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improving doc manuals and added bonferroni parameter to interval construction

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Alejandro Moreo 2026-07-06 12:11:24 +02:00
parent fd6373651e
commit 574fc61032
22 changed files with 1035 additions and 893 deletions
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7
docs/build/html/_modules/index.html vendored
View File

@ -29,7 +29,7 @@
<link href="../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../_static/custom.css?v=9f2a2228" />
@ -383,7 +383,6 @@
<li><a href="quapy/method/_threshold_optim.html">quapy.method._threshold_optim</a></li>
<li><a href="quapy/method/aggregative.html">quapy.method.aggregative</a></li>
<li><a href="quapy/method/base.html">quapy.method.base</a></li>
<li><a href="quapy/method/composable.html">quapy.method.composable</a></li>
<li><a href="quapy/method/confidence.html">quapy.method.confidence</a></li>
<li><a href="quapy/method/meta.html">quapy.method.meta</a></li>
<li><a href="quapy/method/non_aggregative.html">quapy.method.non_aggregative</a></li>
@ -391,9 +390,7 @@
<li><a href="quapy/plot.html">quapy.plot</a></li>
<li><a href="quapy/protocol.html">quapy.protocol</a></li>
<li><a href="quapy/util.html">quapy.util</a></li>
<li><a href="qunfold/methods/linear/losses.html">qunfold.methods.linear.losses</a></li>
<li><a href="qunfold/methods/linear/representations.html">qunfold.methods.linear.representations</a></li>
<li><a href="qunfold/sklearn.html">qunfold.sklearn</a></li>
<li><a href="sklearn/utils/_metadata_requests.html">sklearn.utils._metadata_requests</a></li>
</ul>
</article>

108
docs/build/html/_modules/quapy/classification/neural.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,27 +370,27 @@
<article class="bd-article">
<h1>Source code for quapy.classification.neural</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">pad_sequence</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">nn</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.utils.rnn</span><span class="w"> </span><span class="kn">import</span> <span class="n">pad_sequence</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">EarlyStop</span>
<div class="viewcode-block" id="NeuralClassifierTrainer">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">[docs]</a>
<span class="k">class</span> <span class="nc">NeuralClassifierTrainer</span><span class="p">:</span>
<span class="k">class</span><span class="w"> </span><span class="nc">NeuralClassifierTrainer</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains a neural network for text classification.</span>
@ -408,7 +408,7 @@
<span class="sd"> according to the evaluation in the held-out validation split (default &#39;../checkpoint/classifier_net.dat&#39;)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">net</span><span class="p">:</span> <span class="s1">&#39;TextClassifierNet&#39;</span><span class="p">,</span>
<span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@ -443,7 +443,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.reset_net_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">[docs]</a>
<span class="k">def</span> <span class="nf">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Reinitialize the network parameters</span>
<span class="sd"> :param vocab_size: the size of the vocabulary</span>
@ -456,7 +456,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Get hyper-parameters for this estimator</span>
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
@ -466,7 +466,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.set_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Set the parameters of this trainer and the learner it is training.</span>
<span class="sd"> In this current version, parameter names for the trainer and learner should</span>
<span class="sd"> be disjoint.</span>
@ -492,14 +492,14 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Gets the device in which the network is allocated</span>
<span class="sd"> :return: device</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
<span class="k">def</span> <span class="nf">_train_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_train_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
@ -519,7 +519,7 @@
<span class="n">status</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_test_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_test_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
@ -537,7 +537,7 @@
<span class="n">status</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__update_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__update_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="n">pbar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">] training epoch=</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;tr-loss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr&quot;</span><span class="p">][</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;tr-acc=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr&quot;</span><span class="p">][</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% &#39;</span>
@ -549,7 +549,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the model according to the given training data.</span>
@ -599,7 +599,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances</span>
@ -612,7 +612,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba">[docs]</a>
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts posterior probabilities for the instances</span>
@ -631,7 +631,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.transform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform">[docs]</a>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the embeddings of the instances</span>
@ -653,7 +653,7 @@
<div class="viewcode-block" id="TorchDataset">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset">[docs]</a>
<span class="k">class</span> <span class="nc">TorchDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">TorchDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms labelled instances into a Torch&#39;s :class:`torch.utils.data.DataLoader` object</span>
@ -661,19 +661,19 @@
<span class="sd"> :param labels: array-like of shape `(n_samples, n_classes)` with the class labels</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span>
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span>
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">&#39;doc&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="n">index</span><span class="p">],</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">}</span>
<div class="viewcode-block" id="TorchDataset.asDataloader">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">[docs]</a>
<span class="k">def</span> <span class="nf">asDataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">pad_length</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">asDataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">pad_length</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Converts the labelled collection into a Torch DataLoader with dynamic padding for</span>
<span class="sd"> the batch</span>
@ -686,7 +686,7 @@
<span class="sd"> :param device: whether to allocate tensors in cpu or in cuda</span>
<span class="sd"> :return: a :class:`torch.utils.data.DataLoader` object</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="s1">&#39;doc&#39;</span><span class="p">][:</span><span class="n">pad_length</span><span class="p">])</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pad_sequence</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">padding_value</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PAD_INDEX&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
@ -704,7 +704,7 @@
<div class="viewcode-block" id="TextClassifierNet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet">[docs]</a>
<span class="k">class</span> <span class="nc">TextClassifierNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">TextClassifierNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract Text classifier (`torch.nn.Module`)</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -712,7 +712,7 @@
<div class="viewcode-block" id="TextClassifierNet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -727,7 +727,7 @@
<div class="viewcode-block" id="TextClassifierNet.forward">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">[docs]</a>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Performs the forward pass.</span>
<span class="sd"> :param x: a batch of instances, typically generated by a torch&#39;s `DataLoader`</span>
@ -741,7 +741,7 @@
<div class="viewcode-block" id="TextClassifierNet.dimensions">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions">[docs]</a>
<span class="k">def</span> <span class="nf">dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Gets the number of dimensions of the embedding space</span>
<span class="sd"> :return: integer</span>
@ -751,7 +751,7 @@
<div class="viewcode-block" id="TextClassifierNet.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba">[docs]</a>
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts posterior probabilities for the instances in `x`</span>
@ -766,7 +766,7 @@
<div class="viewcode-block" id="TextClassifierNet.xavier_uniform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform">[docs]</a>
<span class="k">def</span> <span class="nf">xavier_uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">xavier_uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Performs Xavier initialization of the network parameters</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -778,7 +778,7 @@
<div class="viewcode-block" id="TextClassifierNet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get hyper-parameters for this estimator</span>
@ -788,7 +788,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return the size of the vocabulary</span>
@ -800,7 +800,7 @@
<div class="viewcode-block" id="LSTMnet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet">[docs]</a>
<span class="k">class</span> <span class="nc">LSTMnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">LSTMnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
<span class="sd"> Long Short Term Memory networks.</span>
@ -814,7 +814,7 @@
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
@ -836,7 +836,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">set_size</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">set_size</span><span class="p">):</span>
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span>
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">&#39;lstm_class_nlayers&#39;</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">&#39;hidden_size&#39;</span><span class="p">])</span>
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">&#39;lstm_class_nlayers&#39;</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">&#39;hidden_size&#39;</span><span class="p">])</span>
@ -846,7 +846,7 @@
<div class="viewcode-block" id="LSTMnet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding">[docs]</a>
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -865,7 +865,7 @@
<div class="viewcode-block" id="LSTMnet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get hyper-parameters for this estimator</span>
@ -875,7 +875,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return the size of the vocabulary</span>
@ -887,7 +887,7 @@
<div class="viewcode-block" id="CNNnet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet">[docs]</a>
<span class="k">class</span> <span class="nc">CNNnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">CNNnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
<span class="sd"> Convolutional Neural Networks.</span>
@ -904,7 +904,7 @@
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">kernel_heights</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CNNnet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
@ -929,7 +929,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_heights</span><span class="p">)</span> <span class="o">*</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">conv_layer</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">conv_layer</span><span class="p">):</span>
<span class="n">conv_out</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># conv_out.size() = (batch_size, out_channels, dim, 1)</span>
<span class="n">activation</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv_out</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># activation.size() = (batch_size, out_channels, dim1)</span>
<span class="n">max_out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">activation</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># maxpool_out.size() = (batch_size, out_channels)</span>
@ -937,7 +937,7 @@
<div class="viewcode-block" id="CNNnet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding">[docs]</a>
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -962,7 +962,7 @@
<div class="viewcode-block" id="CNNnet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get hyper-parameters for this estimator</span>
@ -972,7 +972,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return the size of the vocabulary</span>

36
docs/build/html/_modules/quapy/classification/svmperf.html vendored
