|||
else:
|
||||
plt.show()
|
||||
|
||||
|
||||
def __join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order):
|
||||
data = defaultdict(lambda: {'x': np.empty(shape=(0)), 'y': np.empty(shape=(0))})
|
||||
|
||||
if method_order is None:
|
||||
method_order = []
|
||||
|
||||
for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
|
||||
tr_prev_i = np.repeat(tr_prev_i.reshape(1, -1), repeats=test_prevs_i.shape[0], axis=0)
|
||||
|
||||
tr_test_drifts = x_error(test_prevs_i, tr_prev_i)
|
||||
data[method]['x'] = np.concatenate([data[method]['x'], tr_test_drifts])
|
||||
|
||||
method_drifts = y_error(test_prevs_i, estim_prevs_i)
|
||||
data[method]['y'] = np.concatenate([data[method]['y'], method_drifts])
|
||||
|
||||
if method not in method_order:
|
||||
method_order.append(method)
|
||||
|
||||
return data
|
||||
Loading…
Reference in New Issue