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<section id="quapy-method-package">
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<h1>quapy.method package<a class="headerlink" href="#quapy-method-package" title="Link to this heading"></a></h1>
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<section id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
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</section>
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<section id="module-quapy.method.aggregative">
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<span id="quapy-method-aggregative-module"></span><h2>quapy.method.aggregative module<a class="headerlink" href="#module-quapy.method.aggregative" title="Link to this heading"></a></h2>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.method.aggregative.ACC">
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||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minimize'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#ACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="quapy.method.aggregative.AggregativeCrispQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Adjusted Classify & Count</a>,
|
||
the “adjusted” variant of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">CC</span></code></a>, that corrects the predictions of CC
|
||
according to the <cite>misclassification rates</cite>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
<li><p><strong>solver</strong> – indicates the method to be used for obtaining the final estimates. The choice
|
||
‘exact’ comes down to solving the system of linear equations <span class="math notranslate nohighlight">\(Ax=B\)</span> where <cite>A</cite> is a
|
||
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
|
||
binary) and <cite>B</cite> is the vector of prevalence values estimated via CC, as <span class="math notranslate nohighlight">\(x=A^{-1}B\)</span>. This solution
|
||
might not exist for degenerated classifiers, in which case the method defaults to classify and count
|
||
(i.e., does not attempt any adjustment).
|
||
Another option is to search for the prevalence vector that minimizes the L2 norm of <span class="math notranslate nohighlight">\(|Ax-B|\)</span>. The latter
|
||
is achieved by indicating solver=’minimize’. This one generally works better, and is the default parameter.
|
||
More details about this can be consulted in <a class="reference external" href="https://lq-2022.github.io/proceedings/CompleteVolume.pdf">Bunse, M. “On Multi-Class Extensions of Adjusted Classify and
|
||
Count”, on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
|
||
(LQ 2022), ECML/PKDD 2022, Grenoble (France)</a>.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#ACC.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#ACC.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Estimates the misclassification rates.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – classifier predictions with true labels</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.getPteCondEstim">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">getPteCondEstim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#ACC.getPteCondEstim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC.getPteCondEstim" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.solve_adjustment">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">solve_adjustment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">PteCondEstim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_estim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'exact'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#ACC.solve_adjustment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC.solve_adjustment" title="Link to this definition"></a></dt>
|
||
<dd><p>Solves the system linear system <span class="math notranslate nohighlight">\(Ax = B\)</span> with <span class="math notranslate nohighlight">\(A\)</span> = <cite>PteCondEstim</cite> and <span class="math notranslate nohighlight">\(B\)</span> = <cite>prevs_estim</cite></p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>PteCondEstim</strong> – a <cite>np.ndarray</cite> of shape <cite>(n_classes,n_classes,)</cite> with entry <cite>(i,j)</cite> being the estimate
|
||
of <span class="math notranslate nohighlight">\(P(y_i|y_j)\)</span>, that is, the probability that an instance that belongs to <span class="math notranslate nohighlight">\(y_j\)</span> ends up being
|
||
classified as belonging to <span class="math notranslate nohighlight">\(y_i\)</span></p></li>
|
||
<li><p><strong>prevs_estim</strong> – a <cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with the class prevalence estimates</p></li>
|
||
<li><p><strong>solver</strong> – indicates the method to use for solving the system of linear equations. Valid options are
|
||
‘exact’ (tries to solve the system –may fail if the misclassificatin matrix has rank < n_classes) or
|
||
‘optim_minimize’ (minimizes a norm –always exists).</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an adjusted <cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with the corrected class prevalence estimates</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AdjustedClassifyAndCount">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AdjustedClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.AdjustedClassifyAndCount" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeCrispQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeCrispQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeCrispQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeQuantifier</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
|
||
<p>Abstract class for quantification methods that base their estimations on the aggregation of crips decisions
|
||
as returned by a hard classifier. Aggregative crisp quantifiers thus extend Aggregative
|
||
Quantifiers by implementing specifications about crisp predictions.</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeMedianEstimator">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeMedianEstimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><span class="pre">AggregativeQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeMedianEstimator" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryQuantifier</span></code></a></p>
|
||
<p>This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
|
||
estimation returned by differently (hyper)parameterized base quantifiers.
|
||
The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
|
||
i.e., in cases of binary quantification.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>base_quantifier</strong> – the base, binary quantifier</p></li>
|
||
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
|
||
<li><p><strong>param_grid</strong> – the grid or parameters towards which the median will be computed</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parllel workes</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeMedianEstimator.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeMedianEstimator.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeMedianEstimator.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeMedianEstimator.get_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Get parameters for this estimator.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
|
||
contained subobjects that are estimators.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>params</strong> – Parameter names mapped to their values.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>dict</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeMedianEstimator.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeMedianEstimator.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeMedianEstimator.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeMedianEstimator.set_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Set the parameters of this estimator.</p>
|
||
<p>The method works on simple estimators as well as on nested objects
|
||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||
possible to update each component of a nested object.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>estimator instance</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
|
||
<p>Abstract class for quantification methods that base their estimations on the aggregation of classification
|
||
results. Aggregative quantifiers implement a pipeline that consists of generating classification predictions
|
||
and aggregating them. For this reason, the training phase is implemented by <code class="xref py py-meth docutils literal notranslate"><span class="pre">classification_fit()</span></code> followed
|
||
by <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit" title="quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregation_fit()</span></code></a>, while the testing phase is implemented by <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.classify" title="quapy.method.aggregative.AggregativeQuantifier.classify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">classify()</span></code></a> followed by
|
||
<a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregate()</span></code></a>. Subclasses of this abstract class must provide implementations for these methods.
|
||
Aggregative quantifiers also maintain a <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.classifier" title="quapy.method.aggregative.AggregativeQuantifier.classifier"><code class="xref py py-attr docutils literal notranslate"><span class="pre">classifier</span></code></a> attribute.</p>
|
||
<p>The method <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.fit" title="quapy.method.aggregative.AggregativeQuantifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code></a> comes with a default implementation based on <code class="xref py py-meth docutils literal notranslate"><span class="pre">classification_fit()</span></code>
|
||
and <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit" title="quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregation_fit()</span></code></a>.</p>
|
||
<p>The method <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.quantify" title="quapy.method.aggregative.AggregativeQuantifier.quantify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quantify()</span></code></a> comes with a default implementation based on <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.classify" title="quapy.method.aggregative.AggregativeQuantifier.classify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">classify()</span></code></a>
|
||
and <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregate()</span></code></a>.</p>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.aggregate">
|
||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.aggregation_fit">
|
||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classes_">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.classes_" title="Link to this definition"></a></dt>
|
||
<dd><p>Class labels, in the same order in which class prevalence values are to be computed.
|
||
This default implementation actually returns the class labels of the learner.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>array-like</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classifier">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classifier</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.classifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Gives access to the classifier</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>the classifier (typically an sklearn’s Estimator)</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classifier_fit_predict">
|
||
<span class="sig-name descname"><span class="pre">classifier_fit_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predict_on</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.classifier_fit_predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.classifier_fit_predict" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the classifier if requested (<cite>fit_classifier=True</cite>) and generate the necessary predictions to
|
||
train the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
<li><p><strong>fit_classifier</strong> – whether to train the learner (default is True). Set to False if the
|
||
learner has been trained outside the quantifier.</p></li>
|
||
<li><p><strong>predict_on</strong> – specifies the set on which predictions need to be issued. This parameter can
|
||
be specified as None (default) to indicate no prediction is needed; a float in (0, 1) to
|
||
indicate the proportion of instances to be used for predictions (the remainder is used for
|
||
training); an integer >1 to indicate that the predictions must be generated via k-fold
|
||
cross-validation, using this integer as k; or the data sample itself on which to generate
|
||
the predictions.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classify">
|
||
<span class="sig-name descname"><span class="pre">classify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.classify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.classify" title="Link to this definition"></a></dt>
|
||
<dd><p>Provides the label predictions for the given instances. The predictions should respect the format expected by
|
||
<a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregate()</span></code></a>, e.g., posterior probabilities for probabilistic quantifiers, or crisp predictions for
|
||
non-probabilistic quantifiers. The default one is “decision_function”.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like of shape <cite>(n_instances, n_features,)</cite></p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances,)</cite> with label predictions</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregative quantifier. This comes down to training a classifier and an aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
<li><p><strong>fit_classifier</strong> – whether to train the learner (default is True). Set to False if the
|
||
learner has been trained outside the quantifier.</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances by aggregating the label predictions generated
|
||
by the classifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.val_split">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">val_split</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.val_split" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.val_split_">
|
||
<span class="sig-name descname"><span class="pre">val_split_</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.val_split_" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeSoftQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeSoftQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/aggregative.html#AggregativeSoftQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeQuantifier</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
|
||
<p>Abstract class for quantification methods that base their estimations on the aggregation of posterior
|
||
probabilities as returned by a probabilistic classifier.