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@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,22 +370,22 @@
<article class="bd-article">
<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">subprocess</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
<span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">dump_svmlight_file</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">subprocess</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">os</span><span class="w"> </span><span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">subprocess</span><span class="w"> </span><span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">dump_svmlight_file</span>
<div class="viewcode-block" id="SVMperf">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
<span class="k">class</span> <span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;A wrapper for the `SVM-perf package &lt;https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html&gt;`__ by Thorsten Joachims.</span>
<span class="sd"> When using losses for quantification, the source code has to be patched. See</span>
<span class="sd"> the `installation documentation &lt;https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses&gt;`__</span>
@ -407,7 +407,7 @@
<span class="c1"># losses with their respective codes in svm_perf implementation</span>
<span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;01&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;kld&#39;</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;nkld&#39;</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">&#39;q&#39;</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">&#39;qacc&#39;</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">&#39;qf1&#39;</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">&#39;qgm&#39;</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;mae&#39;</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">&#39;mrae&#39;</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> \
<span class="p">(</span><span class="sa">f</span><span class="s1">&#39;path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path;&#39;</span>
<span class="sa">f</span><span class="s1">&#39;did you install svm-perf? &#39;</span>
@ -420,7 +420,7 @@
<div class="viewcode-block" id="SVMperf.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the SVM for the multivariate performance loss</span>
@ -469,7 +469,7 @@
<div class="viewcode-block" id="SVMperf.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances `X`</span>
@ -484,7 +484,7 @@
<div class="viewcode-block" id="SVMperf.decision_function">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
<span class="k">def</span> <span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Evaluate the decision function for the samples in `X`.</span>
@ -520,7 +520,7 @@
<span class="k">return</span> <span class="n">scores</span></div>
<span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;tmpdir&#39;</span><span class="p">):</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>

106
docs/build/html/_modules/quapy/data/base.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,23 +370,23 @@
<article class="bd-article">
<h1>Source code for quapy.data.base</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">cached_property</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">issparse</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">vstack</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RepeatedStratifiedKFold</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">RandomState</span>
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">strprev</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">temp_seed</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">issparse</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">vstack</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RepeatedStratifiedKFold</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">numpy.random</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomState</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">strprev</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">temp_seed</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<div class="viewcode-block" id="LabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection">[docs]</a>
<span class="k">class</span> <span class="nc">LabelledCollection</span><span class="p">:</span>
<span class="k">class</span><span class="w"> </span><span class="nc">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A LabelledCollection is a set of objects each with a label attached to each of them. </span>
<span class="sd"> This class implements several sampling routines and other utilities.</span>
@ -398,7 +398,7 @@
<span class="sd"> (i.e., a prevalence of 0)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">instances</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
@ -417,7 +417,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;_index&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span>
@ -425,7 +425,7 @@
<div class="viewcode-block" id="LabelledCollection.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.load">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge</span>
<span class="sd"> of reading the instances must be specified. This function can be a custom one, or any of the reading functions</span>
@ -442,7 +442,7 @@
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">),</span> <span class="n">classes</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the length of this collection (number of labelled instances)</span>
@ -452,7 +452,7 @@
<div class="viewcode-block" id="LabelledCollection.prevalence">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span>
@ -464,7 +464,7 @@
<div class="viewcode-block" id="LabelledCollection.counts">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a>
<span class="k">def</span> <span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of instances for each of the classes in the codeframe.</span>
@ -475,7 +475,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of classes</span>
@ -484,7 +484,7 @@
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">n_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">n_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of instances</span>
@ -493,7 +493,7 @@
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns True if the number of classes is 2</span>
@ -503,7 +503,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a>
<span class="k">def</span> <span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the</span>
<span class="sd"> prevalence values are not specified, then returns the index of a uniform sampling.</span>
@ -566,7 +566,7 @@
<div class="viewcode-block" id="LabelledCollection.uniform_sampling_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn</span>
<span class="sd"> with replacement.</span>
@ -584,7 +584,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a>
<span class="k">def</span> <span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence</span>
<span class="sd"> values. For each class, the sampling is drawn with replacement.</span>
@ -604,7 +604,7 @@
<div class="viewcode-block" id="LabelledCollection.uniform_sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn</span>
<span class="sd"> with replacement.</span>
@ -619,7 +619,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling_from_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">[docs]</a>
<span class="k">def</span> <span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the</span>
<span class="sd"> index.</span>
@ -634,7 +634,7 @@
<div class="viewcode-block" id="LabelledCollection.split_stratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">[docs]</a>
<span class="k">def</span> <span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired</span>
<span class="sd"> proportion.</span>
@ -656,7 +656,7 @@
<div class="viewcode-block" id="LabelledCollection.split_random">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_random">[docs]</a>
<span class="k">def</span> <span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired</span>
<span class="sd"> proportion.</span>
@ -683,7 +683,7 @@
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of this collection with another collection.</span>
<span class="sd"> Both labelled collections must have the same classes.</span>
@ -699,7 +699,7 @@
<div class="viewcode-block" id="LabelledCollection.join">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.join">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">&#39;LabelledCollection&#39;</span><span class="p">]):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">&#39;LabelledCollection&#39;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of the collections given in input.</span>
@ -742,7 +742,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets an array-like with the classes used in this collection</span>
@ -751,7 +751,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span>
@ -762,7 +762,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from</span>
<span class="sd"> a :class:`LabelledCollection` object.</span>
@ -772,7 +772,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.instances</span>
@ -781,7 +781,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.labels</span>
@ -790,7 +790,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.prevalence()</span>
@ -801,7 +801,7 @@
<div class="viewcode-block" id="LabelledCollection.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.stats">[docs]</a>
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</span>
@ -836,7 +836,7 @@
<div class="viewcode-block" id="LabelledCollection.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a>
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation.</span>
@ -852,7 +852,7 @@
<span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">repr</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;&lt;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_instances</span><span class="si">}</span><span class="s1"> instances (dtype=</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="si">}</span><span class="s1">), &#39;</span>
<span class="nb">repr</span><span class="o">+=</span><span class="sa">f</span><span class="s1">&#39;n_classes=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="si">}</span><span class="s1">, prevalence=</span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span><span class="si">}</span><span class="s1">&gt;&#39;</span>
<span class="k">return</span> <span class="nb">repr</span></div>
@ -861,7 +861,7 @@
<div class="viewcode-block" id="Dataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset">[docs]</a>
<span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span>
@ -871,7 +871,7 @@
<span class="sd"> :param name: a string representing the name of the dataset</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">&#39;incompatible labels in training and test collections&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">training</span>
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">test</span>
@ -881,7 +881,7 @@
<div class="viewcode-block" id="Dataset.SplitStratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.SplitStratified">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.</span>
<span class="sd"> See :meth:`LabelledCollection.split_stratified`</span>
@ -894,7 +894,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The classes according to which the training collection is labelled</span>
@ -903,7 +903,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of classes according to which the training collection is labelled</span>
@ -912,7 +912,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">n_classes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns True if the training collection is labelled according to two classes</span>
@ -923,7 +923,7 @@
<div class="viewcode-block" id="Dataset.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.load">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.</span>
<span class="sd"> The function in charge of reading the instances must be specified. This function can be a custom one, or any of</span>
@ -945,7 +945,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span>
@ -954,7 +954,7 @@
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Alias to `self.training` and `self.test`</span>
@ -965,7 +965,7 @@
<div class="viewcode-block" id="Dataset.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.stats">[docs]</a>
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</span>
@ -992,7 +992,7 @@
<div class="viewcode-block" id="Dataset.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.kFCV">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around</span>
<span class="sd"> :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.</span>
@ -1009,7 +1009,7 @@
<div class="viewcode-block" id="Dataset.reduce">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.reduce">[docs]</a>
<span class="k">def</span> <span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span>
@ -1030,7 +1030,7 @@
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;training=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="si">}</span><span class="s1">; test=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="si">}</span><span class="s1">&#39;</span></div>
</pre></div>

