<!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="../../">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>quapy.evaluation — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=92fd9be5" />
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
<!--[if lt IE 9]>
<script src="../../_static/js/html5shiv.min.js"></script>
<![endif]-->
<script src="../../_static/jquery.js?v=5d32c60e"></script>
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
<script src="../../_static/documentation_options.js?v=22607128"></script>
<script src="../../_static/doctools.js?v=9a2dae69"></script>
<script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../_static/js/theme.js"></script>
<link rel="index" title="Index" href="../../genindex.html" />
<link rel="search" title="Search" href="../../search.html" />
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
<div class="wy-side-nav-search" >
<a href="../../index.html" class="icon icon-home">
QuaPy: A Python-based open-source framework for quantification
</a>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
<input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
</ul>
</div>
</div>
</nav>
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
<a href="../../index.html">QuaPy: A Python-based open-source framework for quantification</a>
</nav>
<div class="wy-nav-content">
<div class="rst-content">
<div role="navigation" aria-label="Page navigation">
<ul class="wy-breadcrumbs">
<li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
<li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
<li class="breadcrumb-item active">quapy.evaluation</li>
<li class="wy-breadcrumbs-aside">
</li>
</ul>
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<div class="viewcode-block" id="prediction">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a>
<span class="k">def</span> <span class="nf">prediction</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Uses a quantification model to generate predictions for the samples generated via a specific protocol.</span>
<span class="sd"> This function is central to all evaluation processes, and is endowed with an optimization to speed-up the</span>
<span class="sd"> prediction of protocols that generate samples from a large collection. The optimization applies to aggregative</span>
<span class="sd"> quantifiers only, and to OnLabelledCollectionProtocol protocols, and comes down to generating the classification</span>
<span class="sd"> predictions once and for all, and then generating samples over the classification predictions (instead of over</span>
<span class="sd"> the raw instances), so that the classifier prediction is never called again. This behaviour is obtained by</span>
<span class="sd"> setting `aggr_speedup` to 'auto' or True, and is only carried out if the overall process is convenient in terms</span>
<span class="sd"> of computations (e.g., if the number of classification predictions needed for the original collection exceed the</span>
<span class="sd"> number of classification predictions needed for all samples, then the optimization is not undertaken).</span>
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
<span class="sd"> in charge of generating the samples for which the model has to issue class prevalence predictions.</span>
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
<span class="sd"> convenient or not. Set to False to deactivate.</span>
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
<span class="sd"> :return: a tuple `(true_prevs, estim_prevs)` in which each element in the tuple is an array of shape</span>
<span class="sd"> `(n_samples, n_classes)` containing the true, or predicted, prevalence values for each sample</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'auto'</span><span class="p">,</span> <span class="s1">'force'</span><span class="p">],</span> <span class="s1">'invalid value for aggr_speedup'</span>
<span class="n">sout</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="kc">None</span>
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="s1">'auto'</span><span class="p">,</span> <span class="s1">'force'</span><span class="p">]:</span>
<span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span>
<span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span>
<span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">'force'</span><span class="p">:</span>
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'forcing aggregative speedup'</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="s1">'sample_size'</span><span class="p">):</span>
<span class="n">nD</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span>
<span class="n">samplesD</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">()</span> <span class="o">*</span> <span class="n">protocol</span><span class="o">.</span><span class="n">sample_size</span>
<span class="k">if</span> <span class="n">nD</span> <span class="o"><</span> <span class="n">samplesD</span><span class="p">:</span>
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'speeding up the prediction for the aggregative quantifier, '</span>
<span class="sa">f</span><span class="s1">'total classifications </span><span class="si">{</span><span class="n">nD</span><span class="si">}</span><span class="s1"> instead of </span><span class="si">{</span><span class="n">samplesD</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">apply_optimization</span><span class="p">:</span>
<span class="n">pre_classified</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
<span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span>
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">'predicting'</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span>
<span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span>
<span class="n">true_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample_prev</span><span class="p">)</span>
<span class="n">true_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">)</span>
<span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">estim_prevs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span>
