1604 lines
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1604 lines
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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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<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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<li class="toctree-l1"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#submodules">Submodules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.neural">quapy.classification.neural module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.svmperf">quapy.classification.svmperf module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification">Module contents</a></li>
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</ul>
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</li>
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<li class="toctree-l1"><a class="reference internal" href="quapy.data.html">quapy.data package</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#submodules">Submodules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.base">quapy.data.base module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.datasets">quapy.data.datasets module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.preprocessing">quapy.data.preprocessing module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.reader">quapy.data.reader module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data">Module contents</a></li>
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<li class="toctree-l1"><a class="reference internal" href="quapy.method.html">quapy.method package</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.neural">quapy.method.neural module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
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</ul>
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</li>
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</ul>
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</div>
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</section>
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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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</section>
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<section id="module-quapy.error">
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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 class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.absolute_error" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="simple">
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<dt>Computes the absolute error between the two prevalence vectors.</dt><dd><p>Absolute error between two prevalence vectors <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
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<span class="math notranslate nohighlight">\(AE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}|\hat{p}(y)-p(y)|\)</span>,
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where <span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.</p>
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</dd>
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</dl>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
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<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>absolute error</p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.error.acc_error">
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<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">acc_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.acc_error" title="Permalink to this definition">¶</a></dt>
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<dd><p>Computes the error in terms of 1-accuracy. The accuracy is computed as <span class="math notranslate nohighlight">\(\frac{tp+tn}{tp+fp+fn+tn}\)</span>, with
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<cite>tp</cite>, <cite>fp</cite>, <cite>fn</cite>, and <cite>tn</cite> standing for true positives, false positives, false negatives, and true negatives,
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respectively</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>y_true</strong> – array-like of true labels</p></li>
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<li><p><strong>y_pred</strong> – array-like of predicted labels</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>1-accuracy</p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.error.acce">
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<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">acce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.acce" title="Permalink to this definition">¶</a></dt>
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<dd><p>Computes the error in terms of 1-accuracy. The accuracy is computed as <span class="math notranslate nohighlight">\(\frac{tp+tn}{tp+fp+fn+tn}\)</span>, with
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<cite>tp</cite>, <cite>fp</cite>, <cite>fn</cite>, and <cite>tn</cite> standing for true positives, false positives, false negatives, and true negatives,
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respectively</p>
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<dl class="field-list simple">
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||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>y_true</strong> – array-like of true labels</p></li>
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||
<li><p><strong>y_pred</strong> – array-like of predicted labels</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>1-accuracy</p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.ae">
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||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">ae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.ae" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="simple">
|
||
<dt>Computes the absolute error between the two prevalence vectors.</dt><dd><p>Absolute error between two prevalence vectors <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
|
||
<span class="math notranslate nohighlight">\(AE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}|\hat{p}(y)-p(y)|\)</span>,
|
||
where <span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>absolute error</p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.f1_error">
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<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">f1_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.f1_error" title="Permalink to this definition">¶</a></dt>
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<dd><p>F1 error: simply computes the error in terms of macro <span class="math notranslate nohighlight">\(F_1\)</span>, i.e., <span class="math notranslate nohighlight">\(1-F_1^M\)</span>,
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where <span class="math notranslate nohighlight">\(F_1\)</span> is the harmonic mean of precision and recall, defined as <span class="math notranslate nohighlight">\(\frac{2tp}{2tp+fp+fn}\)</span>,
|
||
with <cite>tp</cite>, <cite>fp</cite>, and <cite>fn</cite> standing for true positives, false positives, and false negatives, respectively.
|
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<cite>Macro</cite> averaging means the <span class="math notranslate nohighlight">\(F_1\)</span> is computed for each category independently, and then averaged.</p>
|
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<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>y_true</strong> – array-like of true labels</p></li>
|
||
<li><p><strong>y_pred</strong> – array-like of predicted labels</p></li>
|
||
</ul>
|
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</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p><span class="math notranslate nohighlight">\(1-F_1^M\)</span></p>
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</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
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<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.f1e">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">f1e</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.f1e" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>F1 error: simply computes the error in terms of macro <span class="math notranslate nohighlight">\(F_1\)</span>, i.e., <span class="math notranslate nohighlight">\(1-F_1^M\)</span>,
|
||
where <span class="math notranslate nohighlight">\(F_1\)</span> is the harmonic mean of precision and recall, defined as <span class="math notranslate nohighlight">\(\frac{2tp}{2tp+fp+fn}\)</span>,
|
||
with <cite>tp</cite>, <cite>fp</cite>, and <cite>fn</cite> standing for true positives, false positives, and false negatives, respectively.