84
docs/build/html/_modules/quapy/data/datasets.html vendored
View File

@ -29,7 +29,7 @@
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<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
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@ -370,28 +370,28 @@
<article class="bd-article">
<h1>Source code for quapy.data.datasets</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">import</span> <span class="nn">zipfile</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.data.preprocessing</span> <span class="kn">import</span> <span class="n">text2tfidf</span><span class="p">,</span> <span class="n">reduce_columns</span>
<span class="kn">from</span> <span class="nn">quapy.data.preprocessing</span> <span class="kn">import</span> <span class="n">standardize</span> <span class="k">as</span> <span class="n">standardizer</span>
<span class="kn">from</span> <span class="nn">quapy.data.reader</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">download_file_if_not_exists</span><span class="p">,</span> <span class="n">download_file</span><span class="p">,</span> <span class="n">get_quapy_home</span><span class="p">,</span> <span class="n">pickled_resource</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">zipfile</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">join</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">text2tfidf</span><span class="p">,</span> <span class="n">reduce_columns</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">standardize</span> <span class="k">as</span> <span class="n">standardizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.reader</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">download_file_if_not_exists</span><span class="p">,</span> <span class="n">download_file</span><span class="p">,</span> <span class="n">get_quapy_home</span><span class="p">,</span> <span class="n">pickled_resource</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="k">def</span> <span class="nf">_fetch_ucirepo</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_fetch_ucirepo</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">ucimlrepo</span> <span class="kn">import</span> <span class="n">fetch_ucirepo</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ucimlrepo</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_ucirepo</span>
<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
<span class="s2">&quot;UCI dataset fetching requires the optional &#39;ucimlrepo&#39; package. &quot;</span>
<span class="s2">&quot;Install it to use fetch_UCIBinaryDataset or fetch_UCIMulticlassDataset.&quot;</span>
<span class="p">)</span> <span class="kn">from</span> <span class="nn">exc</span>
<span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
<span class="k">return</span> <span class="n">fetch_ucirepo</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
@ -498,7 +498,7 @@
<div class="viewcode-block" id="fetch_reviews">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_reviews">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a Reviews dataset as a Dataset instance, as used in</span>
<span class="sd"> `Esuli, A., Moreo, A., and Sebastiani, F. &quot;A recurrent neural network for sentiment quantification.&quot;</span>
@ -548,7 +548,7 @@
<div class="viewcode-block" id="fetch_twitter">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_twitter">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a Twitter dataset as a :class:`quapy.data.base.Dataset` instance, as used in:</span>
<span class="sd"> `Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.</span>
@ -626,7 +626,7 @@
<div class="viewcode-block" id="fetch_UCIBinaryDataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryDataset">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI dataset as an instance of :class:`quapy.data.base.Dataset`, as used in</span>
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).</span>
@ -660,7 +660,7 @@
<div class="viewcode-block" id="fetch_UCIBinaryLabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryLabelledCollection">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in</span>
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).</span>
@ -853,7 +853,7 @@
<span class="n">file</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s2">&quot;uci_datasets&quot;</span><span class="p">,</span> <span class="n">dataset_group</span> <span class="o">+</span> <span class="s2">&quot;.pkl&quot;</span><span class="p">)</span>
<span class="nd">@contextmanager</span>
<span class="k">def</span> <span class="nf">download_tmp_file</span><span class="p">(</span><span class="n">url_group</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_tmp_file</span><span class="p">(</span><span class="n">url_group</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Download a data file for a group of datasets temporarely.</span>
<span class="sd"> When used as a context, the file is removed once the context exits.</span>
@ -871,7 +871,7 @@
<span class="k">finally</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">group</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">group</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Download the data to be pickled for a dataset group. Use the `fetch_ucirepo` api when possible.</span>
@ -937,7 +937,7 @@
<span class="k">return</span> <span class="n">data</span>
<span class="k">def</span> <span class="nf">binarize_data</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binarize_data</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Filter and transform data to extract a binary dataset.</span>
@ -982,7 +982,7 @@
<div class="viewcode-block" id="fetch_UCIMulticlassDataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassDataset">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassDataset</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIMulticlassDataset</span><span class="p">(</span>
<span class="n">dataset_name</span><span class="p">,</span>
<span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">min_test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
@ -1045,7 +1045,7 @@
<div class="viewcode-block" id="fetch_UCIMulticlassLabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_class_support</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_class_support</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.</span>
@ -1148,7 +1148,7 @@
<span class="n">file</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;uci_multiclass&#39;</span><span class="p">,</span> <span class="n">dataset_name</span><span class="o">+</span><span class="s1">&#39;.pkl&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">dummify_categorical_features</span><span class="p">(</span><span class="n">df_features</span><span class="p">,</span> <span class="n">dataset_id</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">dummify_categorical_features</span><span class="p">(</span><span class="n">df_features</span><span class="p">,</span> <span class="n">dataset_id</span><span class="p">):</span>
<span class="n">categorical_features</span> <span class="o">=</span> <span class="p">{</span>
<span class="mi">158</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;S1&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;S2&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">,</span> <span class="s2">&quot;S3&quot;</span><span class="p">,</span> <span class="s2">&quot;C3&quot;</span><span class="p">,</span> <span class="s2">&quot;S4&quot;</span><span class="p">,</span> <span class="s2">&quot;C4&quot;</span><span class="p">,</span> <span class="s2">&quot;S5&quot;</span><span class="p">,</span> <span class="s2">&quot;C5&quot;</span><span class="p">],</span> <span class="c1"># poker_hand</span>
<span class="p">}</span>
@ -1162,7 +1162,7 @@
<span class="k">return</span> <span class="n">X</span>
<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">_fetch_ucirepo</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="nb">id</span><span class="p">)</span>
<span class="n">X_df</span> <span class="o">=</span> <span class="n">dummify_categorical_features</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">features</span><span class="p">,</span> <span class="nb">id</span><span class="p">)</span>
@ -1175,7 +1175,7 @@
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">filter_classes</span><span class="p">(</span><span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">min_class_support</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">filter_classes</span><span class="p">(</span><span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">min_class_support</span><span class="p">):</span>
<span class="k">if</span> <span class="n">min_class_support</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">min_class_support</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">:</span>
<span class="k">return</span> <span class="n">data</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">min_class_support</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
@ -1209,18 +1209,18 @@
<span class="k">def</span> <span class="nf">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span> <span class="n">astype</span><span class="o">=</span><span class="nb">float</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span> <span class="n">astype</span><span class="o">=</span><span class="nb">float</span><span class="p">):</span>
<span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">repl</span><span class="p">[</span><span class="n">x</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">astype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_array_replace</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;yes&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;no&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_array_replace</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;yes&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;no&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}):</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">repl</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">arr</span><span class="p">[</span><span class="n">arr</span> <span class="o">==</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<div class="viewcode-block" id="fetch_lequa2022">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_lequa2022">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_lequa2022</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_lequa2022</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads the official datasets provided for the `LeQua 2022 &lt;https://lequa2022.github.io/index&gt;`_ competition.</span>
<span class="sd"> In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification</span>
@ -1246,7 +1246,7 @@
<span class="sd"> that return a series of samples stored in a directory which are labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.data._lequa</span> <span class="kn">import</span> <span class="n">load_raw_documents</span><span class="p">,</span> <span class="n">load_vector_documents_2022</span><span class="p">,</span> <span class="n">SamplesFromDir</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._lequa</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_raw_documents</span><span class="p">,</span> <span class="n">load_vector_documents_2022</span><span class="p">,</span> <span class="n">SamplesFromDir</span>
<span class="k">assert</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">LEQUA2022_TASKS</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;Unknown task </span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">. Valid ones are </span><span class="si">{</span><span class="n">LEQUA2022_TASKS</span><span class="si">}</span><span class="s1">&#39;</span>
@ -1260,7 +1260,7 @@
<span class="n">lequa_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;lequa2022&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span> <span class="o">+</span> <span class="s1">&#39;_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1296,7 +1296,7 @@
<div class="viewcode-block" id="fetch_lequa2024">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_lequa2024">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_lequa2024</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">merge_T3</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_lequa2024</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">merge_T3</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads the official datasets provided for the `LeQua 2024 &lt;https://lequa2024.github.io/index&gt;`_ competition.</span>
<span class="sd"> LeQua 2024 defines four tasks (T1, T2, T3, T4) related to the problem of quantification;</span>
@ -1327,7 +1327,7 @@
<span class="sd"> that return a series of samples stored in a directory which are labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.data._lequa</span> <span class="kn">import</span> <span class="n">load_vector_documents_2024</span><span class="p">,</span> <span class="n">SamplesFromDir</span><span class="p">,</span> <span class="n">LabelledCollectionsFromDir</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._lequa</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_vector_documents_2024</span><span class="p">,</span> <span class="n">SamplesFromDir</span><span class="p">,</span> <span class="n">LabelledCollectionsFromDir</span>
