<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">absolute_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.absolute_error"title="Permalink to this definition">¶</a></dt>
<dd><dlclass="simple">
<dt>Computes the absolute error between the two prevalence vectors.</dt><dd><p>Absolute error between two prevalence vectors <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">acc_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">y_true</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_pred</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.acc_error"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the error in terms of 1-accuracy. The accuracy is computed as <spanclass="math notranslate nohighlight">\(\frac{tp+tn}{tp+fp+fn+tn}\)</span>, with
<cite>tp</cite>, <cite>fp</cite>, <cite>fn</cite>, and <cite>tn</cite> standing for true positives, false positives, false negatives, and true negatives,
respectively</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">acce</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">y_true</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_pred</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.acce"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the error in terms of 1-accuracy. The accuracy is computed as <spanclass="math notranslate nohighlight">\(\frac{tp+tn}{tp+fp+fn+tn}\)</span>, with
<cite>tp</cite>, <cite>fp</cite>, <cite>fn</cite>, and <cite>tn</cite> standing for true positives, false positives, false negatives, and true negatives,
respectively</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">ae</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.ae"title="Permalink to this definition">¶</a></dt>
<dd><dlclass="simple">
<dt>Computes the absolute error between the two prevalence vectors.</dt><dd><p>Absolute error between two prevalence vectors <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">f1_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">y_true</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_pred</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.f1_error"title="Permalink to this definition">¶</a></dt>
<dd><p>F1 error: simply computes the error in terms of macro <spanclass="math notranslate nohighlight">\(F_1\)</span>, i.e., <spanclass="math notranslate nohighlight">\(1-F_1^M\)</span>,
where <spanclass="math notranslate nohighlight">\(F_1\)</span> is the harmonic mean of precision and recall, defined as <spanclass="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 <spanclass="math notranslate nohighlight">\(F_1\)</span> is computed for each category independently, and then averaged.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">f1e</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">y_true</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_pred</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.f1e"title="Permalink to this definition">¶</a></dt>
<dd><p>F1 error: simply computes the error in terms of macro <spanclass="math notranslate nohighlight">\(F_1\)</span>, i.e., <spanclass="math notranslate nohighlight">\(1-F_1^M\)</span>,
where <spanclass="math notranslate nohighlight">\(F_1\)</span> is the harmonic mean of precision and recall, defined as <spanclass="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 <spanclass="math notranslate nohighlight">\(F_1\)</span> is computed for each category independently, and then averaged.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">from_name</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">err_name</span></span></em><spanclass="sig-paren">)</span><aclass="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 <aclass="reference internal"href="#quapy.error.mae"title="quapy.error.mae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.mae()</span></code></a></p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>err_name</strong>– string, the error name</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>a callable implementing the requested error</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">kld</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.kld"title="Permalink to this definition">¶</a></dt>
<dt>Computes the Kullback-Leibler divergence between the two prevalence distributions.</dt><dd><p>Kullback-Leibler divergence between two prevalence distributions <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
<spanclass="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
<spanclass="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
</dd>
</dl>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>Kullback-Leibler divergence between the two distributions</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mae</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mae"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean absolute error (see <aclass="reference internal"href="#quapy.error.ae"title="quapy.error.ae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.ae()</span></code></a>) across the sample pairs.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mean_absolute_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mean_absolute_error"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean absolute error (see <aclass="reference internal"href="#quapy.error.ae"title="quapy.error.ae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.ae()</span></code></a>) across the sample pairs.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mean_relative_absolute_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mean_relative_absolute_error"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean relative absolute error (see <aclass="reference internal"href="#quapy.error.rae"title="quapy.error.rae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.rae()</span></code></a>) across the sample pairs.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mkld</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mkld"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean Kullback-Leibler divergence (see <aclass="reference internal"href="#quapy.error.kld"title="quapy.error.kld"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.kld()</span></code></a>) across the sample pairs.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mnkld</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mnkld"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean Normalized Kullback-Leibler divergence (see <aclass="reference internal"href="#quapy.error.nkld"title="quapy.error.nkld"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.nkld()</span></code></a>) across the sample pairs.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mrae</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mrae"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean relative absolute error (see <aclass="reference internal"href="#quapy.error.rae"title="quapy.error.rae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.rae()</span></code></a>) across the sample pairs.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">mse</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prevs_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.mse"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean squared error (see <aclass="reference internal"href="#quapy.error.se"title="quapy.error.se"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.se()</span></code></a>) across the sample pairs.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">nkld</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.nkld"title="Permalink to this definition">¶</a></dt>