|
||
Aggregative soft quantifiers thus extend Aggregative Quantifiers by implementing specifications
|
||
about soft predictions.</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.BinaryAggregativeQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">BinaryAggregativeQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/aggregative.html#BinaryAggregativeQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryQuantifier</span></code></a></p>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.BinaryAggregativeQuantifier.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#BinaryAggregativeQuantifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.BinaryAggregativeQuantifier.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregative quantifier. This comes down to training a classifier and an aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
<li><p><strong>fit_classifier</strong> – whether to train the learner (default is True). Set to False if the
|
||
learner has been trained outside the quantifier.</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.BinaryAggregativeQuantifier.neg_label">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">neg_label</span></span><a class="headerlink" href="#quapy.method.aggregative.BinaryAggregativeQuantifier.neg_label" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.BinaryAggregativeQuantifier.pos_label">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pos_label</span></span><a class="headerlink" href="#quapy.method.aggregative.BinaryAggregativeQuantifier.pos_label" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.CC">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">CC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#CC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.CC" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="quapy.method.aggregative.AggregativeCrispQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></p>
|
||
<p>The most basic Quantification method. One that simply classifies all instances and counts how many have been
|
||
attributed to each of the classes in order to compute class prevalence estimates.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.CC.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#CC.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.CC.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Computes class prevalence estimates by counting the prevalence of each of the predicted labels.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – array-like with label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.CC.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#CC.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.CC.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Nothing to do here!</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – this is actually None</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ClassifyAndCount">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ClassifyAndCount" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">CC</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DMy">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">DMy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nbins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Callable</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cdf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optim_minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DMy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DMy" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
|
||
<p>Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of posterior
|
||
probabilities. This implementation takes the number of bins, the divergence, and the possibility to work on CDF
|
||
as hyperparameters.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a <cite>sklearn</cite>’s Estimator that generates a probabilistic classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set to model the
|
||
validation distribution.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the validation distribution should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
<li><p><strong>nbins</strong> – number of bins used to discretize the distributions (default 8)</p></li>
|
||
<li><p><strong>divergence</strong> – a string representing a divergence measure (currently, “HD” and “topsoe” are implemented)
|
||
or a callable function taking two ndarrays of the same dimension as input (default “HD”, meaning Hellinger
|
||
Distance)</p></li>
|
||
<li><p><strong>cdf</strong> – whether to use CDF instead of PDF (default False)</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers (default None)</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DMy.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DMy.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DMy.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution
|
||
(the mixture) that best matches the test distribution, in terms of the divergence measure of choice.
|
||
In the multiclass case, with <cite>n</cite> the number of classes, the test and mixture distributions contain
|
||
<cite>n</cite> channels (proper distributions of binned posterior probabilities), on which the divergence is computed
|
||
independently. The matching is computed as an average of the divergence across all channels.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>posteriors</strong> – posterior probabilities of the instances in the sample</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a vector of class prevalence estimates</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DMy.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DMy.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DMy.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the classifier (if requested) and generates the validation distributions out of the training data.
|
||
The validation distributions have shape <cite>(n, ch, nbins)</cite>, with <cite>n</cite> the number of classes, <cite>ch</cite> the number of
|
||
channels, and <cite>nbins</cite> the number of bins. In particular, let <cite>V</cite> be the validation distributions; then <cite>di=V[i]</cite>
|
||
are the distributions obtained from training data labelled with class <cite>i</cite>; while <cite>dij = di[j]</cite> is the discrete
|
||
distribution of posterior probabilities <cite>P(Y=j|X=x)</cite> for training data labelled with class <cite>i</cite>, and <cite>dij[k]</cite>
|
||
is the fraction of instances with a value in the <cite>k</cite>-th bin.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – the training set</p></li>
|
||
<li><p><strong>fit_classifier</strong> – set to False to bypass the training (the learner is assumed to be already fit)</p></li>
|
||
<li><p><strong>val_split</strong> – either a float in (0,1) indicating the proportion of training instances to use for
|
||
validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
|
||
indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV
|
||
to estimate the parameters</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DistributionMatchingY">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">DistributionMatchingY</span></span><a class="headerlink" href="#quapy.method.aggregative.DistributionMatchingY" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.DMy" title="quapy.method.aggregative.DMy"><code class="xref py py-class docutils literal notranslate"><span class="pre">DMy</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DyS">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">DyS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Callable</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DyS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DyS" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://ojs.aaai.org/index.php/AAAI/article/view/4376">DyS framework</a> (DyS).
|
||
DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
|
||
minimizes the distance between distributions.
|
||
Details for the ternary search have been got from <<a class="reference external" href="https://dl.acm.org/doi/pdf/10.1145/3219819.3220059">https://dl.acm.org/doi/pdf/10.1145/3219819.3220059</a>></p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier</p></li>
|
||
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
|
||
<li><p><strong>n_bins</strong> – an int with the number of bins to use to compute the histograms.</p></li>
|
||
<li><p><strong>divergence</strong> – a str indicating the name of divergence (currently supported ones are “HD” or “topsoe”), or a
|
||
callable function computes the divergence between two distributions (two equally sized arrays).</p></li>
|
||
<li><p><strong>tol</strong> – a float with the tolerance for the ternary search algorithm.</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DyS.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DyS.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DyS.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.DyS.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#DyS.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DyS.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">EMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exact_train_prev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recalib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/6789744">Expectation Maximization for Quantification</a> (EMQ),
|
||
aka <cite>Saerens-Latinne-Decaestecker</cite> (SLD) algorithm.
|
||
EMQ consists of using the well-known <cite>Expectation Maximization algorithm</cite> to iteratively update the posterior
|
||
probabilities generated by a probabilistic classifier and the class prevalence estimates obtained via
|
||
maximum-likelihood estimation, in a mutually recursive way, until convergence.</p>
|
||
<p>This implementation also gives access to the heuristics proposed by <a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>. These heuristics consist of using, as the training
|
||
prevalence, an estimate of it obtained via k-fold cross validation (instead of the true training prevalence),
|
||
and to recalibrate the posterior probabilities of the classifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer, indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>, default 5); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated. This hyperparameter is only meant to be used when the
|
||
heuristics are to be applied, i.e., if a recalibration is required. The default value is None (meaning
|
||
the recalibration is not required). In case this hyperparameter is set to a value other than None, but
|
||
the recalibration is not required (recalib=None), a warning message will be raised.</p></li>
|
||
<li><p><strong>exact_train_prev</strong> – set to True (default) for using the true training prevalence as the initial observation;
|
||
set to False for computing the training prevalence as an estimate of it, i.e., as the expected
|
||
value of the posterior probabilities of the training instances.</p></li>
|
||
<li><p><strong>recalib</strong> – a string indicating the method of recalibration.