<span class="k">assert</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">LEQUA2024_TASKS</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;Unknown task </span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">. Valid ones are </span><span class="si">{</span><span class="n">LEQUA2024_TASKS</span><span class="si">}</span><span class="s1">&#39;</span>
@ -1346,7 +1346,7 @@
<span class="n">lequa_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;lequa2024&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span> <span class="o">+</span> <span class="s1">&#39;_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1386,7 +1386,7 @@
<div class="viewcode-block" id="fetch_IFCB">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_IFCB">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_IFCB</span><span class="p">(</span><span class="n">single_sample_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_IFCB</span><span class="p">(</span><span class="n">single_sample_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads the IFCB dataset for quantification from `Zenodo &lt;https://zenodo.org/records/10036244&gt;`_ (for more</span>
<span class="sd"> information on this dataset, please follow the zenodo link).</span>
@ -1411,7 +1411,7 @@
<span class="sd"> i.e., a sampling protocol that returns a series of samples labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.data._ifcb</span> <span class="kn">import</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">,</span> <span class="n">IFCBTestSamples</span><span class="p">,</span> <span class="n">get_sample_list</span><span class="p">,</span> <span class="n">generate_modelselection_split</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._ifcb</span><span class="w"> </span><span class="kn">import</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">,</span> <span class="n">IFCBTestSamples</span><span class="p">,</span> <span class="n">get_sample_list</span><span class="p">,</span> <span class="n">generate_modelselection_split</span>
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
@ -1423,7 +1423,7 @@
<span class="n">ifcb_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;ifcb&#39;</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="s1">&#39;ifcb_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1468,7 +1468,7 @@
<span class="k">def</span> <span class="nf">_fetch_image_embedding_splits</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">]:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_fetch_image_embedding_splits</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a pre-generated embedding set (train, val, or test) of an image dataset from `Zenodo &lt;https://zenodo.org/records/21131944&gt;`_.</span>
<span class="sd"> </span>
@ -1498,7 +1498,7 @@
<span class="n">trained_network</span> <span class="o">=</span> <span class="n">dataset_network</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">download_embedding_npz</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">trained_network</span><span class="p">,</span> <span class="n">embedding</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_embedding_npz</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">trained_network</span><span class="p">,</span> <span class="n">embedding</span><span class="p">):</span>
<span class="n">target_file</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">trained_network</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">embedding</span><span class="si">}</span><span class="s1">.npz&#39;</span>
<span class="n">URL</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;https://zenodo.org/records/21131944/files/</span><span class="si">{</span><span class="n">target_file</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;image&#39;</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@ -1519,7 +1519,7 @@
<div class="viewcode-block" id="fetch_image_embeddings">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_image_embeddings">[docs]</a>
<span class="k">def</span> <span class="nf">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">heldout_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">heldout_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads an image dataset with pre-generated embeddings. Available datasets include:</span>

22
docs/build/html/_modules/quapy/data/reader.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,16 +370,16 @@
<article class="bd-article">
<h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">dok_matrix</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">dok_matrix</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<div class="viewcode-block" id="from_text">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a>
<span class="k">def</span> <span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a labelled colletion of documents.</span>
<span class="sd"> File fomart &lt;0 or 1&gt;\t&lt;document&gt;\n</span>
@ -413,7 +413,7 @@
<div class="viewcode-block" id="from_sparse">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a>
<span class="k">def</span> <span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a labelled collection of real-valued instances expressed in sparse format</span>
<span class="sd"> File format &lt;-1 or 0 or 1&gt;[\s col(int):val(float)]\n</span>
@ -422,7 +422,7 @@
<span class="sd"> :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;:&#39;</span><span class="p">)</span>
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
@ -450,7 +450,7 @@
<div class="viewcode-block" id="from_csv">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a>
<span class="k">def</span> <span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a csv file in which columns are separated by &#39;,&#39;.</span>
<span class="sd"> File format &lt;label&gt;,&lt;feat1&gt;,&lt;feat2&gt;,...,&lt;featn&gt;\n</span>
@ -473,7 +473,7 @@
<div class="viewcode-block" id="reindex_labels">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a>
<span class="k">def</span> <span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.</span>
<span class="sd"> E.g.:</span>
@ -496,7 +496,7 @@
<div class="viewcode-block" id="binarize">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a>
<span class="k">def</span> <span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span>

106
docs/build/html/_modules/quapy/functional.html vendored
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@ -29,7 +29,7 @@
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@ -370,17 +370,17 @@
<article class="bd-article">
<h1>Source code for quapy.functional</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">lru_cache</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span> <span class="nn">numpy.typing</span> <span class="kn">import</span> <span class="n">ArrayLike</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">lru_cache</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">ArrayLike</span>
<span class="kn">import</span> <span class="nn">scipy</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">scipy</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="c1"># ------------------------------------------------------------------------------------------</span>
@ -389,7 +389,7 @@
<div class="viewcode-block" id="classes_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.classes_from_labels">[docs]</a>
<span class="k">def</span> <span class="nf">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Obtains a np.ndarray with the (sorted) classes</span>
<span class="sd"> :param labels: array-like with the instances&#39; labels</span>
@ -403,7 +403,7 @@
<div class="viewcode-block" id="num_classes_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.num_classes_from_labels">[docs]</a>
<span class="k">def</span> <span class="nf">num_classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">num_classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Obtains the number of classes from an array-like of instance&#39;s labels</span>
<span class="sd"> :param labels: array-like with the instances&#39; labels</span>
@ -418,7 +418,7 @@
<div class="viewcode-block" id="counts_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.counts_from_labels">[docs]</a>
<span class="k">def</span> <span class="nf">counts_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">counts_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the raw count values from a vector of labels.</span>
@ -439,7 +439,7 @@
<div class="viewcode-block" id="prevalence_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_labels">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the prevalence values from a vector of labels.</span>
@ -457,7 +457,7 @@
<div class="viewcode-block" id="prevalence_from_probabilities">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_probabilities">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence_from_probabilities</span><span class="p">(</span><span class="n">posteriors</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">binarize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_from_probabilities</span><span class="p">(</span><span class="n">posteriors</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">binarize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a vector of prevalence values from a matrix of posterior probabilities.</span>
@ -481,7 +481,7 @@
<div class="viewcode-block" id="num_prevalence_combinations">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.num_prevalence_combinations">[docs]</a>
<span class="k">def</span> <span class="nf">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally</span>
<span class="sd"> distant prevalence values are generated and `n_repeats` repetitions are requested.</span>
@ -510,7 +510,7 @@
<div class="viewcode-block" id="get_nprevpoints_approximation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_nprevpoints_approximation">[docs]</a>
<span class="k">def</span> <span class="nf">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">combinations_budget</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">combinations_budget</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so</span>
<span class="sd"> that the number of valid prevalence values generated as combinations of prevalence points (points in a</span>
@ -538,7 +538,7 @@
<div class="viewcode-block" id="as_binary_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.as_binary_prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">as_binary_prevalence</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span> <span class="n">clip_if_necessary</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">as_binary_prevalence</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span> <span class="n">clip_if_necessary</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two</span>
<span class="sd"> values representing a binary distribution.</span>
@ -560,7 +560,7 @@
<div class="viewcode-block" id="strprev">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.strprev">[docs]</a>
<span class="k">def</span> <span class="nf">strprev</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">prec</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">strprev</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">prec</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a string representation for a prevalence vector. E.g.,</span>
@ -577,7 +577,7 @@
<div class="viewcode-block" id="check_prevalence_vector">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.check_prevalence_vector">[docs]</a>
<span class="k">def</span> <span class="nf">check_prevalence_vector</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">raise_exception</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">aggr</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">check_prevalence_vector</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">raise_exception</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">aggr</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks that `prevalences` is a valid prevalence vector, i.e., it contains values in [0,1] and</span>
<span class="sd"> the values sum up to 1. In other words, verifies that the `prevalences` vectors lies in the</span>
@ -619,7 +619,7 @@
<div class="viewcode-block" id="uniform_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.uniform_prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a vector representing the uniform distribution for `n_classes`</span>
@ -635,7 +635,7 @@
<div class="viewcode-block" id="normalize_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.normalize_prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Normalizes a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in</span>
<span class="sd"> cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in</span>
@ -678,7 +678,7 @@
<div class="viewcode-block" id="l1_norm">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.l1_norm">[docs]</a>
<span class="k">def</span> <span class="nf">l1_norm</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">l1_norm</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies L1 normalization to the `unnormalized_arr` so that it becomes a valid prevalence</span>