<dt>Computes the Normalized Kullback-Leibler divergence between the two prevalence distributions.</dt><dd><p>Normalized Kullback-Leibler divergence between two prevalence distributions <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span>
is computed as <spanclass="math notranslate nohighlight">\(NKLD(p,\hat{p}) = 2\frac{e^{KLD(p,\hat{p})}}{e^{KLD(p,\hat{p})}+1}-1\)</span>, where
<spanclass="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
</dd>
</dl>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>Normalized Kullback-Leibler divergence between the two distributions</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">rae</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.rae"title="Permalink to this definition">¶</a></dt>
<dt>Computes the absolute relative error between the two prevalence vectors.</dt><dd><p>Relative absolute error between two prevalence vectors <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
where <spanclass="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
</dd>
</dl>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">relative_absolute_error</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.relative_absolute_error"title="Permalink to this definition">¶</a></dt>
<dt>Computes the absolute relative error between the two prevalence vectors.</dt><dd><p>Relative absolute error between two prevalence vectors <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
where <spanclass="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.
The distributions are smoothed using the <cite>eps</cite> factor (see <aclass="reference internal"href="#quapy.error.smooth"title="quapy.error.smooth"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.smooth()</span></code></a>).</p>
</dd>
</dl>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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 <spanclass="math notranslate nohighlight">\(\frac{1}{2T}\)</span>, with <spanclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">se</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">p</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">p_hat</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.se"title="Permalink to this definition">¶</a></dt>
<dt>Computes the squared error between the two prevalence vectors.</dt><dd><p>Squared error between two prevalence vectors <spanclass="math notranslate nohighlight">\(p\)</span> and <spanclass="math notranslate nohighlight">\(\hat{p}\)</span> is computed as
<spanclass="math notranslate nohighlight">\(SE(p,\hat{p})=\frac{1}{|\mathcal{Y}|}\sum_{y\in \mathcal{Y}}(\hat{p}(y)-p(y))^2\)</span>, where
<spanclass="math notranslate nohighlight">\(\mathcal{Y}\)</span> are the classes of interest.</p>
</dd>
</dl>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.error.</span></span><spanclass="sig-name descname"><spanclass="pre">smooth</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eps</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.error.smooth"title="Permalink to this definition">¶</a></dt>
<dd><p>Smooths a prevalence distribution with <spanclass="math notranslate nohighlight">\(\epsilon\)</span> (<cite>eps</cite>) as:
<spanid="quapy-evaluation-module"></span><h2>quapy.evaluation module<aclass="headerlink"href="#module-quapy.evaluation"title="Permalink to this headline">¶</a></h2>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.evaluation.</span></span><spanclass="sig-name descname"><spanclass="pre">gen_prevalence_prediction</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">model</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><aclass="reference internal"href="quapy.method.html#quapy.method.base.BaseQuantifier"title="quapy.method.base.BaseQuantifier"><spanclass="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">gen_fn</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">Callable</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">eval_budget</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.evaluation.gen_prevalence_prediction"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.evaluation.</span></span><spanclass="sig-name descname"><spanclass="pre">natural_prevalence_report</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">model</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><aclass="reference internal"href="quapy.method.html#quapy.method.base.BaseQuantifier"title="quapy.method.base.BaseQuantifier"><spanclass="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">test</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">sample_size</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_repetitions</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">random_seed</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">42</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">error_metrics</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">Iterable</span><spanclass="p"><spanclass="pre">[</span></span><spanclass="pre">Union</span><spanclass="p"><spanclass="pre">[</span></span><spanclass="pre">str</span><spanclass="p"><spanclass="pre">,</span></span><spanclass="pre">Callable</span><spanclass="p"><spanclass="pre">]</span></span><spanclass="p"><spanclass="pre">]</span></span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">'mae'</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">verbose</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">False</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.evaluation.natural_prevalence_report"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
<divclass="section"id="module-quapy.functional">
<spanid="quapy-functional-module"></span><h2>quapy.functional module<aclass="headerlink"href="#module-quapy.functional"title="Permalink to this headline">¶</a></h2>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">HellingerDistance</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">P</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">Q</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.HellingerDistance"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">adjusted_quantification</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevalence_estim</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">clip</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.adjusted_quantification"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">artificial_prevalence_sampling</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">dimensions</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_prevalences</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">21</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">repeat</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">return_constrained_dim</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">False</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.artificial_prevalence_sampling"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">get_nprevpoints_approximation</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">combinations_budget</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_classes</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_repeats</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="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 n_classes classes so that
the number of valid prevalences generated as combinations of prevalence points (points in a n_classes-dimensional
simplex) do not exceed combinations_budget.