|
||
Available choices include “nbvs” (No-Bias Vector Scaling), “bcts” (Bias-Corrected Temperature Scaling,
|
||
default), “ts” (Temperature Scaling), and “vs” (Vector Scaling). Default is None (no recalibration).</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers. Only used for recalibrating the classifier if <cite>val_split</cite> is set to
|
||
an integer <cite>k</cite> –the number of folds.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.EM">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">EM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tr_prev</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">posterior_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.EM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.EM" title="Link to this definition"></a></dt>
|
||
<dd><p>Computes the <cite>Expectation Maximization</cite> routine.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tr_prev</strong> – array-like, the training prevalence</p></li>
|
||
<li><p><strong>posterior_probabilities</strong> – <cite>np.ndarray</cite> of shape <cite>(n_instances, n_classes,)</cite> with the
|
||
posterior probabilities</p></li>
|
||
<li><p><strong>epsilon</strong> – float, the threshold different between two consecutive iterations
|
||
to reach before stopping the loop</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a tuple with the estimated prevalence values (shape <cite>(n_classes,)</cite>) and
|
||
the corrected posterior probabilities (shape <cite>(n_instances, n_classes,)</cite>)</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.EMQ_BCTS">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">EMQ_BCTS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.EMQ_BCTS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.EMQ_BCTS" title="Link to this definition"></a></dt>
|
||
<dd><p>Constructs an instance of EMQ using the best configuration found in the <a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>, i.e., one that relies on Bias-Corrected Temperature
|
||
Scaling (BCTS) as a recalibration function, and that uses an estimate of the training prevalence instead of
|
||
the true training prevalence.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers.</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>An instance of EMQ with BCTS</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.EPSILON">
|
||
<span class="sig-name descname"><span class="pre">EPSILON</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0.0001</span></em><a class="headerlink" href="#quapy.method.aggregative.EMQ.EPSILON" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.MAX_ITER">
|
||
<span class="sig-name descname"><span class="pre">MAX_ITER</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">1000</span></em><a class="headerlink" href="#quapy.method.aggregative.EMQ.MAX_ITER" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.classify">
|
||
<span class="sig-name descname"><span class="pre">classify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.classify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.classify" title="Link to this definition"></a></dt>
|
||
<dd><p>Provides the posterior probabilities for the given instances. If the classifier was required
|
||
to be recalibrated, then these posteriors are recalibrated accordingly.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like of shape <cite>(n_instances, n_dimensions,)</cite></p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances, n_classes,)</cite> with posterior probabilities</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.predict_proba">
|
||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#EMQ.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ.predict_proba" title="Link to this definition"></a></dt>
|
||
<dd><p>Returns the posterior probabilities updated by the EM algorithm.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>instances</strong> – np.ndarray of shape <cite>(n_instances, n_dimensions)</cite></p></li>
|
||
<li><p><strong>epsilon</strong> – error tolerance</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances, n_classes)</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ExpectationMaximizationQuantifier">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ExpectationMaximizationQuantifier</span></span><a class="headerlink" href="#quapy.method.aggregative.ExpectationMaximizationQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">EMQ</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">HDy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#HDy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.HDy" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0020025512004069">Hellinger Distance y</a> (HDy).
|
||
HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of
|
||
minimizing the divergence (in terms of the Hellinger Distance) between two distributions of posterior
|
||
probabilities returned by the classifier. One of the distributions is generated from the unlabelled examples and
|
||
the other is generated from a validation set. This latter distribution is defined as a mixture of the
|
||
class-conditional distributions of the posterior probabilities returned for the positive and negative validation
|
||
examples, respectively. The parameters of the mixture thus represent the estimates of the class prevalence values.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier</p></li>
|
||
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#HDy.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.HDy.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#HDy.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.HDy.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a HDy quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – the training set</p></li>
|
||
<li><p><strong>fit_classifier</strong> – set to False to bypass the training (the learner is assumed to be already fit)</p></li>
|
||
<li><p><strong>val_split</strong> – either a float in (0,1) indicating the proportion of training instances to use for
|
||
validation (e.g., 0.3 for using 30% of the training set as validation data), or a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> indicating the validation set itself</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.HellingerDistanceY">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">HellingerDistanceY</span></span><a class="headerlink" href="#quapy.method.aggregative.HellingerDistanceY" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.HDy" title="quapy.method.aggregative.HDy"><code class="xref py py-class docutils literal notranslate"><span class="pre">HDy</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAllAggregative">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">OneVsAllAggregative</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallel_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'multiprocessing'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#OneVsAllAggregative"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.OneVsAllAggregative" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.OneVsAllGeneric" title="quapy.method.base.OneVsAllGeneric"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneVsAllGeneric</span></code></a>, <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeQuantifier</span></code></a></p>
|
||
<p>Allows any binary quantifier to perform quantification on single-label datasets.
|
||
The method maintains one binary quantifier for each class, and then l1-normalizes the outputs so that the
|
||
class prevelences sum up to 1.
|
||
This variant was used, along with the <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">EMQ</span></code></a> quantifier, in
|
||
<a class="reference external" href="https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf">Gao and Sebastiani, 2016</a>.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>binary_quantifier</strong> – a quantifier (binary) that will be employed to work on multiclass model in a
|
||
one-vs-all manner</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
<li><p><strong>parallel_backend</strong> – the parallel backend for joblib (default “loky”); this is helpful for some quantifiers
|
||
(e.g., ELM-based ones) that cannot be run with multiprocessing, since the temp dir they create during fit will
|
||
is removed and no longer available at predict time.</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAllAggregative.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#OneVsAllAggregative.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.OneVsAllAggregative.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAllAggregative.classify">
|
||
<span class="sig-name descname"><span class="pre">classify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#OneVsAllAggregative.classify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.OneVsAllAggregative.classify" title="Link to this definition"></a></dt>
|
||
<dd><p>If the base quantifier is not probabilistic, returns a matrix of shape <cite>(n,m,)</cite> with <cite>n</cite> the number of
|
||
instances and <cite>m</cite> the number of classes. The entry <cite>(i,j)</cite> is a binary value indicating whether instance
|
||
<cite>i `belongs to class `j</cite>. The binary classifications are independent of each other, meaning that an instance
|
||
can end up be attributed to 0, 1, or more classes.
|
||
If the base quantifier is probabilistic, returns a matrix of shape <cite>(n,m,2)</cite> with <cite>n</cite> the number of instances
|
||
and <cite>m</cite> the number of classes. The entry <cite>(i,j,1)</cite> (resp. <cite>(i,j,0)</cite>) is a value in [0,1] indicating the
|
||
posterior probability that instance <cite>i</cite> belongs (resp. does not belong) to class <cite>j</cite>. The posterior
|
||
probabilities are independent of each other, meaning that, in general, they do not sum up to one.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">solver</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minimize'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Adjusted Classify & Count</a>,
|
||
the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>). Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
<li><p><strong>solver</strong> – <p>indicates the method to be used for obtaining the final estimates. The choice
|
||
‘exact’ comes down to solving the system of linear equations <span class="math notranslate nohighlight">\(Ax=B\)</span> where <cite>A</cite> is a
|
||
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
|
||
binary) and <cite>B</cite> is the vector of prevalence values estimated via CC, as <span class="math notranslate nohighlight">\(x=A^{-1}B\)</span>. This solution
|
||
might not exist for degenerated classifiers, in which case the method defaults to classify and count
|
||
(i.e., does not attempt any adjustment).
|
||
Another option is to search for the prevalence vector that minimizes the L2 norm of <span class="math notranslate nohighlight">\(|Ax-B|\)</span>. The latter
|
||
is achieved by indicating solver=’minimize’. This one generally works better, and is the default parameter.
|
||
More details about this can be consulted in <a class="reference external" href="https://lq-2022.github.io/proceedings/CompleteVolume.pdf">Bunse, M. “On Multi-Class Extensions of Adjusted Classify and
|
||
Count”, on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
|
||
(LQ 2022), ECML/PKDD 2022, Grenoble (France)</a>.</p>
|
||
</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PACC.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PACC.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Estimates the misclassification rates</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – classifier soft predictions with true labels</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.getPteCondEstim">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">getPteCondEstim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PACC.getPteCondEstim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC.getPteCondEstim" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PCC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PCC" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Classify & Count</a>,
|
||
the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PCC.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PCC.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PCC.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PCC.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Nothing to do here!</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – this is actually None</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ProbabilisticAdjustedClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">PACC</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.ProbabilisticClassifyAndCount">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ProbabilisticClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ProbabilisticClassifyAndCount" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.PCC" title="quapy.method.aggregative.PCC"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCC</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.SLD">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SLD</span></span><a class="headerlink" href="#quapy.method.aggregative.SLD" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">EMQ</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.SMM">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SMM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#SMM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.SMM" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a></p>
|
||
<p><a class="reference external" href="https://ieeexplore.ieee.org/document/9260028">SMM method</a> (SMM).
|
||
SMM is a simplification of matching distribution methods where the representation of the examples
|
||
is created using the mean instead of a histogram (conceptually equivalent to PACC).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier.</p></li>
|
||
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.SMM.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#SMM.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.SMM.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.SMM.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#SMM.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.SMM.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.newELM">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">newELM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#newELM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.newELM" title="Link to this definition"></a></dt>
|
||
<dd><p>Explicit Loss Minimization (ELM) quantifiers.