<span class="sd"> vector. Zero vectors are mapped onto the uniform distribution. Raises an exception if</span>
@ -704,7 +704,7 @@
<div class="viewcode-block" id="clip">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.clip">[docs]</a>
<span class="k">def</span> <span class="nf">clip</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">clip</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Clips the values in [0,1] and then applies the L1 normalization.</span>
@ -719,7 +719,7 @@
<div class="viewcode-block" id="projection_simplex_sort">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.projection_simplex_sort">[docs]</a>
<span class="k">def</span> <span class="nf">projection_simplex_sort</span><span class="p">(</span><span class="n">unnormalized_arr</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">projection_simplex_sort</span><span class="p">(</span><span class="n">unnormalized_arr</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Projects a point onto the probability simplex.</span>
<span class="sd"> The code is adapted from Mathieu Blondel&#39;s BSD-licensed</span>
@ -747,7 +747,7 @@
<div class="viewcode-block" id="softmax">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.softmax">[docs]</a>
<span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">softmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the softmax function to all vectors even if the original vectors were valid distributions.</span>
<span class="sd"> If you want to leave valid vectors untouched, use condsoftmax instead.</span>
@ -762,7 +762,7 @@
<div class="viewcode-block" id="condsoftmax">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.condsoftmax">[docs]</a>
<span class="k">def</span> <span class="nf">condsoftmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condsoftmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the softmax function only to vectors that do not represent valid distributions.</span>
@ -787,7 +787,7 @@
<div class="viewcode-block" id="HellingerDistance">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.HellingerDistance">[docs]</a>
<span class="k">def</span> <span class="nf">HellingerDistance</span><span class="p">(</span><span class="n">P</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Q</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">HellingerDistance</span><span class="p">(</span><span class="n">P</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Q</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.</span>
<span class="sd"> The HD for two discrete distributions of `k` bins is defined as:</span>
@ -805,7 +805,7 @@
<div class="viewcode-block" id="TopsoeDistance">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.TopsoeDistance">[docs]</a>
<span class="k">def</span> <span class="nf">TopsoeDistance</span><span class="p">(</span><span class="n">P</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Q</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-20</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">TopsoeDistance</span><span class="p">(</span><span class="n">P</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">Q</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-20</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Topsoe distance between two (discretized) distributions `P` and `Q`.</span>
<span class="sd"> The Topsoe distance for two discrete distributions of `k` bins is defined as:</span>
@ -824,7 +824,7 @@
<div class="viewcode-block" id="get_divergence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_divergence">[docs]</a>
<span class="k">def</span> <span class="nf">get_divergence</span><span class="p">(</span><span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_divergence</span><span class="p">(</span><span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Guarantees that the divergence received as argument is a function. That is, if this argument is already</span>
<span class="sd"> a callable, then it is returned, if it is instead a string, then tries to instantiate the corresponding</span>
@ -853,7 +853,7 @@
<div class="viewcode-block" id="argmin_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.argmin_prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;optim_minimize&quot;</span><span class="p">,</span> <span class="s2">&quot;linear_search&quot;</span><span class="p">,</span> <span class="s2">&quot;ternary_search&quot;</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
@ -880,7 +880,7 @@
<div class="viewcode-block" id="optim_minimize">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.optim_minimize">[docs]</a>
<span class="k">def</span> <span class="nf">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">return_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">return_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex</span>
<span class="sd"> that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy&#39;s</span>
@ -892,7 +892,7 @@
<span class="sd"> :return: (ndarray) the best prevalence vector found or a tuple which also contains the value of the loss</span>
<span class="sd"> if return_loss=True</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">optimize</span>
<span class="c1"># the initial point is set as the uniform distribution</span>
<span class="n">uniform_distribution</span> <span class="o">=</span> <span class="n">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
@ -911,7 +911,7 @@
<div class="viewcode-block" id="linear_search">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.linear_search">[docs]</a>
<span class="k">def</span> <span class="nf">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring</span>
<span class="sd"> the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the</span>
@ -935,7 +935,7 @@
<div class="viewcode-block" id="ternary_search">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ternary_search">[docs]</a>
<span class="k">def</span> <span class="nf">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Performs a ternary search for the best prevalence value in binary problems.</span>
<span class="sd"> This search assumes the loss is unimodal over the interval [0,1].</span>
@ -971,7 +971,7 @@
<div class="viewcode-block" id="prevalence_linspace">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_linspace">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence_linspace</span><span class="p">(</span><span class="n">grid_points</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_linspace</span><span class="p">(</span><span class="n">grid_points</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Produces an array of uniformly separated values of prevalence.</span>
<span class="sd"> By default, produces an array of 21 prevalence values, with</span>
@ -996,7 +996,7 @@
<div class="viewcode-block" id="uniform_prevalence_sampling">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.uniform_prevalence_sampling">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_prevalence_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_prevalence_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the `Kraemer algorithm &lt;http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf&gt;`_</span>
<span class="sd"> for sampling uniformly at random from the unit simplex. This implementation is adapted from this</span>
@ -1032,7 +1032,7 @@
<div class="viewcode-block" id="solve_adjustment_binary">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.solve_adjustment_binary">[docs]</a>
<span class="k">def</span> <span class="nf">solve_adjustment_binary</span><span class="p">(</span><span class="n">prevalence_estim</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">tpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">fpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">clip</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">solve_adjustment_binary</span><span class="p">(</span><span class="n">prevalence_estim</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">tpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">fpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">clip</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the</span>
<span class="sd"> positive class `p` comes down to computing:</span>
@ -1059,7 +1059,7 @@
<div class="viewcode-block" id="solve_adjustment">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.solve_adjustment">[docs]</a>
<span class="k">def</span> <span class="nf">solve_adjustment</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="nf">solve_adjustment</span><span class="p">(</span>
<span class="n">class_conditional_rates</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">unadjusted_counts</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;inversion&quot;</span><span class="p">,</span> <span class="s2">&quot;invariant-ratio&quot;</span><span class="p">],</span>
@ -1116,7 +1116,7 @@
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;unknown </span><span class="si">{</span><span class="n">method</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">solver</span> <span class="o">==</span> <span class="s2">&quot;minimize&quot;</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span> <span class="o">@</span> <span class="n">prev</span> <span class="o">-</span> <span class="n">B</span><span class="p">)</span>
<span class="k">return</span> <span class="n">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">elif</span> <span class="n">solver</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;exact-raise&quot;</span><span class="p">,</span> <span class="s2">&quot;exact-cc&quot;</span><span class="p">]:</span>
@ -1144,7 +1144,7 @@
<div class="viewcode-block" id="CompositionalTransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CompositionalTransformation">[docs]</a>
<span class="k">class</span> <span class="nc">CompositionalTransformation</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">CompositionalTransformation</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of transformations for compositional data.</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -1152,13 +1152,13 @@
<span class="n">EPSILON</span> <span class="o">=</span> <span class="mf">1e-12</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="o">...</span>
<div class="viewcode-block" id="CompositionalTransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CompositionalTransformation.inverse">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="o">...</span></div>
</div>
@ -1166,12 +1166,12 @@
<div class="viewcode-block" id="CLRtransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CLRtransformation">[docs]</a>
<span class="k">class</span> <span class="nc">CLRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">CLRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Centered log-ratio (CLR) transformation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">smooth</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">EPSILON</span><span class="p">)</span>
<span class="n">geometric_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
@ -1179,7 +1179,7 @@
<div class="viewcode-block" id="CLRtransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CLRtransformation.inverse">[docs]</a>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
</div>
@ -1187,12 +1187,12 @@
<div class="viewcode-block" id="ILRtransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation">[docs]</a>
<span class="k">class</span> <span class="nc">ILRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ILRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Isometric log-ratio (ILR) transformation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">smooth</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">EPSILON</span><span class="p">)</span>
<span class="n">basis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_V</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@ -1200,7 +1200,7 @@
<div class="viewcode-block" id="ILRtransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation.inverse">[docs]</a>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
<span class="n">basis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_V</span><span class="p">(</span><span class="n">Z</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">logp</span> <span class="o">=</span> <span class="n">Z</span> <span class="o">@</span> <span class="n">basis</span>
@ -1211,7 +1211,7 @@
<div class="viewcode-block" id="ILRtransformation.get_V">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation.get_V">[docs]</a>
<span class="nd">@lru_cache</span><span class="p">(</span><span class="n">maxsize</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_V</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_V</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="n">helmert</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="n">helmert</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
@ -1224,7 +1224,7 @@
<div class="viewcode-block" id="normalized_entropy">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.normalized_entropy">[docs]</a>
<span class="k">def</span> <span class="nf">normalized_entropy</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">normalized_entropy</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the normalized Shannon entropy of a prevalence vector.</span>
@ -1240,7 +1240,7 @@