|
||
<cite>Macro</cite> averaging means the <span class="math notranslate nohighlight">\(F_1\)</span> is computed for each category independently, and then averaged.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>y_true</strong> – array-like of true labels</p></li>
|
||
<li><p><strong>y_pred</strong> – array-like of predicted labels</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p><span class="math notranslate nohighlight">\(1-F_1^M\)</span></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.from_name">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">from_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">err_name</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.from_name" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Gets an error function from its name. E.g., <cite>from_name(“mae”)</cite> will return function <a class="reference internal" href="#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a></p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>err_name</strong> – string, the error name</p>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a callable implementing the requested error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.kld">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">kld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.kld" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Computes the Kullback-Leibler divergence between the two prevalence distributions.</dt><dd><p>Kullback-Leibler divergence between two prevalence distributions <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
|
||
<span class="math notranslate nohighlight">\(KLD(p,\hat{p})=D_{KL}(p||\hat{p})=\sum_{y\in \mathcal{Y}} p(y)\log\frac{p(y)}{\hat{p}(y)}\)</span>, where
|
||
<span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. KLD is not defined in cases in which the distributions contain zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>Kullback-Leibler divergence between the two distributions</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mae">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mae" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean absolute error (see <a class="reference internal" href="#quapy.error.ae" title="quapy.error.ae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.ae()</span></code></a>) across the sample pairs.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mean_absolute_error">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mean_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean absolute error (see <a class="reference internal" href="#quapy.error.ae" title="quapy.error.ae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.ae()</span></code></a>) across the sample pairs.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mean_relative_absolute_error">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mean_relative_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.mean_relative_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean relative absolute error (see <a class="reference internal" href="#quapy.error.rae" title="quapy.error.rae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.rae()</span></code></a>) across the sample pairs.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. <cite>mrae</cite> is not defined in cases in which the true distribution contains zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean relative absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mkld">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.mkld" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean Kullback-Leibler divergence (see <a class="reference internal" href="#quapy.error.kld" title="quapy.error.kld"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.kld()</span></code></a>) across the sample pairs.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. KLD is not defined in cases in which the distributions contain zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean Kullback-Leibler distribution</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mnkld">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mnkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.mnkld" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean Normalized Kullback-Leibler divergence (see <a class="reference internal" href="#quapy.error.nkld" title="quapy.error.nkld"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.nkld()</span></code></a>) across the sample pairs.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. NKLD is not defined in cases in which the distributions contain zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean Normalized Kullback-Leibler distribution</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mrae">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mrae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.mrae" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean relative absolute error (see <a class="reference internal" href="#quapy.error.rae" title="quapy.error.rae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.rae()</span></code></a>) across the sample pairs.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. <cite>mrae</cite> is not defined in cases in which the true distribution contains zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean relative absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.mse">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mse" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the mean squared error (see <a class="reference internal" href="#quapy.error.se" title="quapy.error.se"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.se()</span></code></a>) across the sample pairs.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_samples, n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>mean squared error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.nkld">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">nkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.nkld" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Computes the Normalized Kullback-Leibler divergence between the two prevalence distributions.</dt><dd><p>Normalized Kullback-Leibler divergence between two prevalence distributions <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span>
|
||
is computed as <span class="math notranslate nohighlight">\(NKLD(p,\hat{p}) = 2\frac{e^{KLD(p,\hat{p})}}{e^{KLD(p,\hat{p})}+1}-1\)</span>, where
|
||
<span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. NKLD is not defined in cases in which the distributions contain zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>Normalized Kullback-Leibler divergence between the two distributions</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.rae">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">rae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.rae" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Computes the absolute relative error between the two prevalence vectors.</dt><dd><p>Relative absolute error between two prevalence vectors <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
|
||
<span class="math notranslate nohighlight">\(RAE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}\frac{|\hat{p}(y)-p(y)|}{p(y)}\)</span>,
|
||
where <span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. <cite>rae</cite> is not defined in cases in which the true distribution contains zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>relative absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.relative_absolute_error">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">relative_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</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.error.relative_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Computes the absolute relative error between the two prevalence vectors.</dt><dd><p>Relative absolute error between two prevalence vectors <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
|
||
<span class="math notranslate nohighlight">\(RAE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}\frac{|\hat{p}(y)-p(y)|}{p(y)}\)</span>,
|
||
where <span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
|
||
The distributions are smoothed using the <cite>eps</cite> factor (see <a class="reference internal" href="#quapy.error.smooth" title="quapy.error.smooth"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.smooth()</span></code></a>).</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor. <cite>rae</cite> is not defined in cases in which the true distribution contains zeros; <cite>eps</cite>
|
||
is typically set to be <span class="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <span class="math notranslate nohighlight">\(T\)</span> the sample size. If <cite>eps=None</cite>, the sample size
|
||
will be taken from the environment variable <cite>SAMPLE_SIZE</cite> (which has thus to be set beforehand).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>relative absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.se">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">se</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.se" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Computes the squared error between the two prevalence vectors.</dt><dd><p>Squared error between two prevalence vectors <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
|
||
<span class="math notranslate nohighlight">\(SE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}(\hat{p}(y)-p(y))^2\)</span>, where
|
||
<span class="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>prevs_hat</strong> – array-like of shape <cite>(n_classes,)</cite> with the predicted prevalence values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>absolute error</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.error.smooth">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">smooth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.smooth" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Smooths a prevalence distribution with <span class="math notranslate nohighlight">\(\epsilon\)</span> (<cite>eps</cite>) as:
|
||
<span class="math notranslate nohighlight">\(\underline{p}(y)=\frac{\epsilon+p(y)}{\epsilon|\mathcal{Y}|+\displaystyle\sum_{y\in \mathcal{Y}}p(y)}\)</span></p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevs</strong> – array-like of shape <cite>(n_classes,)</cite> with the true prevalence values</p></li>
|
||
<li><p><strong>eps</strong> – smoothing factor</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>array-like of shape <cite>(n_classes,)</cite> with the smoothed distribution</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</section>
|
||
<section id="module-quapy.evaluation">
|
||
<span id="quapy-evaluation-module"></span><h2>quapy.evaluation module<a class="headerlink" href="#module-quapy.evaluation" title="Permalink to this headline">¶</a></h2>
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_prediction">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_prediction</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_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">101</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</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">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</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">None</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">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.artificial_prevalence_prediction" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Performs the predictions for all samples generated according to the Artificial Prevalence Protocol (APP).