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).
|
||
This function equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CC</span><span class="p">(</span><span class="n">SVMperf</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">C</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
|
||
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
|
||
<li><p><strong>loss</strong> – the loss to optimize (see <a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.valid_losses" title="quapy.classification.svmperf.SVMperf.valid_losses"><code class="xref py py-attr docutils literal notranslate"><span class="pre">quapy.classification.svmperf.SVMperf.valid_losses</span></code></a>)</p></li>
|
||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.newSVMAE">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">newSVMAE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#newSVMAE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.newSVMAE" title="Link to this definition"></a></dt>
|
||
<dd><p>SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Absolute Error as first used by
|
||
<a class="reference external" href="https://arxiv.org/abs/2011.02552">Moreo and Sebastiani, 2021</a>.
|
||
Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CC</span><span class="p">(</span><span class="n">SVMperf</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
|
||
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
|
||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.newSVMKLD">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">newSVMKLD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#newSVMKLD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.newSVMKLD" title="Link to this definition"></a></dt>
|
||
<dd><p>SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence
|
||
normalized via the logistic function, as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406">Esuli et al. 2015</a>.
|
||
Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CC</span><span class="p">(</span><span class="n">SVMperf</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'nkld'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
|
||
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
|
||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.newSVMQ">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">newSVMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#newSVMQ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.newSVMQ" title="Link to this definition"></a></dt>
|
||
<dd><p>SVM(Q) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the <cite>Q</cite> loss combining a
|
||
classification-oriented loss and a quantification-oriented loss, as proposed by
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S003132031400291X">Barranquero et al. 2015</a>.
|
||
Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CC</span><span class="p">(</span><span class="n">SVMperf</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'q'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
|
||
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
|
||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.aggregative.newSVMRAE">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">newSVMRAE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#newSVMRAE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.newSVMRAE" title="Link to this definition"></a></dt>
|
||
<dd><p>SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Relative Absolute Error as first
|
||
used by <a class="reference external" href="https://arxiv.org/abs/2011.02552">Moreo and Sebastiani, 2021</a>.
|
||
Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CC</span><span class="p">(</span><span class="n">SVMperf</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'mrae'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">))</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
|
||
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
|
||
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py class" id="module-quapy.method._kdey">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEBase">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEBase</span></span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEBase" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||
<p>Common ancestor for KDE-based methods. Implements some common routines.</p>
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEBase.BANDWIDTH_METHOD">
|
||
<span class="sig-name descname"><span class="pre">BANDWIDTH_METHOD</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">['scott',</span> <span class="pre">'silverman']</span></em><a class="headerlink" href="#quapy.method._kdey.KDEBase.BANDWIDTH_METHOD" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEBase.get_kde_function">
|
||
<span class="sig-name descname"><span class="pre">get_kde_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEBase.get_kde_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEBase.get_kde_function" title="Link to this definition"></a></dt>
|
||
<dd><p>Wraps the KDE function from scikit-learn.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>X</strong> – data for which the density function is to be estimated</p></li>
|
||
<li><p><strong>bandwidth</strong> – the bandwidth of the kernel</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a scikit-learn’s KernelDensity object</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEBase.get_mixture_components">
|
||
<span class="sig-name descname"><span class="pre">get_mixture_components</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEBase.get_mixture_components"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEBase.get_mixture_components" title="Link to this definition"></a></dt>
|
||
<dd><p>Returns an array containing the mixture components, i.e., the KDE functions for each class.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>X</strong> – the data containing the covariates</p></li>
|
||
<li><p><strong>y</strong> – the class labels</p></li>
|
||
<li><p><strong>n_classes</strong> – integer, the number of classes</p></li>
|
||
<li><p><strong>bandwidth</strong> – float, the bandwidth of the kernel</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEBase.pdf">
|
||
<span class="sig-name descname"><span class="pre">pdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kde</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEBase.pdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEBase.pdf" title="Link to this definition"></a></dt>
|
||
<dd><p>Wraps the density evalution of scikit-learn’s KDE. Scikit-learn returns log-scores (s), so this
|
||
function returns <span class="math notranslate nohighlight">\(e^{s}\)</span></p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>kde</strong> – a previously fit KDE function</p></li>
|
||
<li><p><strong>X</strong> – the data for which the density is to be estimated</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>np.ndarray with the densities</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyCS">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyCS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
|
||
<p>Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as
|
||
the divergence measure to be minimized. This method was first proposed in the paper
|
||
<a class="reference external" href="https://arxiv.org/abs/2401.00490">Kernel Density Estimation for Multiclass Quantification</a>, in which
|
||
the authors proposed a Monte Carlo approach for minimizing the divergence.</p>
|
||
<p>The distribution matching optimization problem comes down to solving:</p>
|
||
<p><span class="math notranslate nohighlight">\(\hat{\alpha} = \arg\min_{\alpha\in\Delta^{n-1}} \mathcal{D}(\boldsymbol{p}_{\alpha}||q_{\widetilde{U}})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
|
||
<span class="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
|
||
<p><span class="math notranslate nohighlight">\(\boldsymbol{p}_{\alpha}(\widetilde{x}) = \sum_{i=1}^n \alpha_i p_{\widetilde{L}_i}(\widetilde{x})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_X(\boldsymbol{x}) = \frac{1}{|X|} \sum_{x_i\in X} K\left(\frac{x-x_i}{h}\right)\)</span> is the
|
||
KDE function that uses the datapoints in X as the kernel centers.</p>
|
||
<p>In KDEy-CS, the divergence is taken to be the Cauchy-Schwarz divergence given by:</p>
|
||
<p><span class="math notranslate nohighlight">\(\mathcal{D}_{\mathrm{CS}}(p||q)=-\log\left(\frac{\int p(x)q(x)dx}{\sqrt{\int p(x)^2dx \int q(x)^2dx}}\right)\)</span></p>
|
||
<p>The authors showed that this distribution matching admits a closed-form solution</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier.</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.</p></li>
|
||
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyCS.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyCS.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyCS.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyCS.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">
|
||
<span class="sig-name descname"><span class="pre">gram_matrix_mix_sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyCS.gram_matrix_mix_sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyHD">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyHD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">montecarlo_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyHD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyHD" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method._kdey.KDEBase" title="quapy.method._kdey.KDEBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDEBase</span></code></a></p>
|
||
<p>Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as
|
||
the divergence measure to be minimized. This method was first proposed in the paper
|
||
<a class="reference external" href="https://arxiv.org/abs/2401.00490">Kernel Density Estimation for Multiclass Quantification</a>, in which
|
||
the authors proposed a Monte Carlo approach for minimizing the divergence.</p>
|
||
<p>The distribution matching optimization problem comes down to solving:</p>
|
||
<p><span class="math notranslate nohighlight">\(\hat{\alpha} = \arg\min_{\alpha\in\Delta^{n-1}} \mathcal{D}(\boldsymbol{p}_{\alpha}||q_{\widetilde{U}})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
|
||
<span class="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
|
||
<p><span class="math notranslate nohighlight">\(\boldsymbol{p}_{\alpha}(\widetilde{x}) = \sum_{i=1}^n \alpha_i p_{\widetilde{L}_i}(\widetilde{x})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_X(\boldsymbol{x}) = \frac{1}{|X|} \sum_{x_i\in X} K\left(\frac{x-x_i}{h}\right)\)</span> is the
|
||
KDE function that uses the datapoints in X as the kernel centers.</p>