<div class="viewcode-block" id="antagonistic_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.antagonistic_prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">antagonistic_prevalence</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">strength</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">antagonistic_prevalence</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">strength</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reflects a prevalence vector in ILR space and maps it back to the simplex.</span>
@ -1257,7 +1257,7 @@
<div class="viewcode-block" id="in_simplex">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.in_simplex">[docs]</a>
<span class="k">def</span> <span class="nf">in_simplex</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">in_simplex</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks whether points lie in the probability simplex.</span>

62
docs/build/html/_modules/quapy/method/_neural.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
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<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,24 +370,24 @@
<article class="bd-article">
<h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">import</span> <span class="nn">random</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">MSELoss</span>
<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">relu</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="kn">import</span> <span class="n">MSELoss</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">relu</span>
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">UPP</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">UPP</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">EarlyStop</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<div class="viewcode-block" id="QuaNetTrainer">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer">[docs]</a>
<span class="k">class</span> <span class="nc">QuaNetTrainer</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetTrainer</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implementation of `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_, a neural network for</span>
<span class="sd"> quantification. This implementation uses `PyTorch &lt;https://pytorch.org/&gt;`_ and can take advantage of GPU</span>
@ -439,7 +439,7 @@
<span class="sd"> :param device: string, indicate &quot;cpu&quot; or &quot;cuda&quot;</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">classifier</span><span class="p">,</span>
<span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -492,7 +492,7 @@
<div class="viewcode-block" id="QuaNetTrainer.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains QuaNet.</span>
@ -574,7 +574,7 @@
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_get_aggregative_estims</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_aggregative_estims</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
<span class="n">label_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">prevs_estim</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
@ -587,7 +587,7 @@
<div class="viewcode-block" id="QuaNetTrainer.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span>
@ -600,7 +600,7 @@
<span class="k">return</span> <span class="n">prevalence</span></div>
<span class="k">def</span> <span class="nf">_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="n">mse_loss</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
@ -652,7 +652,7 @@
<div class="viewcode-block" id="QuaNetTrainer.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="n">classifier_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
<span class="n">classifier_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__&#39;</span><span class="o">+</span><span class="n">k</span><span class="p">:</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">classifier_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">classifier_params</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">}</span></div>
@ -660,7 +660,7 @@
<div class="viewcode-block" id="QuaNetTrainer.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="n">learner_params</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">:</span>
@ -672,7 +672,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">learner_params</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
<span class="n">quanet_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">quanet_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">learner_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">learner_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">intersection</span> <span class="o">=</span> <span class="n">quanet_keys</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">learner_keys</span><span class="p">)</span>
@ -682,7 +682,7 @@
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">[docs]</a>
<span class="k">def</span> <span class="nf">clean_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Removes the checkpoint</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -691,23 +691,23 @@
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint_dir">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">[docs]</a>
<span class="k">def</span> <span class="nf">clean_checkpoint_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Removes anything contained in the checkpoint directory</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">shutil</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span></div>
<div class="viewcode-block" id="mae_loss">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.mae_loss">[docs]</a>
<span class="k">def</span> <span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Torch-like wrapper for the Mean Absolute Error</span>
@ -721,7 +721,7 @@
<div class="viewcode-block" id="QuaNetModule">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule">[docs]</a>
<span class="k">class</span> <span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_ forward pass.</span>
<span class="sd"> See :class:`QuaNetTrainer` for training QuaNet.</span>
@ -738,7 +738,7 @@
<span class="sd"> :param order_by: integer, class for which the document embeddings are to be sorted</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">doc_embedding_size</span><span class="p">,</span>
<span class="n">n_classes</span><span class="p">,</span>
<span class="n">stats_size</span><span class="p">,</span>
@ -773,10 +773,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
@ -786,7 +786,7 @@
<div class="viewcode-block" id="QuaNetModule.forward">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule.forward">[docs]</a>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
<span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span>
<span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>

72
docs/build/html/_modules/quapy/method/_threshold_optim.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
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@ -370,19 +370,19 @@
<article class="bd-article">
<h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">abstractmethod</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span>
<div class="viewcode-block" id="ThresholdOptimization">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a>
<span class="k">class</span> <span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -407,14 +407,14 @@
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<div class="viewcode-block" id="ThresholdOptimization.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the criterion according to which the threshold should be selected.</span>
<span class="sd"> This function should return the (float) score to be minimized.</span>
@ -428,7 +428,7 @@
<div class="viewcode-block" id="ThresholdOptimization.discard">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a>
<span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span>
@ -440,7 +440,7 @@
<span class="k">def</span> <span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Seeks for the best `tpr` and `fpr` according to the score obtained at different</span>
<span class="sd"> decision thresholds. The scoring function is implemented in function `_condition`.</span>
@ -477,7 +477,7 @@
<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
<span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span>
<span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length &gt; 1</span>
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@ -486,26 +486,26 @@
<span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">FP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">FN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">TN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span>
<span class="k">def</span> <span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FN</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FN</span><span class="p">):</span>
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">TP</span> <span class="o">/</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FN</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
<span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">FP</span> <span class="o">/</span> <span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
<div class="viewcode-block" id="ThresholdOptimization.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
<span class="c1"># the standard behavior is to keep the best threshold only</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
@ -514,7 +514,7 @@
<div class="viewcode-block" id="ThresholdOptimization.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div>
</div>
@ -523,7 +523,7 @@
<div class="viewcode-block" id="T50">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a>
<span class="k">class</span> <span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -545,12 +545,12 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="T50.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a>
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div>
</div>
@ -558,7 +558,7 @@
<div class="viewcode-block" id="MAX">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a>
<span class="k">class</span> <span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -578,12 +578,12 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MAX.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a>
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="c1"># MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div>
</div>
@ -592,7 +592,7 @@
<div class="viewcode-block" id="X">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a>
<span class="k">class</span> <span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -612,12 +612,12 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="X.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a>
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div>
</div>
@ -625,7 +625,7 @@
<div class="viewcode-block" id="MS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a>
<span class="k">class</span> <span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -644,18 +644,18 @@
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a>
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span></div>
<div class="viewcode-block" id="MS.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
<span class="c1"># keeps all candidates</span>
<span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@ -667,7 +667,7 @@
<div class="viewcode-block" id="MS.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@ -678,7 +678,7 @@
<div class="viewcode-block" id="MS2">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a>
<span class="k">class</span> <span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -698,12 +698,12 @@
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS2.discard">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a>
<span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span><span class="o">-</span><span class="n">fpr</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.25</span></div>
</div>

262
docs/build/html/_modules/quapy/method/aggregative.html vendored
View File

@ -29,7 +29,7 @@
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@ -370,25 +370,25 @@
<article class="bd-article">
<h1>Source code for quapy.method.aggregative</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibratedClassifierCV</span>
<span class="kn">from</span> <span class="nn">sklearn.exceptions</span> <span class="kn">import</span> <span class="n">NotFittedError</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_is_fitted</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">CalibratedClassifierCV</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.exceptions</span><span class="w"> </span><span class="kn">import</span> <span class="n">NotFittedError</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils.validation</span><span class="w"> </span><span class="kn">import</span> <span class="n">check_is_fitted</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">get_divergence</span>
<span class="kn">from</span> <span class="nn">quapy.classification.svmperf</span> <span class="kn">import</span> <span class="n">SVMperf</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">OneVsAllGeneric</span>