|
||
The APP consists of exploring a grid of prevalence values containing <cite>n_prevalences</cite> points (e.g.,
|
||
[0, 0.05, 0.1, 0.15, …, 1], if <cite>n_prevalences=21</cite>), and generating all valid combinations of
|
||
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], …,
|
||
[1, 0, 0] prevalence values of size <cite>sample_size</cite> will be considered). The number of samples for each valid
|
||
combination of prevalence values is indicated by <cite>repeats</cite>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform APP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>n_prevpoints</strong> – integer, the number of different prevalences to sample (or set to None if eval_budget
|
||
is specified; default 101, i.e., steps of 1%)</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of repetitions for each prevalence (default 1)</p></li>
|
||
<li><p><strong>eval_budget</strong> – integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
|
||
there are 3 classes, <cite>repeats=1</cite>, and <cite>eval_budget=20</cite>, then <cite>n_prevpoints</cite> will be set to 5, since this
|
||
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0]) and
|
||
since setting <cite>n_prevpoints=6</cite> would produce more than 20 evaluations.</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – integer, allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>verbose</strong> – if True, shows a progress bar</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a tuple containing two <cite>np.ndarrays</cite> of shape <cite>(m,n,)</cite> with <cite>m</cite> the number of samples
|
||
<cite>(n_prevpoints*repeats)</cite> and <cite>n</cite> the number of classes. The first one contains the true prevalence values
|
||
for the samples generated while the second one contains the prevalence estimations</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_protocol">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_protocol</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_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">101</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</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">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</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">None</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_metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><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> <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.artificial_prevalence_protocol" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Generates samples according to the Artificial Prevalence Protocol (APP).
|
||
The APP consists of exploring a grid of prevalence values containing <cite>n_prevalences</cite> points (e.g.,
|
||
[0, 0.05, 0.1, 0.15, …, 1], if <cite>n_prevalences=21</cite>), and generating all valid combinations of
|
||
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], …,
|
||
[1, 0, 0] prevalence values of size <cite>sample_size</cite> will be considered). The number of samples for each valid
|
||
combination of prevalence values is indicated by <cite>repeats</cite>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform APP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>n_prevpoints</strong> – integer, the number of different prevalences to sample (or set to None if eval_budget
|
||
is specified; default 101, i.e., steps of 1%)</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of repetitions for each prevalence (default 1)</p></li>
|
||
<li><p><strong>eval_budget</strong> – integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
|
||
there are 3 classes, <cite>repeats=1</cite>, and <cite>eval_budget=20</cite>, then <cite>n_prevpoints</cite> will be set to 5, since this
|
||
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0]) and
|
||
since setting <cite>n_prevpoints=6</cite> would produce more than 20 evaluations.</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – integer, allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>error_metric</strong> – a string indicating the name of the error (as defined in <a class="reference internal" href="#module-quapy.error" title="quapy.error"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.error</span></code></a>) or a
|
||
callable error function</p></li>
|
||
<li><p><strong>verbose</strong> – set to True (default False) for displaying some information on standard output</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>yields one sample at a time</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_report">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_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_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">101</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</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">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</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">None</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.artificial_prevalence_report" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Generates an evaluation report for all samples generated according to the Artificial Prevalence Protocol (APP).
|
||
The APP consists of exploring a grid of prevalence values containing <cite>n_prevalences</cite> points (e.g.,
|
||
[0, 0.05, 0.1, 0.15, …, 1], if <cite>n_prevalences=21</cite>), and generating all valid combinations of
|
||
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], …,
|
||
[1, 0, 0] prevalence values of size <cite>sample_size</cite> will be considered). The number of samples for each valid
|
||
combination of prevalence values is indicated by <cite>repeats</cite>.
|
||
Te report takes the form of a
|
||
pandas’ <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">dataframe</a>
|
||
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
|
||
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform APP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>n_prevpoints</strong> – integer, the number of different prevalences to sample (or set to None if eval_budget
|
||
is specified; default 101, i.e., steps of 1%)</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of repetitions for each prevalence (default 1)</p></li>
|
||
<li><p><strong>eval_budget</strong> – integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
|
||
there are 3 classes, <cite>repeats=1</cite>, and <cite>eval_budget=20</cite>, then <cite>n_prevpoints</cite> will be set to 5, since this
|
||
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0]) and
|
||
since setting <cite>n_prevpoints=6</cite> would produce more than 20 evaluations.</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – integer, allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>error_metrics</strong> – a string indicating the name of the error (as defined in <a class="reference internal" href="#module-quapy.error" title="quapy.error"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.error</span></code></a>) or a
|
||
callable error function; optionally, a list of strings or callables can be indicated, if the results
|
||
are to be evaluated with more than one error metric. Default is “mae”</p></li>
|
||
<li><p><strong>verbose</strong> – if True, shows a progress bar</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>pandas’ dataframe with rows corresponding to different samples, and with columns informing of the
|
||
true prevalence values, the estimated prevalence values, and the score obtained by each of the evaluation
|
||
measures indicated.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.evaluate">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate</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_samples</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><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 class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><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></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.evaluate" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Evaluates a model on a sequence of test samples in terms of a given error metric.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test_samples</strong> – an iterable yielding one sample at a time</p></li>
|
||
<li><p><strong>error_metric</strong> – a string indicating the name of the error (as defined in <a class="reference internal" href="#module-quapy.error" title="quapy.error"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.error</span></code></a>) or a
|
||
callable error function</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>the score obtained using <cite>error_metric</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.gen_prevalence_prediction">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">gen_prevalence_prediction</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">gen_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</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.evaluation.gen_prevalence_prediction" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Generates prevalence predictions for a custom protocol defined as a generator function that yields
|
||
samples at each iteration. The sequence of samples is processed exhaustively if <cite>eval_budget=None</cite>
|
||
or up to the <cite>eval_budget</cite> iterations if specified.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>gen_fn</strong> – a generator function yielding one sample at each iteration</p></li>
|
||
<li><p><strong>eval_budget</strong> – a maximum number of evaluations to run. Set to None (default) for exploring the
|
||
entire sequence</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a tuple containing two <cite>np.ndarrays</cite> of shape <cite>(m,n,)</cite> with <cite>m</cite> the number of samples
|
||
generated and <cite>n</cite> the number of classes. The first one contains the true prevalence values
|
||
for the samples generated while the second one contains the prevalence estimations</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.gen_prevalence_report">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">gen_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">gen_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</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">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><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.gen_prevalence_report" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>GGenerates an evaluation report for a custom protocol defined as a generator function that yields
|
||
samples at each iteration. The sequence of samples is processed exhaustively if <cite>eval_budget=None</cite>
|
||
or up to the <cite>eval_budget</cite> iterations if specified.