|
||
<p>In KDEy-HD, the divergence is taken to be the squared Hellinger Distance, an f-divergence with corresponding
|
||
f-generator function given by:</p>
|
||
<p><span class="math notranslate nohighlight">\(f(u)=(\sqrt{u}-1)^2\)</span></p>
|
||
<p>The authors proposed a Monte Carlo solution that relies on importance sampling:</p>
|
||
<p><span class="math notranslate nohighlight">\(\hat{D}_f(p||q)= \frac{1}{t} \sum_{i=1}^t f\left(\frac{p(x_i)}{q(x_i)}\right) \frac{q(x_i)}{r(x_i)}\)</span></p>
|
||
<p>where the datapoints (trials) <span class="math notranslate nohighlight">\(x_1,\ldots,x_t\sim_{\mathrm{iid}} r\)</span> with <span class="math notranslate nohighlight">\(r\)</span> the
|
||
uniform distribution.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier.</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.</p></li>
|
||
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
|
||
<li><p><strong>montecarlo_trials</strong> – number of Monte Carlo trials (default 10000)</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyHD.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyHD.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyHD.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyHD.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyHD.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyHD.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyML">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyML</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyML"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyML" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method._kdey.KDEBase" title="quapy.method._kdey.KDEBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDEBase</span></code></a></p>
|
||
<p>Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as
|
||
the divergence measure to be minimized. This method was first proposed in the paper
|
||
<a class="reference external" href="https://arxiv.org/abs/2401.00490">Kernel Density Estimation for Multiclass Quantification</a>, in which
|
||
the authors show that minimizing the distribution mathing criterion for KLD is akin to performing
|
||
maximum likelihood (ML).</p>
|
||
<p>The distribution matching optimization problem comes down to solving:</p>
|
||
<p><span class="math notranslate nohighlight">\(\hat{\alpha} = \arg\min_{\alpha\in\Delta^{n-1}} \mathcal{D}(\boldsymbol{p}_{\alpha}||q_{\widetilde{U}})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
|
||
<span class="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
|
||
<p><span class="math notranslate nohighlight">\(\boldsymbol{p}_{\alpha}(\widetilde{x}) = \sum_{i=1}^n \alpha_i p_{\widetilde{L}_i}(\widetilde{x})\)</span></p>
|
||
<p>where <span class="math notranslate nohighlight">\(p_X(\boldsymbol{x}) = \frac{1}{|X|} \sum_{x_i\in X} K\left(\frac{x-x_i}{h}\right)\)</span> is the
|
||
KDE function that uses the datapoints in X as the kernel centers.</p>
|
||
<p>In KDEy-ML, the divergence is taken to be the Kullback-Leibler Divergence. This is equivalent to solving:
|
||
<span class="math notranslate nohighlight">\(\hat{\alpha} = \arg\min_{\alpha\in\Delta^{n-1}} -
|
||
\mathbb{E}_{q_{\widetilde{U}}} \left[ \log \boldsymbol{p}_{\alpha}(\widetilde{x}) \right]\)</span></p>
|
||
<p>which corresponds to the maximum likelihood estimate.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a binary classifier.</p></li>
|
||
<li><p><strong>val_split</strong> – specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set; or as an integer (default 5), indicating that the predictions
|
||
are to be generated in a <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
|
||
for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.</p></li>
|
||
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
|
||
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyML.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyML.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyML.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood
|
||
of the data (i.e., that minimizes the negative log-likelihood)</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>posteriors</strong> – instances in the sample converted into posterior probabilities</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a vector of class prevalence estimates</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyML.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyML.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyML.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class" id="module-quapy.method._neural">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetModule">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._neural.</span></span><span class="sig-name descname"><span class="pre">QuaNetModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc_embedding_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stats_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ff_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1024,</span> <span class="pre">512]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qdrop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetModule" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
|
||
<p>Implements the <a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">QuaNet</a> forward pass.
|
||
See <a class="reference internal" href="#quapy.method._neural.QuaNetTrainer" title="quapy.method._neural.QuaNetTrainer"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuaNetTrainer</span></code></a> for training QuaNet.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>doc_embedding_size</strong> – integer, the dimensionality of the document embeddings</p></li>
|
||
<li><p><strong>n_classes</strong> – integer, number of classes</p></li>
|
||
<li><p><strong>stats_size</strong> – integer, number of statistics estimated by simple quantification methods</p></li>
|
||
<li><p><strong>lstm_hidden_size</strong> – integer, hidden dimensionality of the LSTM cell</p></li>
|
||
<li><p><strong>lstm_nlayers</strong> – integer, number of LSTM layers</p></li>
|
||
<li><p><strong>ff_layers</strong> – list of integers, dimensions of the densely-connected FF layers on top of the
|
||
quantification embedding</p></li>
|
||
<li><p><strong>bidirectional</strong> – boolean, whether or not to use bidirectional LSTM</p></li>
|
||
<li><p><strong>qdrop_p</strong> – float, dropout probability</p></li>
|
||
<li><p><strong>order_by</strong> – integer, class for which the document embeddings are to be sorted</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetModule.device">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.method._neural.QuaNetModule.device" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetModule.forward">
|
||
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doc_posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">statistics</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetModule.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetModule.forward" title="Link to this definition"></a></dt>
|
||
<dd><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._neural.</span></span><span class="sig-name descname"><span class="pre">QuaNetTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tr_iter_per_poch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">va_iter_per_poch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ff_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1024,</span> <span class="pre">512]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qdrop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointdir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<p>Implementation of <a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">QuaNet</a>, a neural network for
|
||
quantification. This implementation uses <a class="reference external" href="https://pytorch.org/">PyTorch</a> and can take advantage of GPU
|
||
for speeding-up the training phase.</p>
|
||
<p>Example:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
|
||
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span><span class="p">,</span> <span class="n">CNNnet</span>
|
||
<span class="go">>>></span>
|
||
<span class="gp">>>> </span><span class="c1"># use samples of 100 elements</span>
|
||
<span class="gp">>>> </span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
|
||
<span class="go">>>></span>
|
||
<span class="gp">>>> </span><span class="c1"># load the kindle dataset as text, and convert words to numerical indexes</span>
|
||
<span class="gp">>>> </span><span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">qp</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="go">>>></span>
|
||
<span class="gp">>>> </span><span class="c1"># the text classifier is a CNN trained by NeuralClassifierTrainer</span>
|
||
<span class="gp">>>> </span><span class="n">cnn</span> <span class="o">=</span> <span class="n">CNNnet</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">classifier</span> <span class="o">=</span> <span class="n">NeuralClassifierTrainer</span><span class="p">(</span><span class="n">cnn</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||
<span class="go">>>></span>
|
||
<span class="gp">>>> </span><span class="c1"># train QuaNet (QuaNet is an alias to QuaNetTrainer)</span>
|
||
<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">QuaNet</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||
<span class="gp">>>> </span><span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – an object implementing <cite>fit</cite> (i.e., that can be trained on labelled data),
|
||
<cite>predict_proba</cite> (i.e., that can generate posterior probabilities of unlabelled examples) and
|
||
<cite>transform</cite> (i.e., that can generate embedded representations of the unlabelled instances).</p></li>
|
||
<li><p><strong>sample_size</strong> – integer, the sample size; default is None, meaning that the sample size should be
|
||
taken from qp.environ[“SAMPLE_SIZE”]</p></li>
|
||
<li><p><strong>n_epochs</strong> – integer, maximum number of training epochs</p></li>
|
||
<li><p><strong>tr_iter_per_poch</strong> – integer, number of training iterations before considering an epoch complete</p></li>
|
||
<li><p><strong>va_iter_per_poch</strong> – integer, number of validation iterations to perform after each epoch</p></li>
|
||
<li><p><strong>lr</strong> – float, the learning rate</p></li>
|
||
<li><p><strong>lstm_hidden_size</strong> – integer, hidden dimensionality of the LSTM cells</p></li>
|
||
<li><p><strong>lstm_nlayers</strong> – integer, number of LSTM layers</p></li>
|
||
<li><p><strong>ff_layers</strong> – list of integers, dimensions of the densely-connected FF layers on top of the
|
||
quantification embedding</p></li>
|
||