<span class="kn">from</span> <span class="nn">quapy.method._helper</span> <span class="kn">import</span> <span class="p">(</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_divergence</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.classification.svmperf</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVMperf</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">OneVsAllGeneric</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">_get_abstention_calibrators</span><span class="p">,</span>
<span class="n">_get_cvxpy</span><span class="p">,</span>
<span class="n">_rlls_check_mode</span><span class="p">,</span>
@ -406,7 +406,7 @@
<div class="viewcode-block" id="AggregativeQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">AggregativeQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AggregativeQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for quantification methods that base their estimations on the aggregation of classification</span>
<span class="sd"> results. Aggregative quantifiers implement a pipeline that consists of generating classification predictions</span>
@ -434,7 +434,7 @@
<span class="sd"> the training data be wasted.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">classifier</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="n">BaseEstimator</span><span class="p">],</span>
<span class="n">fit_classifier</span><span class="p">:</span><span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">val_split</span><span class="p">:</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span><span class="nb">float</span><span class="p">,</span><span class="nb">tuple</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
@ -495,7 +495,7 @@
<span class="k">assert</span> <span class="n">fitted</span><span class="p">,</span> <span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">fit_classifier</span><span class="si">=}</span><span class="s1"> requires the classifier to be already trained, &#39;</span>
<span class="sa">f</span><span class="s1">&#39;but this does not seem to be&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements any check to be performed in the parameters of the init method before undertaking</span>
<span class="sd"> the training of the quantifier. This is made as to allow for a quick execution stop when the</span>
@ -505,7 +505,7 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">_check_non_empty_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_non_empty_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Asserts all classes have positive instances.</span>
@ -522,7 +522,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregative quantifier. This comes down to training a classifier (if requested) and an</span>
<span class="sd"> aggregation function.</span>
@ -539,7 +539,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.classifier_fit_predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classifier_fit_predict">[docs]</a>
<span class="k">def</span> <span class="nf">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the classifier if requested (`fit_classifier=True`) and generate the necessary predictions to</span>
<span class="sd"> train the aggregation function.</span>
@ -589,7 +589,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function.</span>
@ -601,7 +601,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gives access to the classifier</span>
@ -610,7 +610,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier_</span>
<span class="nd">@classifier</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Setter for the classifier</span>
@ -620,7 +620,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.classify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classify">[docs]</a>
<span class="k">def</span> <span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Provides the label predictions for the given instances. The predictions should respect the format expected by</span>
<span class="sd"> :meth:`aggregate`, e.g., posterior probabilities for probabilistic quantifiers, or crisp predictions for</span>
@ -632,7 +632,7 @@
<span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classifier_method</span><span class="p">())(</span><span class="n">X</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Name of the method that must be used for issuing label predictions. The default one is &quot;decision_function&quot;.</span>
@ -640,7 +640,7 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="s1">&#39;decision_function&#39;</span>
<span class="k">def</span> <span class="nf">_check_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adapt_if_necessary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adapt_if_necessary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Guarantees that the underlying classifier implements the method required for issuing predictions, i.e.,</span>
<span class="sd"> the method indicated by the :meth:`_classifier_method`</span>
@ -652,7 +652,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generate class prevalence estimates for the sample&#39;s instances by aggregating the label predictions generated</span>
<span class="sd"> by the classifier.</span>
@ -667,7 +667,7 @@
<div class="viewcode-block" id="AggregativeQuantifier.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the aggregation of the classifier predictions.</span>
@ -678,7 +678,7 @@
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Class labels, in the same order in which class prevalence values are to be computed.</span>
<span class="sd"> This default implementation actually returns the class labels of the learner.</span>
@ -691,14 +691,14 @@
<div class="viewcode-block" id="AggregativeCrispQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeCrispQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">AggregativeCrispQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AggregativeCrispQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for quantification methods that base their estimations on the aggregation of crisp decisions</span>
<span class="sd"> as returned by a hard classifier. Aggregative crisp quantifiers thus extend Aggregative</span>
<span class="sd"> Quantifiers by implementing specifications about crisp predictions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Name of the method that must be used for issuing label predictions. For crisp quantifiers, the method</span>
<span class="sd"> is &#39;predict&#39;, that returns an array of shape `(n_instances,)` of label predictions.</span>
@ -711,7 +711,7 @@
<div class="viewcode-block" id="AggregativeSoftQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeSoftQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">AggregativeSoftQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AggregativeSoftQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for quantification methods that base their estimations on the aggregation of posterior</span>
<span class="sd"> probabilities as returned by a probabilistic classifier.</span>
@ -719,7 +719,7 @@
<span class="sd"> about soft predictions.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_classifier_method</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Name of the method that must be used for issuing label predictions. For probabilistic quantifiers, the method</span>
<span class="sd"> is &#39;predict_proba&#39;, that returns an array of shape `(n_instances, n_dimensions,)` with posterior</span>
@ -729,7 +729,7 @@
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="s1">&#39;predict_proba&#39;</span>
<span class="k">def</span> <span class="nf">_check_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adapt_if_necessary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adapt_if_necessary</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Guarantees that the underlying classifier implements the method indicated by the :meth:`_classifier_method`.</span>
<span class="sd"> In case it does not, the classifier is calibrated (by means of the Platt&#39;s calibration method implemented by</span>
@ -753,19 +753,19 @@
<div class="viewcode-block" id="BinaryAggregativeQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">BinaryAggregativeQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">BinaryAggregativeQuantifier</span><span class="p">(</span><span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">pos_label</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">pos_label</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">neg_label</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">neg_label</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<div class="viewcode-block" id="BinaryAggregativeQuantifier.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
</div>
@ -776,19 +776,19 @@
<span class="c1"># ------------------------------------</span>
<div class="viewcode-block" id="CC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.CC">[docs]</a>
<span class="k">class</span> <span class="nc">CC</span><span class="p">(</span><span class="n">AggregativeCrispQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">CC</span><span class="p">(</span><span class="n">AggregativeCrispQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The most basic Quantification method. One that simply classifies all instances and counts how many have been</span>
<span class="sd"> attributed to each of the classes in order to compute class prevalence estimates.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<div class="viewcode-block" id="CC.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.CC.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Nothing to do here!</span>
@ -800,7 +800,7 @@
<div class="viewcode-block" id="CC.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.CC.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes class prevalence estimates by counting the prevalence of each of the predicted labels.</span>
@ -814,7 +814,7 @@
<div class="viewcode-block" id="PCC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PCC">[docs]</a>
<span class="k">class</span> <span class="nc">PCC</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">PCC</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Probabilistic Classify &amp; Count &lt;https://ieeexplore.ieee.org/abstract/document/5694031&gt;`_,</span>
<span class="sd"> the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.</span>
@ -822,12 +822,12 @@
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="PCC.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PCC.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Nothing to do here!</span>
@ -839,7 +839,7 @@
<div class="viewcode-block" id="PCC.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PCC.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_probabilities</span><span class="p">(</span><span class="n">classif_posteriors</span><span class="p">,</span> <span class="n">binarize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
</div>
@ -847,7 +847,7 @@
<div class="viewcode-block" id="ACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.ACC">[docs]</a>
<span class="k">class</span> <span class="nc">ACC</span><span class="p">(</span><span class="n">AggregativeCrispQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ACC</span><span class="p">(</span><span class="n">AggregativeCrispQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Adjusted Classify &amp; Count &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_,</span>
<span class="sd"> the &quot;adjusted&quot; variant of :class:`CC`, that corrects the predictions of CC</span>
@ -895,7 +895,7 @@
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">fit_classifier</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
@ -918,7 +918,7 @@
<div class="viewcode-block" id="ACC.newInvariantRatioEstimation">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.ACC.newInvariantRatioEstimation">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">newInvariantRatioEstimation</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newInvariantRatioEstimation</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Constructs a quantifier that implements the Invariant Ratio Estimator of</span>
<span class="sd"> `Vaz et al. 2018 &lt;https://jmlr.org/papers/v20/18-456.html&gt;`_. This amounts</span>
@ -943,7 +943,7 @@
<span class="k">return</span> <span class="n">ACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;invariant-ratio&#39;</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;mapsimplex&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ACC</span><span class="o">.</span><span class="n">SOLVERS</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;unknown solver; valid ones are </span><span class="si">{</span><span class="n">ACC</span><span class="o">.</span><span class="n">SOLVERS</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ACC</span><span class="o">.</span><span class="n">METHODS</span><span class="p">:</span>
@ -953,7 +953,7 @@
<div class="viewcode-block" id="ACC.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.ACC.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Estimates the misclassification rates.</span>
<span class="sd"> :param classif_predictions: array-like with the predicted labels</span>
@ -968,7 +968,7 @@