|
||
Te report takes the form of a
|
||
pandas’ <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">dataframe</a>
|
||
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
|
||
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>gen_fn</strong> – a generator function yielding one sample at each iteration</p></li>
|
||
<li><p><strong>eval_budget</strong> – a maximum number of evaluations to run. Set to None (default) for exploring the
|
||
entire sequence</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a tuple containing two <cite>np.ndarrays</cite> of shape <cite>(m,n,)</cite> with <cite>m</cite> the number of samples
|
||
generated. The first one contains the true prevalence values
|
||
for the samples generated while the second one contains the prevalence estimations</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_prediction">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_prediction</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">repeats</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">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_prediction" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Performs the predictions for all samples generated according to the Natural Prevalence Protocol (NPP).
|
||
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
|
||
prevalence of the collection.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform NPP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of samples to generate</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>verbose</strong> – if True, shows a progress bar</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a tuple containing two <cite>np.ndarrays</cite> of shape <cite>(m,n,)</cite> with <cite>m</cite> the number of samples
|
||
<cite>(repeats)</cite> and <cite>n</cite> the number of classes. The first one contains the true prevalence values
|
||
for the samples generated while the second one contains the prevalence estimations</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_protocol">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_protocol</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">repeats</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_metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><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> <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_protocol" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Generates samples according to the Natural Prevalence Protocol (NPP).
|
||
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
|
||
prevalence of the collection.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform NPP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of samples to generate</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>error_metric</strong> – a string indicating the name of the error (as defined in <a class="reference internal" href="#module-quapy.error" title="quapy.error"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.error</span></code></a>) or a
|
||
callable error function</p></li>
|
||
<li><p><strong>verbose</strong> – if True, shows a progress bar</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>yields one sample at a time</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_report">
|
||
<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">repeats</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><p>Generates an evaluation report for all samples generated according to the Natural Prevalence Protocol (NPP).
|
||
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
|
||
prevalence of the collection.
|
||
Te report takes the form of a
|
||
pandas’ <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">dataframe</a>
|
||
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
|
||
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> – the model in charge of generating the class prevalence estimations</p></li>
|
||
<li><p><strong>test</strong> – the test set on which to perform NPP</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the size of the samples</p></li>
|
||
<li><p><strong>repeats</strong> – integer, the number of samples to generate</p></li>
|
||
<li><p><strong>n_jobs</strong> – integer, number of jobs to be run in parallel (default 1)</p></li>
|
||
<li><p><strong>random_seed</strong> – allows to replicate the samplings. The seed is local to the method and does not affect
|
||
any other random process (default 42)</p></li>
|
||
<li><p><strong>error_metrics</strong> – a string indicating the name of the error (as defined in <a class="reference internal" href="#module-quapy.error" title="quapy.error"><code class="xref py py-mod docutils literal notranslate"><span class="pre">quapy.error</span></code></a>) or a
|
||
callable error function; optionally, a list of strings or callables can be indicated, if the results
|
||
are to be evaluated with more than one error metric. Default is “mae”</p></li>
|
||
<li><p><strong>verbose</strong> – if True, shows a progress bar</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a tuple containing two <cite>np.ndarrays</cite> of shape <cite>(m,n,)</cite> with <cite>m</cite> the number of samples
|
||
<cite>(repeats)</cite> and <cite>n</cite> the number of classes. The first one contains the true prevalence values
|
||
for the samples generated while the second one contains the prevalence estimations</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</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">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">HellingerDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.HellingerDistance" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the Hellingher Distance (HD) between (discretized) distributions <cite>P</cite> and <cite>Q</cite>.
|
||
The HD for two discrete distributions of <cite>k</cite> bins is defined as:</p>
|
||
<div class="math notranslate nohighlight">
|
||
\[HD(P,Q) = \frac{ 1 }{ \sqrt{ 2 } } \sqrt{ \sum_{i=1}^k ( \sqrt{p_i} - \sqrt{q_i} )^2 }\]</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>P</strong> – real-valued array-like of shape <cite>(k,)</cite> representing a discrete distribution</p></li>
|
||
<li><p><strong>Q</strong> – real-valued array-like of shape <cite>(k,)</cite> representing a discrete distribution</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>float</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.adjusted_quantification">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">adjusted_quantification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalence_estim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.adjusted_quantification" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the
|
||
positive class <cite>p</cite> comes down to computing:</p>
|
||
<div class="math notranslate nohighlight">
|
||
\[ACC(p) = \frac{ p - fpr }{ tpr - fpr }\]</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevalence_estim</strong> – float, the estimated value for the positive class</p></li>
|
||
<li><p><strong>tpr</strong> – float, the true positive rate of the classifier</p></li>
|
||
<li><p><strong>fpr</strong> – float, the false positive rate of the classifier</p></li>
|
||
<li><p><strong>clip</strong> – set to True (default) to clip values that might exceed the range [0,1]</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>float, the adjusted count</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.artificial_prevalence_sampling">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimensions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</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">return_constrained_dim</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.functional.artificial_prevalence_sampling" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Generates vectors of prevalence values artificially drawn from an exhaustive grid of prevalence values. The