<li><p><strong>bidirectional</strong> – boolean, indicates whether the LSTM is bidirectional or not</p></li>
|
||
<li><p><strong>qdrop_p</strong> – float, dropout probability</p></li>
|
||
<li><p><strong>patience</strong> – integer, number of epochs showing no improvement in the validation set before stopping the
|
||
training phase (early stopping)</p></li>
|
||
<li><p><strong>checkpointdir</strong> – string, a path where to store models’ checkpoints</p></li>
|
||
<li><p><strong>checkpointname</strong> – string (optional), the name of the model’s checkpoint</p></li>
|
||
<li><p><strong>device</strong> – string, indicate “cpu” or “cuda”</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.classes_">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.classes_" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.clean_checkpoint">
|
||
<span class="sig-name descname"><span class="pre">clean_checkpoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.clean_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.clean_checkpoint" title="Link to this definition"></a></dt>
|
||
<dd><p>Removes the checkpoint</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">
|
||
<span class="sig-name descname"><span class="pre">clean_checkpoint_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.clean_checkpoint_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir" title="Link to this definition"></a></dt>
|
||
<dd><p>Removes anything contained in the checkpoint directory</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains QuaNet.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>data</strong> – the training data on which to train QuaNet. If <cite>fit_classifier=True</cite>, the data will be split in
|
||
40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If
|
||
<cite>fit_classifier=False</cite>, the data will be split in 66/34 for training QuaNet and validating it, respectively.</p></li>
|
||
<li><p><strong>fit_classifier</strong> – if True, trains the classifier on a split containing 40% of the data</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.get_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Get parameters for this estimator.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
|
||
contained subobjects that are estimators.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>params</strong> – Parameter names mapped to their values.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>dict</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.QuaNetTrainer.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#QuaNetTrainer.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.QuaNetTrainer.set_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Set the parameters of this estimator.</p>
|
||
<p>The method works on simple estimators as well as on nested objects
|
||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||
possible to update each component of a nested object.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>estimator instance</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method._neural.mae_loss">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method._neural.</span></span><span class="sig-name descname"><span class="pre">mae_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_neural.html#mae_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._neural.mae_loss" title="Link to this definition"></a></dt>
|
||
<dd><p>Torch-like wrapper for the Mean Absolute Error</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>output</strong> – predictions</p></li>
|
||
<li><p><strong>target</strong> – ground truth values</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>mean absolute error loss</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py class" id="module-quapy.method._threshold_optim">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MAX">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">MAX</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MAX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MAX" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
|
||
<p>Threshold Optimization variant for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
|
||
for the threshold that maximizes <cite>tpr-fpr</cite>.
|
||
The goal is to bring improved stability to the denominator of the adjustment.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MAX.condition">
|
||
<span class="sig-name descname"><span class="pre">condition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">float</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MAX.condition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MAX.condition" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>float, a score for the given <cite>tpr</cite> and <cite>fpr</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">MS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
|
||
<p>Median Sweep. Threshold Optimization variant for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that generates
|
||
class prevalence estimates for all decision thresholds and returns the median of them all.
|
||
The goal is to bring improved stability to the denominator of the adjustment.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS.condition">
|
||
<span class="sig-name descname"><span class="pre">condition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">float</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS.condition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS.condition" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>float, a score for the given <cite>tpr</cite> and <cite>fpr</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS2">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">MS2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS2" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.MS" title="quapy.method._threshold_optim.MS"><code class="xref py py-class docutils literal notranslate"><span class="pre">MS</span></code></a></p>
|
||
<p>Median Sweep 2. Threshold Optimization variant for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that generates
|
||
class prevalence estimates for all decision thresholds and returns the median of for cases in
|
||
which <cite>tpr-fpr>0.25</cite>
|
||
The goal is to bring improved stability to the denominator of the adjustment.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MS2.discard">
|
||
<span class="sig-name descname"><span class="pre">discard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS2.discard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS2.discard" title="Link to this definition"></a></dt>
|
||
<dd><p>Indicates whether a combination of tpr and fpr should be discarded</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>true if the combination is to be discarded, false otherwise</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.T50">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">T50</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#T50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.T50" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
|
||
<p>Threshold Optimization variant for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
|
||
for the threshold that makes <cite>tpr</cite> closest to 0.5.
|
||
The goal is to bring improved stability to the denominator of the adjustment.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.T50.condition">
|
||
<span class="sig-name descname"><span class="pre">condition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">float</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#T50.condition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.T50.condition" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>float, a score for the given <cite>tpr</cite> and <cite>fpr</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">ThresholdOptimization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a></p>
|
||
<p>Abstract class of Threshold Optimization variants for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a>.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
The different variants are based on different heuristics for choosing a decision threshold
|
||
that would allow for more true positives and many more false positives, on the grounds this
|
||
would deliver larger denominators.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization.aggregate">
|
||
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization.aggregate" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the aggregation of label predictions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">
|
||
<span class="sig-name descname"><span class="pre">aggregate_with_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tprs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fprs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregate_with_threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">
|
||
<span class="sig-name descname"><span class="pre">aggregation_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregation_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains the aggregation function.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classif_predictions</strong> – a LabelledCollection containing the label predictions issued
|
||
by the classifier</p></li>
|
||
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization.condition">
|
||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">condition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">float</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.condition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization.condition" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>float, a score for the given <cite>tpr</cite> and <cite>fpr</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization.discard">
|
||
<span class="sig-name descname"><span class="pre">discard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.discard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization.discard" title="Link to this definition"></a></dt>
|
||
<dd><p>Indicates whether a combination of tpr and fpr should be discarded</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>true if the combination is to be discarded, false otherwise</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.X">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">X</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#X"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.X" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
|
||
<p>Threshold Optimization variant for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
|
||
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
|
||
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
|
||
for the threshold that yields <cite>tpr=1-fpr</cite>.