<div class="viewcode-block" id="evaluation_report">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a>
<span class="k">def</span> <span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">,</span>
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates a report (a pandas' DataFrame) containing information of the evaluation of the model as according</span>
<span class="sd"> to a specific protocol and in terms of one or more evaluation metrics (errors).</span>
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
<span class="sd"> in charge of generating the samples in which the model is evaluated.</span>
<span class="sd"> :param error_metrics: a string, or list of strings, representing the name(s) of an error function in `qp.error`</span>
<span class="sd"> (e.g., 'mae', the default value), or a callable function, or a list of callable functions, implementing</span>
<span class="sd"> the error function itself.</span>
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
<span class="sd"> convenient or not. Set to False to deactivate.</span>
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
<span class="sd"> :return: a pandas' DataFrame containing the columns 'true-prev' (the true prevalence of each sample),</span>
<span class="sd"> 'estim-prev' (the prevalence estimated by the model for each sample), and as many columns as error metrics</span>
<span class="sd"> have been indicated, each displaying the score in terms of that metric for every sample.</span>
<span class="sd"> """</span>
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span>
<span class="n">error_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">e</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_metrics</span><span class="p">]</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="s1">'__call__'</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">),</span> <span class="s1">'invalid error functions'</span>
<span class="n">error_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">]</span>
<span class="n">row_entries</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
<span class="n">series</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'true-prev'</span><span class="p">:</span> <span class="n">true_prev</span><span class="p">,</span> <span class="s1">'estim-prev'</span><span class="p">:</span> <span class="n">estim_prev</span><span class="p">}</span>
<span class="k">for</span> <span class="n">error_name</span><span class="p">,</span> <span class="n">error_metric</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">error_names</span><span class="p">,</span> <span class="n">error_funcs</span><span class="p">):</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">)</span>
<span class="n">series</span><span class="p">[</span><span class="n">error_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
<span class="n">row_entries</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span><span class="n">row_entries</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
<div class="viewcode-block" id="evaluate">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Evaluates a quantification model according to a specific sample generation protocol and in terms of one</span>
<span class="sd"> evaluation metric (error).</span>
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
<span class="sd"> :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
<span class="sd"> :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the</span>
<span class="sd"> protocol in charge of generating the samples in which the model is evaluated.</span>
<span class="sd"> :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
<span class="sd"> (e.g., 'mae'), or a callable function implementing the error function itself.</span>
<span class="sd"> :param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of</span>
<span class="sd"> instances in the original collection on which the protocol acts is larger than the number of instances</span>
<span class="sd"> in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is</span>
<span class="sd"> convenient or not. Set to False to deactivate.</span>
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
<span class="sd"> :return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with</span>
<span class="sd"> the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns</span>
<span class="sd"> a single float</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metric</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">error_metric</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_metric</span><span class="p">)</span>
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div>
<div class="viewcode-block" id="evaluate_on_samples">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a>
<span class="k">def</span> <span class="nf">evaluate_on_samples</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span>
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Evaluates a quantification model on a given set of samples and in terms of one evaluation metric (error).</span>
<span class="sd"> :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
<span class="sd"> :param samples: a list of samples on which the quantifier is to be evaluated</span>
<span class="sd"> :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
<span class="sd"> (e.g., 'mae'), or a callable function implementing the error function itself.</span>
<span class="sd"> :param verbose: boolean, show or not information in stdout</span>
<span class="sd"> :return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with</span>
<span class="sd"> the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns</span>
<span class="sd"> a single float</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">IterateProtocol</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="n">error_metric</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span></div>
</pre></div>
</div>
</div>
<footer>
<hr/>
<div role="contentinfo">
<p>© Copyright 2024, Alejandro Moreo.</p>
</div>
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script>
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
</body>
</html>