|
||
number of prevalence values explored for each dimension depends on <cite>n_prevalences</cite>, so that, if, for example,
|
||
<cite>n_prevalences=11</cite> then the prevalence values of the grid are taken from [0, 0.1, 0.2, …, 0.9, 1]. Only
|
||
valid prevalence distributions are returned, i.e., vectors of prevalence values that sum up to 1. For each
|
||
valid vector of prevalence values, <cite>repeat</cite> copies are returned. The vector of prevalence values can be
|
||
implicit (by setting <cite>return_constrained_dim=False</cite>), meaning that the last dimension (which is constrained
|
||
to 1 - sum of the rest) is not returned (note that, quite obviously, in this case the vector does not sum up to 1).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>dimensions</strong> – the number of classes</p></li>
|
||
<li><p><strong>n_prevalences</strong> – the number of equidistant prevalence points to extract from the [0,1] interval for the grid
|
||
(default is 21)</p></li>
|
||
<li><p><strong>repeat</strong> – number of copies for each valid prevalence vector (default is 1)</p></li>
|
||
<li><p><strong>return_constrained_dim</strong> – set to True to return all dimensions, or to False (default) for ommitting the
|
||
constrained dimension</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a <cite>np.ndarray</cite> of shape <cite>(n, dimensions)</cite> if <cite>return_constrained_dim=True</cite> or of shape <cite>(n, dimensions-1)</cite>
|
||
if <cite>return_constrained_dim=False</cite>, where <cite>n</cite> is the number of valid combinations found in the grid multiplied
|
||
by <cite>repeat</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.get_nprevpoints_approximation">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">get_nprevpoints_approximation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">combinations_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</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.get_nprevpoints_approximation" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Searches for the largest number of (equidistant) prevalence points to define for each of the <cite>n_classes</cite> classes so
|
||
that the number of valid prevalence values generated as combinations of prevalence points (points in a
|
||
<cite>n_classes</cite>-dimensional simplex) do not exceed combinations_budget.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>combinations_budget</strong> – integer, maximum number of combinatios allowed</p></li>
|
||
<li><p><strong>n_classes</strong> – integer, number of classes</p></li>
|
||
<li><p><strong>n_repeats</strong> – integer, number of repetitions for each prevalence combination</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>the largest number of prevalence points that generate less than combinations_budget valid prevalences</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.normalize_prevalence">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">normalize_prevalence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalences</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.normalize_prevalence" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Normalize a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
|
||
cases in which the prevalence values are not all-zeros, and to convert the prevalence values into <cite>1/n_classes</cite> in
|
||
cases in which all values are zero.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>prevalences</strong> – array-like of shape <cite>(n_classes,)</cite> or of shape <cite>(n_samples, n_classes,)</cite> with prevalence values</p>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a normalized vector or matrix of prevalence values</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.num_prevalence_combinations">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">num_prevalence_combinations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_prevpoints</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</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.num_prevalence_combinations" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if <cite>n_prevpoints</cite> equally
|
||
distant prevalence values are generated and <cite>n_repeats</cite> repetitions are requested.
|
||
The computation comes down to calculating:</p>
|
||
<div class="math notranslate nohighlight">
|
||
\[\binom{N+C-1}{C-1} \times r\]</div>
|
||
<p>where <cite>N</cite> is <cite>n_prevpoints-1</cite>, i.e., the number of probability mass blocks to allocate, <cite>C</cite> is the number of
|
||
classes, and <cite>r</cite> is <cite>n_repeats</cite>. This solution comes from the
|
||
<a class="reference external" href="https://brilliant.org/wiki/integer-equations-star-and-bars/">Stars and Bars</a> problem.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>n_classes</strong> – integer, number of classes</p></li>
|
||
<li><p><strong>n_prevpoints</strong> – integer, number of prevalence points.</p></li>
|
||
<li><p><strong>n_repeats</strong> – integer, number of repetitions for each prevalence combination</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the</p>
|
||
</dd>
|
||
</dl>
|
||
<p>number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.prevalence_from_labels">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_from_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.prevalence_from_labels" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Computed the prevalence values from a vector of labels.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>labels</strong> – array-like of shape <cite>(n_instances)</cite> with the label for each instance</p></li>
|
||
<li><p><strong>classes</strong> – the class labels. This is needed in order to correctly compute the prevalence vector even when
|
||
some classes have no examples.</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>an ndarray of shape <cite>(len(classes))</cite> with the class prevalence values</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.prevalence_from_probabilities">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_from_probabilities</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binarize</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</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.functional.prevalence_from_probabilities" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Returns a vector of prevalence values from a matrix of posterior probabilities.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>posteriors</strong> – array-like of shape <cite>(n_instances, n_classes,)</cite> with posterior probabilities for each class</p></li>
|
||
<li><p><strong>binarize</strong> – set to True (default is False) for computing the prevalence values on crisp decisions (i.e.,
|
||
converting the vectors of posterior probabilities into class indices, by taking the argmax).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>array of shape <cite>(n_classes,)</cite> containing the prevalence values</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.prevalence_linspace">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_linspace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</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">smooth_limits_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.prevalence_linspace" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Produces an array of uniformly separated values of prevalence.