|
||
The goal is to bring improved stability to the denominator of the adjustment.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – a sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>, defaults 5), or as a
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method._threshold_optim.X.condition">
|
||
<span class="sig-name descname"><span class="pre">condition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">float</span></span></span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#X.condition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.X.condition" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>tpr</strong> – float, true positive rate</p></li>
|
||
<li><p><strong>fpr</strong> – float, false positive rate</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>float, a score for the given <cite>tpr</cite> and <cite>fpr</cite></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
</section>
|
||
<section id="module-quapy.method.base">
|
||
<span id="quapy-method-base-module"></span><h2>quapy.method.base module<a class="headerlink" href="#module-quapy.method.base" title="Link to this heading"></a></h2>
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">BaseQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/base.html#BaseQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.BaseQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code></p>
|
||
<p>Abstract Quantifier. A quantifier is defined as an object of a class that implements the method <a class="reference internal" href="#quapy.method.base.BaseQuantifier.fit" title="quapy.method.base.BaseQuantifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code></a> on
|
||
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, the method <a class="reference internal" href="#quapy.method.base.BaseQuantifier.quantify" title="quapy.method.base.BaseQuantifier.quantify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quantify()</span></code></a>, and the <code class="xref py py-meth docutils literal notranslate"><span class="pre">set_params()</span></code> and
|
||
<code class="xref py py-meth docutils literal notranslate"><span class="pre">get_params()</span></code> for model selection (see <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ()</span></code></a>)</p>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.fit">
|
||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#BaseQuantifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.BaseQuantifier.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.quantify">
|
||
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#BaseQuantifier.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.BaseQuantifier.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.base.BinaryQuantifier">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">BinaryQuantifier</span></span><a class="reference internal" href="_modules/quapy/method/base.html#BinaryQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.BinaryQuantifier" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<p>Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes
|
||
(typically, to be interpreted as one class and its complement).</p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.base.OneVsAll">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">OneVsAll</span></span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAll"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAll" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.base.OneVsAllGeneric">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">OneVsAllGeneric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAllGeneric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.OneVsAll" title="quapy.method.base.OneVsAll"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneVsAll</span></code></a>, <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<p>Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary
|
||
quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.</p>
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.base.OneVsAllGeneric.classes_">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric.classes_" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.base.OneVsAllGeneric.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAllGeneric.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.base.OneVsAllGeneric.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAllGeneric.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.base.newOneVsAll">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">newOneVsAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#newOneVsAll"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.newOneVsAll" title="Link to this definition"></a></dt>
|
||
<dd></dd></dl>
|
||
|
||
</section>
|
||
<section id="module-quapy.method.meta">
|
||
<span id="quapy-method-meta-module"></span><h2>quapy.method.meta module<a class="headerlink" href="#module-quapy.method.meta" title="Link to this heading"></a></h2>
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.EACC">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#EACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.EACC" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ACC</span></code></a> quantifiers, as used by
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
|
||
<p>Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.ECC">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">ECC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#ECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.ECC" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.CC</span></code></a> quantifiers, as used by
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
|
||
<p>Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">CC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.EEMQ">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EEMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#EEMQ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.EEMQ" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.EMQ</span></code></a> quantifiers.</p>
|
||
<p>Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.EHDy">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EHDy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#EHDy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.EHDy" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.HDy" title="quapy.method.aggregative.HDy"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.HDy</span></code></a> quantifiers, as used by
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
|
||
<p>Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.EPACC">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EPACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#EPACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.EPACC" title="Link to this definition"></a></dt>
|
||
<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a> quantifiers.</p>
|
||
<p>Equivalent to:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">Ensemble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">red_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_pos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ave'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#Ensemble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.Ensemble" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.VALID_POLICIES">
|
||
<span class="sig-name descname"><span class="pre">VALID_POLICIES</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'ave',</span> <span class="pre">'ds',</span> <span class="pre">'mae',</span> <span class="pre">'mkld',</span> <span class="pre">'mnae',</span> <span class="pre">'mnkld',</span> <span class="pre">'mnrae',</span> <span class="pre">'mrae',</span> <span class="pre">'mse',</span> <span class="pre">'ptr'}</span></em><a class="headerlink" href="#quapy.method.meta.Ensemble.VALID_POLICIES" title="Link to this definition"></a></dt>
|
||
<dd><p>Implementation of the Ensemble methods for quantification described by
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego et al., 2017</a>
|
||
and
|
||
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.
|
||
The policies implemented include:</p>
|
||
<ul class="simple">
|
||
<li><p>Average (<cite>policy=’ave’</cite>): computes class prevalence estimates as the average of the estimates
|
||
returned by the base quantifiers.</p></li>
|
||
<li><p>Training Prevalence (<cite>policy=’ptr’</cite>): applies a dynamic selection to the ensemble’s members by retaining only
|
||
those members such that the class prevalence values in the samples they use as training set are closest to
|
||
preliminary class prevalence estimates computed as the average of the estimates of all the members. The final
|
||
estimate is recomputed by considering only the selected members.</p></li>
|
||
<li><p>Distribution Similarity (<cite>policy=’ds’</cite>): performs a dynamic selection of base members by retaining
|
||
the members trained on samples whose distribution of posterior probabilities is closest, in terms of the
|
||
Hellinger Distance, to the distribution of posterior probabilities in the test sample</p></li>
|
||
<li><p>Accuracy (<cite>policy=’<valid error name>’</cite>): performs a static selection of the ensemble members by
|
||
retaining those that minimize a quantification error measure, which is passed as an argument.</p></li>
|
||
</ul>
|
||
<p>Example:</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">quantifier</span><span class="o">=</span><span class="n">ACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">()),</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">'ave'</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>quantifier</strong> – base quantification member of the ensemble</p></li>
|
||
<li><p><strong>size</strong> – number of members</p></li>
|
||
<li><p><strong>red_size</strong> – number of members to retain after selection (depending on the policy)</p></li>
|
||
<li><p><strong>min_pos</strong> – minimum number of positive instances to consider a sample as valid</p></li>
|
||
<li><p><strong>policy</strong> – the selection policy; available policies include: <cite>ave</cite> (default), <cite>ptr</cite>, <cite>ds</cite>, and accuracy
|
||
(which is instantiated via a valid error name, e.g., <cite>mae</cite>)</p></li>
|
||
<li><p><strong>max_sample_size</strong> – maximum number of instances to consider in the samples (set to None
|
||
to indicate no limit, default)</p></li>
|
||
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation split, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers (default 1)</p></li>
|
||
<li><p><strong>verbose</strong> – set to True (default is False) to get some information in standard output</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.aggregative">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">aggregative</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.aggregative" title="Link to this definition"></a></dt>
|
||
<dd><p>Indicates that the quantifier is not aggregative.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>False</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#Ensemble.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.Ensemble.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#Ensemble.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.Ensemble.get_params" title="Link to this definition"></a></dt>
|
||
<dd><p>This function should not be used within <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
|
||
with the abstract class).
|
||
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
|
||
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a classifier <cite>l</cite> optimized for
|
||
classification (not recommended).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with scikit-learn</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>raises an Exception</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py property">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.probabilistic">
|
||
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probabilistic</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.probabilistic" title="Link to this definition"></a></dt>
|
||
<dd><p>Indicates that the quantifier is not probabilistic.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>False</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#Ensemble.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.Ensemble.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#Ensemble.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.Ensemble.set_params" title="Link to this definition"></a></dt>
|
||
<dd><p>This function should not be used within <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
|
||
with the abstract class).
|
||
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
|
||
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a classifier <cite>l</cite> optimized for
|
||
classification (not recommended).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>parameters</strong> – dictionary</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>raises an Exception</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">MedianEstimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><span class="pre">BinaryQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryQuantifier</span></code></a></p>
|
||
<p>This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
|
||
estimation returned by differently (hyper)parameterized base quantifiers.
|
||
The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
|
||
i.e., in cases of binary quantification.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>base_quantifier</strong> – the base, binary quantifier</p></li>
|
||
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
|
||
<li><p><strong>param_grid</strong> – the grid or parameters towards which the median will be computed</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parllel workes</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator.get_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Get parameters for this estimator.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
|
||
contained subobjects that are estimators.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>params</strong> – Parameter names mapped to their values.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>dict</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator.set_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Set the parameters of this estimator.</p>
|
||
<p>The method works on simple estimators as well as on nested objects
|
||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||
possible to update each component of a nested object.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>estimator instance</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator2">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">MedianEstimator2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><span class="pre">BinaryQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator2" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryQuantifier</span></code></a></p>
|
||
<p>This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
|
||
estimation returned by differently (hyper)parameterized base quantifiers.
|
||
The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
|
||
i.e., in cases of binary quantification.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>base_quantifier</strong> – the base, binary quantifier</p></li>
|
||
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
|
||
<li><p><strong>param_grid</strong> – the grid or parameters towards which the median will be computed</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parllel workes</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator2.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator2.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator2.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Trains a quantifier.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator2.get_params">
|
||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator2.get_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator2.get_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Get parameters for this estimator.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
|
||
contained subobjects that are estimators.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>params</strong> – Parameter names mapped to their values.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>dict</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator2.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator2.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator2.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with class prevalence estimates.</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.MedianEstimator2.set_params">
|
||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#MedianEstimator2.set_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.MedianEstimator2.set_params" title="Link to this definition"></a></dt>
|
||
<dd><p>Set the parameters of this estimator.</p>
|
||
<p>The method works on simple estimators as well as on nested objects
|
||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||
possible to update each component of a nested object.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||
</dd>
|
||
<dt class="field-odd">Return type<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p>estimator instance</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.ensembleFactory">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">ensembleFactory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_quantifier_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_model_sel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#ensembleFactory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.ensembleFactory" title="Link to this definition"></a></dt>
|
||
<dd><p>Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model
|
||
selection for quantification) for a given evaluation metric using <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a>.