:param n_classes: number of classes
:param n_repeats: number of repetitions for each prevalence combination
:param combinations_budget: maximum number of combinatios allowed
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">normalize_prevalence</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevalences</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.normalize_prevalence"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">num_prevalence_combinations</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">n_prevpoints</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_classes</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_repeats</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">int</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.num_prevalence_combinations"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the number of prevalence combinations in the n_classes-dimensional simplex if nprevpoints equally distant
prevalences are generated and n_repeats repetitions are requested
:param n_classes: number of classes
:param n_prevpoints: number of prevalence points.
:param n_repeats: number of repetitions for each prevalence combination
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">prevalence_from_labels</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">labels</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">classes_</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.prevalence_from_labels"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">prevalence_from_probabilities</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posteriors</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">binarize</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">bool</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">False</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.prevalence_from_probabilities"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">prevalence_linspace</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">n_prevalences</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">21</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">repeat</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">smooth_limits_epsilon</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">0.01</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.prevalence_linspace"title="Permalink to this definition">¶</a></dt>
<dd><p>Produces a uniformly separated values of prevalence. By default, produces an array 21 prevalences, 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]
:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
:param repeat: number of times each prevalence is to be repeated (defaults to 1)
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
:return: an array of uniformly separated prevalence values</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">strprev</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">prevalences</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">prec</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">3</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.strprev"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">uniform_prevalence_sampling</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">n_classes</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">size</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.uniform_prevalence_sampling"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.functional.</span></span><spanclass="sig-name descname"><spanclass="pre">uniform_simplex_sampling</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">n_classes</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">size</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.functional.uniform_simplex_sampling"title="Permalink to this definition">¶</a></dt>
<spanid="quapy-model-selection-module"></span><h2>quapy.model_selection module<aclass="headerlink"href="#module-quapy.model_selection"title="Permalink to this headline">¶</a></h2>
<emclass="property"><spanclass="pre">class</span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.model_selection.</span></span><spanclass="sig-name descname"><spanclass="pre">GridSearchQ</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="pre">model:</span><spanclass="pre">quapy.method.base.BaseQuantifier,</span><spanclass="pre">param_grid:</span><spanclass="pre">dict,</span><spanclass="pre">sample_size:</span><spanclass="pre">Optional[int],</span><spanclass="pre">protocol='app',</span><spanclass="pre">n_prevpoints:</span><spanclass="pre">Optional[int]</span><spanclass="pre">=</span><spanclass="pre">None,</span><spanclass="pre">n_repetitions:</span><spanclass="pre">int</span><spanclass="pre">=</span><spanclass="pre">1,</span><spanclass="pre">eval_budget:</span><spanclass="pre">Optional[int]</span><spanclass="pre">=</span><spanclass="pre">None,</span><spanclass="pre">error:</span><spanclass="pre">Union[Callable,</span><spanclass="pre">str]</span><spanclass="pre">=</span><spanclass="pre"><function</span><spanclass="pre">mae>,</span><spanclass="pre">refit=True,</span><spanclass="pre">val_split=0.4,</span><spanclass="pre">n_jobs=1,</span><spanclass="pre">random_seed=42,</span><spanclass="pre">timeout=-1,</span><spanclass="pre">verbose=False</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ"title="Permalink to this definition">¶</a></dt>
<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>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>model</strong> (<aclass="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>
<spanclass="sig-name descname"><spanclass="pre">best_model</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ.best_model"title="Permalink to this definition">¶</a></dt>
<emclass="property"><spanclass="pre">property</span></em><spanclass="sig-name descname"><spanclass="pre">classes_</span></span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ.classes_"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">training</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">Optional</span><spanclass="p"><spanclass="pre">[</span></span><spanclass="pre">Union</span><spanclass="p"><spanclass="pre">[</span></span><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">quapy.data.base.LabelledCollection</span></a><spanclass="p"><spanclass="pre">,</span></span><spanclass="pre">float</span><spanclass="p"><spanclass="pre">,</span></span><spanclass="pre">Callable</span><spanclass="p"><spanclass="pre">]</span></span><spanclass="p"><spanclass="pre">]</span></span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ.fit"title="Permalink to this definition">¶</a></dt>