|
||
By default, produces an array of 21 prevalence values, with
|
||
step 0.05 and with the limits smoothed, i.e.:
|
||
[0.01, 0.05, 0.10, 0.15, …, 0.90, 0.95, 0.99]</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>n_prevalences</strong> – the number of prevalence values to sample from the [0,1] interval (default 21)</p></li>
|
||
<li><p><strong>repeats</strong> – number of times each prevalence is to be repeated (defaults to 1)</p></li>
|
||
<li><p><strong>smooth_limits_epsilon</strong> – the quantity to add and subtract to the limits 0 and 1</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>an array of uniformly separated prevalence values</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.strprev">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">strprev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.strprev" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Returns a string representation for a prevalence vector. E.g.,</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">strprev</span><span class="p">([</span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">],</span> <span class="n">prec</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="s1">'[0.33, 0.67]'</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>prevalences</strong> – a vector of prevalence values</p></li>
|
||
<li><p><strong>prec</strong> – float precision</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>string</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.uniform_prevalence_sampling">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_prevalence_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_prevalence_sampling" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Implements the <a class="reference external" href="http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf">Kraemer algorithm</a>
|
||
for sampling uniformly at random from the unit simplex. This implementation is adapted from this
|
||
<cite>post <https://cs.stackexchange.com/questions/3227/uniform-sampling-from-a-simplex>_</cite>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>n_classes</strong> – integer, number of classes (dimensionality of the simplex)</p></li>
|
||
<li><p><strong>size</strong> – number of samples to return</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(size, n_classes,)</cite> if <cite>size>1</cite>, or of shape <cite>(n_classes,)</cite> otherwise</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.functional.uniform_simplex_sampling">
|
||
<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><p>Implements the <a class="reference external" href="http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf">Kraemer algorithm</a>
|
||
for sampling uniformly at random from the unit simplex. This implementation is adapted from this
|
||
<cite>post <https://cs.stackexchange.com/questions/3227/uniform-sampling-from-a-simplex>_</cite>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>n_classes</strong> – integer, number of classes (dimensionality of the simplex)</p></li>
|
||
<li><p><strong>size</strong> – number of samples to return</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(size, n_classes,)</cite> if <cite>size>1</cite>, or of shape <cite>(n_classes,)</cite> otherwise</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</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">
|
||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.model_selection.</span></span><span class="sig-name descname"><span class="pre">GridSearchQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">model:</span> <span class="pre">quapy.method.base.BaseQuantifier,</span> <span class="pre">param_grid:</span> <span class="pre">dict,</span> <span class="pre">sample_size:</span> <span class="pre">Optional[int],</span> <span class="pre">protocol='app',</span> <span class="pre">n_prevpoints:</span> <span class="pre">Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">n_repetitions:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">eval_budget:</span> <span class="pre">Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">error:</span> <span class="pre">Union[Callable,</span> <span class="pre">str]</span> <span class="pre">=</span> <span class="pre"><function</span> <span class="pre">mae>,</span> <span class="pre">refit=True,</span> <span class="pre">val_split=0.4,</span> <span class="pre">n_jobs=1,</span> <span class="pre">random_seed=42,</span> <span class="pre">timeout=-1,</span> <span class="pre">verbose=False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
|
||
<p>Grid Search optimization targeting a quantification-oriented metric.</p>
|
||
<p>Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation
|
||
protocol for quantification.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>model</strong> (<a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><em>BaseQuantifier</em></a>) – the quantifier to optimize</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with keys the parameter names and values the list of values to explore</p></li>
|
||
<li><p><strong>sample_size</strong> – the size of the samples to extract from the validation set (ignored if protocl=’gen’)</p></li>
|
||
<li><p><strong>protocol</strong> – either ‘app’ for the artificial prevalence protocol, ‘npp’ for the natural prevalence
|
||
protocol, or ‘gen’ for using a custom sampling generator function</p></li>
|
||
<li><p><strong>n_prevpoints</strong> – if specified, indicates the number of equally distant points to extract from the interval
|
||
[0,1] in order to define the prevalences of the samples; e.g., if n_prevpoints=5, then the prevalences for
|
||
each class will be explored in [0.00, 0.25, 0.50, 0.75, 1.00]. If not specified, then eval_budget is requested.
|
||
Ignored if protocol!=’app’.</p></li>
|
||
<li><p><strong>n_repetitions</strong> – the number of repetitions for each combination of prevalences. This parameter is ignored
|
||
for the protocol=’app’ if eval_budget is set and is lower than the number of combinations that would be
|
||
generated using the value assigned to n_prevpoints (for the current number of classes and n_repetitions).
|
||
Ignored for protocol=’npp’ and protocol=’gen’ (use eval_budget for setting a maximum number of samples in
|
||
those cases).</p></li>
|
||
<li><p><strong>eval_budget</strong> – if specified, sets a ceil on the number of evaluations to perform for each hyper-parameter
|
||
combination. For example, if protocol=’app’, there are 3 classes, n_repetitions=1 and eval_budget=20, then
|
||
n_prevpoints will be set to 5, since this will generate 15 different prevalences, i.e., [0, 0, 1],
|
||
[0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0], and since setting it to 6 would generate more than
|
||
20. When protocol=’gen’, indicates the maximum number of samples to generate, but less samples will be
|
||
generated if the generator yields less samples.</p></li>
|
||
<li><p><strong>error</strong> – an error function (callable) or a string indicating the name of an error function (valid ones
|
||
are those in qp.error.QUANTIFICATION_ERROR</p></li>
|
||
<li><p><strong>refit</strong> – whether or not to refit the model on the whole labelled collection (training+validation) with
|
||
the best chosen hyperparameter combination. Ignored if protocol=’gen’</p></li>
|
||
<li><p><strong>val_split</strong> – either a LabelledCollection on which to test the performance of the different settings, or
|
||
a float in [0,1] indicating the proportion of labelled data to extract from the training set, or a callable
|
||
returning a generator function each time it is invoked (only for protocol=’gen’).</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel jobs</p></li>
|
||
<li><p><strong>random_seed</strong> – set the seed of the random generator to replicate experiments. Ignored if protocol=’gen’.</p></li>
|
||
<li><p><strong>timeout</strong> – establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
|
||
Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
|
||
being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.</p></li>
|
||
<li><p><strong>verbose</strong> – set to True to get information through the stdout</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.best_model">
|
||
<span class="sig-name descname"><span class="pre">best_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.best_model" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Returns the best model found after calling the <a class="reference internal" href="#quapy.model_selection.GridSearchQ.fit" title="quapy.model_selection.GridSearchQ.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code></a> method, i.e., the one trained on the combination
|
||
of hyper-parameters that minimized the error function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns</dt>
|
||
<dd class="field-odd"><p>a trained quantifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.classes_">
|
||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.classes_" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Classes on which the quantifier has been trained on.