|
||
If the evaluation metric is classification-oriented
|
||
(instead of quantification-oriented), then the optimization will be carried out via sklearn’s
|
||
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a>.</p>
|
||
<p>Example to instantiate an <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a> based on <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a>
|
||
in which the base members are optimized for <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> via
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a>. The ensemble follows the policy <cite>Accuracy</cite> based
|
||
on <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> (the same measure being optimized),
|
||
meaning that a static selection of members of the ensemble is made based on their performance
|
||
in terms of this error.</p>
|
||
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
|
||
<span class="gp">>>> </span> <span class="s1">'C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">),</span>
|
||
<span class="gp">>>> </span> <span class="s1">'class_weight'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
|
||
<span class="gp">>>> </span><span class="p">}</span>
|
||
<span class="gp">>>> </span><span class="n">param_mod_sel</span> <span class="o">=</span> <span class="p">{</span>
|
||
<span class="gp">>>> </span> <span class="s1">'sample_size'</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span>
|
||
<span class="gp">>>> </span> <span class="s1">'protocol'</span><span class="p">:</span> <span class="s1">'app'</span>
|
||
<span class="gp">>>> </span><span class="p">}</span>
|
||
<span class="gp">>>> </span><span class="n">common</span><span class="o">=</span><span class="p">{</span>
|
||
<span class="gp">>>> </span> <span class="s1">'max_sample_size'</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
|
||
<span class="gp">>>> </span> <span class="s1">'n_jobs'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||
<span class="gp">>>> </span> <span class="s1">'param_grid'</span><span class="p">:</span> <span class="n">param_grid</span><span class="p">,</span>
|
||
<span class="gp">>>> </span> <span class="s1">'param_mod_sel'</span><span class="p">:</span> <span class="n">param_mod_sel</span><span class="p">,</span>
|
||
<span class="gp">>>> </span><span class="p">}</span>
|
||
<span class="go">>>></span>
|
||
<span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="o">**</span><span class="n">common</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>classifier</strong> – sklearn’s Estimator that generates a classifier</p></li>
|
||
<li><p><strong>base_quantifier_class</strong> – a class of quantifiers</p></li>
|
||
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
|
||
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
|
||
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
|
||
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
|
||
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="quapy.method.meta.get_probability_distribution">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">get_probability_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posterior_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/meta.html#get_probability_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.meta.get_probability_distribution" title="Link to this definition"></a></dt>
|
||
<dd><p>Gets a histogram out of the posterior probabilities (only for the binary case).</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>posterior_probabilities</strong> – array-like of shape <cite>(n_instances, 2,)</cite></p></li>
|
||
<li><p><strong>bins</strong> – integer</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p><cite>np.ndarray</cite> with the relative frequencies for each bin (for the positive class only)</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</section>
|
||
<section id="module-quapy.method.non_aggregative">
|
||
<span id="quapy-method-non-aggregative-module"></span><h2>quapy.method.non_aggregative module<a class="headerlink" href="#module-quapy.method.non_aggregative" title="Link to this heading"></a></h2>
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.DMx">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.non_aggregative.</span></span><span class="sig-name descname"><span class="pre">DMx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nbins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Callable</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cdf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optim_minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#DMx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.DMx" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<p>Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.
|
||
This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>nbins</strong> – number of bins used to discretize the distributions (default 8)</p></li>
|
||
<li><p><strong>divergence</strong> – a string representing a divergence measure (currently, “HD” and “topsoe” are implemented)
|
||
or a callable function taking two ndarrays of the same dimension as input (default “HD”, meaning Hellinger
|
||
Distance)</p></li>
|
||
<li><p><strong>cdf</strong> – whether to use CDF instead of PDF (default False)</p></li>
|
||
<li><p><strong>n_jobs</strong> – number of parallel workers (default None)</p></li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.DMx.HDx">
|
||
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">HDx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#DMx.HDx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.DMx.HDx" title="Link to this definition"></a></dt>
|
||
<dd><p><a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0020025512004069">Hellinger Distance x</a> (HDx).
|
||
HDx is a method for training binary quantifiers, that models quantification as the problem of
|
||
minimizing the average divergence (in terms of the Hellinger Distance) across the feature-specific normalized
|
||
histograms of two representations, one for the unlabelled examples, and another generated from the training
|
||
examples as a mixture model of the class-specific representations. The parameters of the mixture thus represent
|
||
the estimates of the class prevalence values.</p>
|
||
<p>The method computes all matchings for nbins in [10, 20, …, 110] and reports the mean of the median.
|
||
The best prevalence is searched via linear search, from 0 to 1 stepping by 0.01.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>n_jobs</strong> – number of parallel workers</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>an instance of this class setup to mimick the performance of the HDx as originally proposed by
|
||
González-Castro, Alaiz-Rodríguez, Alegre (2013)</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.DMx.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#DMx.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.DMx.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Generates the validation distributions out of the training data (covariates).
|
||
The validation distributions have shape <cite>(n, nfeats, nbins)</cite>, with <cite>n</cite> the number of classes, <cite>nfeats</cite>
|
||
the number of features, and <cite>nbins</cite> the number of bins.
|
||
In particular, let <cite>V</cite> be the validation distributions; then <cite>di=V[i]</cite> are the distributions obtained from
|
||
training data labelled with class <cite>i</cite>; while <cite>dij = di[j]</cite> is the discrete distribution for feature j in
|
||
training data labelled with class <cite>i</cite>, and <cite>dij[k]</cite> is the fraction of instances with a value in the <cite>k</cite>-th bin.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – the training set</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.DMx.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#DMx.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.DMx.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution
|
||
(the mixture) that best matches the test distribution, in terms of the divergence measure of choice.
|
||
The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)
|
||
between all feature-specific discrete distributions.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – instances in the sample</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>a vector of class prevalence estimates</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
<dl class="py attribute">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.DistributionMatchingX">
|
||
<span class="sig-prename descclassname"><span class="pre">quapy.method.non_aggregative.</span></span><span class="sig-name descname"><span class="pre">DistributionMatchingX</span></span><a class="headerlink" href="#quapy.method.non_aggregative.DistributionMatchingX" title="Link to this definition"></a></dt>
|
||
<dd><p>alias of <a class="reference internal" href="#quapy.method.non_aggregative.DMx" title="quapy.method.non_aggregative.DMx"><code class="xref py py-class docutils literal notranslate"><span class="pre">DMx</span></code></a></p>
|
||
</dd></dl>
|
||
|
||
<dl class="py class">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">
|
||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.non_aggregative.</span></span><span class="sig-name descname"><span class="pre">MaximumLikelihoodPrevalenceEstimation</span></span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation" title="Link to this definition"></a></dt>
|
||
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
|
||
<p>The <cite>Maximum Likelihood Prevalence Estimation</cite> (MLPE) method is a lazy method that assumes there is no prior
|
||
probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).
|
||
The estimation of class prevalence values for any test sample is always (i.e., irrespective of the test sample
|
||
itself) the class prevalence seen during training. This method is considered to be a lower-bound quantifier that
|
||
any quantification method should beat.</p>
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">
|
||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit" title="Link to this definition"></a></dt>
|
||
<dd><p>Computes the training prevalence and stores it.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>data</strong> – the training sample</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>self</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
<dl class="py method">
|
||
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">
|
||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation.quantify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify" title="Link to this definition"></a></dt>
|
||
<dd><p>Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||
<dd class="field-odd"><p><strong>instances</strong> – array-like (ignored)</p>
|
||
</dd>
|
||
<dt class="field-even">Returns<span class="colon">:</span></dt>
|
||
<dd class="field-even"><p>the class prevalence seen during training</p>
|
||
</dd>
|
||
</dl>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
|
||
|
||
</section>
|
||
<section id="module-quapy.method">
|
||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.method" title="Link to this heading"></a></h2>
|
||
</section>
|
||
</section>
|
||
|
||
|
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