<dd><dlclass="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>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="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>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="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>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ.quantify"title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate class prevalence values</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>instances</strong>– sample contanining the instances</p>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">parameters</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.model_selection.GridSearchQ.set_params"title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the hyper-parameters to explore.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="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>
</div>
<divclass="section"id="module-quapy.plot">
<spanid="quapy-plot-module"></span><h2>quapy.plot module<aclass="headerlink"href="#module-quapy.plot"title="Permalink to this headline">¶</a></h2>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.plot.</span></span><spanclass="sig-name descname"><spanclass="pre">save_or_show</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">savepath</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.plot.save_or_show"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
<divclass="section"id="module-quapy.util">
<spanid="quapy-util-module"></span><h2>quapy.util module<aclass="headerlink"href="#module-quapy.util"title="Permalink to this headline">¶</a></h2>
<emclass="property"><spanclass="pre">class</span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">EarlyStop</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">patience</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">lower_is_better</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.EarlyStop"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">create_if_not_exist</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">path</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.create_if_not_exist"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">create_parent_dir</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">path</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.create_parent_dir"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">download_file</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">url</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">archive_filename</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.download_file"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">download_file_if_not_exists</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">url</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">archive_path</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.download_file_if_not_exists"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">get_quapy_home</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.get_quapy_home"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">map_parallel</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">func</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">args</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span></em><spanclass="sig-paren">)</span><aclass="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>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">parallel</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">func</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">args</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.parallel"title="Permalink to this definition">¶</a></dt>
<dd><p>A wrapper of multiprocessing:
Parallel(n_jobs=n_jobs)(</p>
<blockquote>
<div><p>delayed(func)(args_i) for args_i in args</p>
</div></blockquote>
<p>)
that takes the quapy.environ variable as input silently</p>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">pickled_resource</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">pickle_path</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">str</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">generation_func</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="n"><spanclass="pre">callable</span></span></em>, <emclass="sig-param"><spanclass="o"><spanclass="pre">*</span></span><spanclass="n"><spanclass="pre">args</span></span></em><spanclass="sig-paren">)</span><aclass="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(<ahref="#id1"><spanclass="problematic"id="id2">*</span></a>args). The next times
this function is invoked, it loads the pickled resource. Example:
def some_array(n):</p>
<blockquote>
<div><p>return np.random.rand(n)</p>
</div></blockquote>
<p>pickled_resource(‘./my_array.pkl’, some_array, 10) # the resource does not exist: it is created by some_array(10)
pickled_resource(‘./my_array.pkl’, some_array, 10) # the resource exists: it is loaded from ‘./my_array.pkl’
:param pickle_path: the path where to save (first time) and load (next times) the resource
:param generation_func: the function that generates the resource, in case it does not exist in pickle_path
:param args: any arg that generation_func uses for generating the resources
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">save_text_file</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">path</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">text</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.util.save_text_file"title="Permalink to this definition">¶</a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.util.</span></span><spanclass="sig-name descname"><spanclass="pre">temp_seed</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">seed</span></span></em><spanclass="sig-paren">)</span><aclass="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.:
with temp_seed(random_seed):</p>
<blockquote>
<div><p># do any computation depending on np.random functionality</p>
</div></blockquote>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>seed</strong>– the seed to set within the “with” context</p>
</dd>
</dl>
</dd></dl>
</div>
<divclass="section"id="module-quapy">
<spanid="module-contents"></span><h2>Module contents<aclass="headerlink"href="#module-quapy"title="Permalink to this headline">¶</a></h2>
<dlclass="py function">
<dtclass="sig sig-object py"id="quapy.isbinary">
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.</span></span><spanclass="sig-name descname"><spanclass="pre">isbinary</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">x</span></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#quapy.isbinary"title="Permalink to this definition">¶</a></dt>