|
||
:return: a ndarray of shape <cite>(n_classes)</cite> with the class identifiers</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</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">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><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 class="p"><span class="pre">,</span> </span><span class="pre">float</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">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.fit" title="Permalink to this definition">¶</a></dt>
|
||
<dd><dl class="simple">
|
||
<dt>Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</dt><dd><p>the error metric.</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>training</strong> – the training set on which to optimize the hyperparameters</p></li>
|
||
<li><p><strong>val_split</strong> – either a LabelledCollection on which to test the performance of the different settings, or
|
||
a float in [0,1] indicating the proportion of labelled data to extract from the training set</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.get_params" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Returns the dictionary of hyper-parameters to explore (<cite>param_grid</cite>)</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> – Unused</p>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>the dictionary <cite>param_grid</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.quantify" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Estimate class prevalence values using the best model found after calling the <a class="reference internal" href="#quapy.model_selection.GridSearchQ.fit" title="quapy.model_selection.GridSearchQ.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code></a> method.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – sample contanining the instances</p>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>a ndarray of shape <cite>(n_classes)</cite> with class prevalence estimates as according to the best model found
|
||
by the model selection process.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</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">parameters</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.set_params" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Sets the hyper-parameters to explore.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>parameters</strong> – a dictionary with keys the parameter names and values the list of values to explore</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
</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><p>Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)
|
||
for different bins of (true) prevalence of the positive classs, for each quantification method.</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>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> – whether or not to add secondary grid (default is False)</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><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>, <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><p>Displays (only) the top performing methods for different regions of the train-test shift in form of a broken
|
||
bar chart, in which each method has bars only for those regions in which either one of the following conditions
|
||
hold: (i) it is the best method (in average) for the bin, or (ii) it is not statistically significantly different
|
||
(in average) as according to a two-sided t-test on independent samples at confidence <cite>ttest_alpha</cite>.
|
||
The binning can be made “isometric” (same size), or “isomerous” (same number of experiments – default). A second
|
||
plot is displayed on top, that displays the distribution of experiments for each bin (when binning=”isometric”) or
|
||
the percentiles points of the distribution (when binning=”isomerous”).</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>tr_prevs</strong> – training prevalence of each experiment</p></li>
|
||
<li><p><strong>n_bins</strong> – number of bins in which the y-axis is to be divided (default is 20)</p></li>
|
||
<li><p><strong>binning</strong> – type of binning, either “isomerous” (default) or “isometric”</p></li>
|
||
<li><p><strong>x_error</strong> – a string representing the name of an error function (as defined in <cite>quapy.error</cite>) to be used for
|
||
measuring the amount of train-test shift (default is “ae”)</p></li>
|
||
<li><p><strong>y_error</strong> – a string representing the name of an error function (as defined in <cite>quapy.error</cite>) to be used for
|
||
measuring the amount of error in the prevalence estimations (default is “ae”)</p></li>
|
||
<li><p><strong>ttest_alpha</strong> – the confidence interval above which a p-value (two-sided t-test on independent samples) is
|
||
to be considered as an indicator that the two means are not statistically significantly different. Default is
|
||
0.005, meaning that a <cite>p-value > 0.005</cite> indicates the two methods involved are to be considered similar</p></li>
|
||
<li><p><strong>tail_density_threshold</strong> – sets a threshold on the density of experiments (over the total number of experiments)
|
||
below which a bin in the tail (i.e., the right-most ones) will be discarded. This is in order to avoid some
|
||
bins to be shown for train-test outliers.</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>
|
||
<li><p><strong>savepath</strong> – path where to save the plot. If not indicated (as default), the plot is shown.</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p></p>
|
||
</dd>
|
||
</dl>
|
||
</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">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">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>, <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>
|
||
<dd><p>Plots the error (along the x-axis, as measured in terms of <cite>error_name</cite>) as a function of the train-test shift
|
||
(along the y-axis, as measured in terms of <a class="reference internal" href="#quapy.error.ae" title="quapy.error.ae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.ae()</span></code></a>). This plot is useful especially for multiclass
|
||
problems, in which “diagonal plots” may be cumbersone, and in order to gain understanding about how methods
|
||
fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the
|
||
high-shift regime).</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>tr_prevs</strong> – training prevalence of each experiment</p></li>
|
||
<li><p><strong>n_bins</strong> – number of bins in which the y-axis is to be divided (default is 20)</p></li>
|
||
<li><p><strong>error_name</strong> – a string representing the name of an error function (as defined in <cite>quapy.error</cite>, default is “ae”)</p></li>
|
||
<li><p><strong>show_std</strong> – whether or not to show standard deviations as color bands (default is False)</p></li>
|
||
<li><p><strong>show_density</strong> – whether or not to display the distribution of experiments for each bin (default is True)</p></li>
|
||
<li><p><strong>logscale</strong> – whether or not to log-scale the y-error measure (default is False)</p></li>
|
||
<li><p><strong>title</strong> – title of the plot (default is “Quantification error as a function of distribution shift”)</p></li>
|
||
<li><p><strong>vlines</strong> – array-like list of values (default is None). If indicated, highlights some regions of the space
|
||
using vertical dotted lines.</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>
|
||
<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>
|
||
|
||
</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">
|
||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">EarlyStop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">patience</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.EarlyStop" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||
<p>A class implementing the early-stopping condition typically used for training neural networks.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>patience</strong> – the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a</p>
|
||
</dd>
|
||
</dl>
|
||
<p>held-out validation split) can be found to be worse than the best one obtained so far, before flagging the
|
||
stopping condition. An instance of this class is <cite>callable</cite>, and is to be used as follows:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">earlystop</span> <span class="o">=</span> <span class="n">EarlyStop</span><span class="p">(</span><span class="n">patience</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="c1"># is True</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="o">.</span><span class="n">STOP</span> <span class="c1"># is False (patience=1)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="o">.</span><span class="n">STOP</span> <span class="c1"># is True (patience=0)</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="o">.</span><span class="n">best_epoch</span> <span class="c1"># is 1</span>
|
||
<span class="gp">>>> </span><span class="n">earlystop</span><span class="o">.</span><span class="n">best_score</span> <span class="c1"># is 0.7</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>lower_is_better</strong> – if True (default) the metric is to be minimized.</p>
|
||
</dd>
|
||
<dt class="field-even">Variables</dt>
|
||
<dd class="field-even"><ul class="simple">
|
||
<li><p><strong>best_score</strong> – keeps track of the best value seen so far</p></li>
|
||
<li><p><strong>best_epoch</strong> – keeps track of the epoch in which the best score was set</p></li>
|
||
<li><p><strong>STOP</strong> – flag (boolean) indicating the stopping condition</p></li>
|
||
<li><p><strong>IMPROVED</strong> – flag (boolean) indicating whether there was an improvement in the last call</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.create_if_not_exist">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">create_if_not_exist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.create_if_not_exist" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>An alias to <cite>os.makedirs(path, exist_ok=True)</cite> that also returns the path. This is useful in cases like, e.g.:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">path</span> <span class="o">=</span> <span class="n">create_if_not_exist</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">dir</span><span class="p">,</span> <span class="n">subdir</span><span class="p">,</span> <span class="n">anotherdir</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>path</strong> – path to create</p>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>the path itself</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.create_parent_dir">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">create_parent_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.create_parent_dir" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Creates the parent dir (if any) of a given path, if not exists. E.g., for <cite>./path/to/file.txt</cite>, the path <cite>./path/to</cite>
|
||
is created.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>path</strong> – the path</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.download_file">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">download_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">archive_filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.download_file" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Downloads a file from a url</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>url</strong> – the url</p></li>
|
||
<li><p><strong>archive_filename</strong> – destination filename</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.download_file_if_not_exists">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">download_file_if_not_exists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">archive_filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.download_file_if_not_exists" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Dowloads a function (using <a class="reference internal" href="#quapy.util.download_file" title="quapy.util.download_file"><code class="xref py py-meth docutils literal notranslate"><span class="pre">download_file()</span></code></a>) if the file does not exist.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>url</strong> – the url</p></li>
|
||
<li><p><strong>archive_filename</strong> – destination filename</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.get_quapy_home">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">get_quapy_home</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.get_quapy_home" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns</dt>
|
||
<dd class="field-odd"><p>a string representing the path</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.map_parallel">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">map_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.map_parallel" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then
|
||
func is applied in two parallel processes to args[0:50] and to args[50:99]</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>func</strong> – function to be parallelized</p></li>
|
||
<li><p><strong>args</strong> – array-like of arguments to be passed to the function in different parallel calls</p></li>
|
||
<li><p><strong>n_jobs</strong> – the number of workers</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.parallel">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.parallel" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>A wrapper of multiprocessing:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
|
||
<span class="gp">>>> </span> <span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="n">args</span>
|
||
<span class="gp">>>> </span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>that takes the <cite>quapy.environ</cite> variable as input silently</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.pickled_resource">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">pickled_resource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pickle_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generation_func</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.pickled_resource" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Allows for fast reuse of resources that are generated only once by calling generation_func(<a href="#id4"><span class="problematic" id="id5">*</span></a>args). The next times
|
||
this function is invoked, it loads the pickled resource. Example:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">some_array</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> <span class="c1"># a mock resource created with one parameter (`n`)</span>
|
||
<span class="gp">>>> </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">pickled_resource</span><span class="p">(</span><span class="s1">'./my_array.pkl'</span><span class="p">,</span> <span class="n">some_array</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># the resource does not exist: it is created by calling some_array(10)</span>
|
||
<span class="gp">>>> </span><span class="n">pickled_resource</span><span class="p">(</span><span class="s1">'./my_array.pkl'</span><span class="p">,</span> <span class="n">some_array</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># the resource exists; it is loaded from './my_array.pkl'</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>pickle_path</strong> – the path where to save (first time) and load (next times) the resource</p></li>
|
||
<li><p><strong>generation_func</strong> – the function that generates the resource, in case it does not exist in pickle_path</p></li>
|
||
<li><p><strong>args</strong> – any arg that generation_func uses for generating the resources</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p>the resource</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.save_text_file">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">save_text_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.save_text_file" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Saves a text file to disk, given its full path, and creates the parent directory if missing.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>path</strong> – path where to save the path.</p></li>
|
||
<li><p><strong>text</strong> – text to save.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.util.temp_seed">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">temp_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.temp_seed" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Can be used in a “with” context to set a temporal seed without modifying the outer numpy’s current state. E.g.:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="n">temp_seed</span><span class="p">(</span><span class="n">random_seed</span><span class="p">):</span>
|
||
<span class="gp">>>> </span> <span class="k">pass</span> <span class="c1"># do any computation depending on np.random functionality</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><p><strong>seed</strong> – the seed to set within the “with” context</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</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>
|
||
|
||
</section>
|
||
</section>
|
||
|
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<h3><a href="index.html">Table of Contents</a></h3>
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<li><a class="reference internal" href="#">quapy package</a><ul>
|
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|
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|
||
<li><a class="reference internal" href="#module-quapy.error">quapy.error module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy.evaluation">quapy.evaluation module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy.functional">quapy.functional module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy.model_selection">quapy.model_selection module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy.plot">quapy.plot module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy.util">quapy.util module</a></li>
|
||
<li><a class="reference internal" href="#module-quapy">Module contents</a></li>
|
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|
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