<div class="viewcode-block" id="ACC.getPteCondEstim">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.ACC.getPteCondEstim">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">getPteCondEstim</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getPteCondEstim</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Estimate the matrix with entry (i,j) being the estimate of P(hat_yi|yj), that is, the probability that a</span>
<span class="sd"> document that belongs to yj ends up being classified as belonging to yi</span>
@ -991,7 +991,7 @@
<div class="viewcode-block" id="ACC.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.ACC.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="n">prevs_estim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cc</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">)</span>
<span class="n">estimate</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">solve_adjustment</span><span class="p">(</span>
<span class="n">class_conditional_rates</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Pte_cond_estim_</span><span class="p">,</span>
@ -1006,7 +1006,7 @@
<div class="viewcode-block" id="PACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PACC">[docs]</a>
<span class="k">class</span> <span class="nc">PACC</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">PACC</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Probabilistic Adjusted Classify &amp; Count &lt;https://ieeexplore.ieee.org/abstract/document/5694031&gt;`_,</span>
<span class="sd"> the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.</span>
@ -1053,7 +1053,7 @@
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@ -1069,7 +1069,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span>
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>
<span class="k">def</span> <span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ACC</span><span class="o">.</span><span class="n">SOLVERS</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;unknown solver; valid ones are </span><span class="si">{</span><span class="n">ACC</span><span class="o">.</span><span class="n">SOLVERS</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ACC</span><span class="o">.</span><span class="n">METHODS</span><span class="p">:</span>
@ -1079,7 +1079,7 @@
<div class="viewcode-block" id="PACC.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PACC.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Estimates the misclassification rates</span>
@ -1094,7 +1094,7 @@
<div class="viewcode-block" id="PACC.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PACC.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="n">prevs_estim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pcc</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_posteriors</span><span class="p">)</span>
<span class="n">estimate</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">solve_adjustment</span><span class="p">(</span>
@ -1109,7 +1109,7 @@
<div class="viewcode-block" id="PACC.getPteCondEstim">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.PACC.getPteCondEstim">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">getPteCondEstim</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">getPteCondEstim</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="c1"># estimate the matrix with entry (i,j) being the estimate of P(hat_yi|yj), that is, the probability that a</span>
<span class="c1"># document that belongs to yj ends up being classified as belonging to yi</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
@ -1126,7 +1126,7 @@
<div class="viewcode-block" id="RLLS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.RLLS">[docs]</a>
<span class="k">class</span> <span class="nc">RLLS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">RLLS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Regularized Learning for Domain Adaptation under Label Shifts</span>
<span class="sd"> &lt;https://arxiv.org/abs/1903.09734&gt;`_, used here as an aggregative</span>
@ -1166,7 +1166,7 @@
<span class="sd"> :func:`quapy.functional.normalize_prevalence`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@ -1185,7 +1185,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_w_</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">_get_cvxpy</span><span class="p">()</span>
<span class="n">_rlls_check_mode</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
@ -1202,7 +1202,7 @@
<div class="viewcode-block" id="RLLS.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.RLLS.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">if</span> <span class="n">classif_predictions</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;RLLS requires source posterior predictions and source labels&#39;</span><span class="p">)</span>
@ -1219,7 +1219,7 @@
<div class="viewcode-block" id="RLLS.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.RLLS.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="n">qz</span> <span class="o">=</span> <span class="n">_rlls_predicted_marginal</span><span class="p">(</span><span class="n">classif_posteriors</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">)</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">_rlls_compute_weights</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">C_zy_</span><span class="p">,</span>
@ -1237,7 +1237,7 @@
<div class="viewcode-block" id="EMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ">[docs]</a>
<span class="k">class</span> <span class="nc">EMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">EMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Expectation Maximization for Quantification &lt;https://ieeexplore.ieee.org/abstract/document/6789744&gt;`_ (EMQ),</span>
<span class="sd"> aka `Saerens-Latinne-Decaestecker` (SLD) algorithm.</span>
@ -1287,7 +1287,7 @@
<span class="n">ON_CALIB_ERROR_VALUES</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;raise&#39;</span><span class="p">,</span> <span class="s1">&#39;backup&#39;</span><span class="p">]</span>
<span class="n">CALIB_OPTIONS</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;nbvs&#39;</span><span class="p">,</span> <span class="s1">&#39;bcts&#39;</span><span class="p">,</span> <span class="s1">&#39;ts&#39;</span><span class="p">,</span> <span class="s1">&#39;vs&#39;</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">exact_train_prev</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">exact_train_prev</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">calib</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">on_calib_error</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">calib</span> <span class="ow">in</span> <span class="n">EMQ</span><span class="o">.</span><span class="n">CALIB_OPTIONS</span><span class="p">,</span> \
@ -1304,7 +1304,7 @@
<div class="viewcode-block" id="EMQ.EMQ_BCTS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.EMQ_BCTS">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">EMQ_BCTS</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">on_calib_error</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EMQ_BCTS</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">on_calib_error</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Constructs an instance of EMQ using the best configuration found in the `Alexandari et al. paper</span>
<span class="sd"> &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_, i.e., one that relies on Bias-Corrected Temperature</span>
@ -1336,7 +1336,7 @@
<span class="n">calib</span><span class="o">=</span><span class="s1">&#39;bcts&#39;</span><span class="p">,</span> <span class="n">on_calib_error</span><span class="o">=</span><span class="n">on_calib_error</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_init_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exact_train_prev</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">calib</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The parameter </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="si">=}</span><span class="s1"> was specified for EMQ, while the parameters &#39;</span>
@ -1352,7 +1352,7 @@
<div class="viewcode-block" id="EMQ.classify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.classify">[docs]</a>
<span class="k">def</span> <span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Provides the posterior probabilities for the given instances. The calibration function, if required,</span>
<span class="sd"> has no effect in this step, and is only involved in the aggregate method.</span>
@ -1365,13 +1365,13 @@
<div class="viewcode-block" id="EMQ.classifier_fit_predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.classifier_fit_predict">[docs]</a>
<span class="k">def</span> <span class="nf">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">classif_predictions</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train_prevalence</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">return</span> <span class="n">classif_predictions</span></div>
<span class="k">def</span> <span class="nf">_fit_calibration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_fit_calibration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">number</span><span class="p">):</span>
@ -1385,7 +1385,7 @@
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_calib_error</span> <span class="o">==</span> <span class="s1">&#39;backup&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">P</span><span class="p">:</span> <span class="n">P</span>
<span class="k">def</span> <span class="nf">_calibrate_if_requested</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">uncalib_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_calibrate_if_requested</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">uncalib_posteriors</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;calibration_function&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">calib_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">uncalib_posteriors</span><span class="p">)</span>
@ -1402,7 +1402,7 @@
<div class="viewcode-block" id="EMQ.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function of EMQ. This comes down to recalibrating the posterior probabilities</span>
<span class="sd"> ir requested.</span>
@ -1441,7 +1441,7 @@
<div class="viewcode-block" id="EMQ.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="n">classif_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calibrate_if_requested</span><span class="p">(</span><span class="n">classif_posteriors</span><span class="p">)</span>
<span class="n">priors</span><span class="p">,</span> <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">EM</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_prevalence</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">)</span>
<span class="k">return</span> <span class="n">priors</span></div>
@ -1449,7 +1449,7 @@
<div class="viewcode-block" id="EMQ.predict_proba">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.predict_proba">[docs]</a>
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the posterior probabilities updated by the EM algorithm.</span>
@ -1466,7 +1466,7 @@
<div class="viewcode-block" id="EMQ.EM">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.EMQ.EM">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">EM</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tr_prev</span><span class="p">,</span> <span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EM</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tr_prev</span><span class="p">,</span> <span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">EPSILON</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the `Expectation Maximization` routine.</span>
@ -1513,7 +1513,7 @@
<div class="viewcode-block" id="HDy">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.HDy">[docs]</a>
<span class="k">class</span> <span class="nc">HDy</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">HDy</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Hellinger Distance y &lt;https://www.sciencedirect.com/science/article/pii/S0020025512004069&gt;`_ (HDy).</span>
<span class="sd"> HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of</span>
@ -1540,12 +1540,12 @@
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="HDy.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.HDy.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function of HDy.</span>
@ -1560,7 +1560,7 @@
<span class="c1"># pre-compute the histogram for positive and negative examples</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span> <span class="c1"># [10, 20, 30, ..., 100, 110]</span>
<span class="k">def</span> <span class="nf">hist</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">bins</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">hist</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">bins</span><span class="p">):</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">h</span> <span class="o">/</span> <span class="n">h</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@ -1570,7 +1570,7 @@
<div class="viewcode-block" id="HDy.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.HDy.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="c1"># &quot;In this work, the number of bins b used in HDx and HDy was chosen from 10 to 110 in steps of 10,</span>
<span class="c1"># and the final estimated a priori probability was taken as the median of these 11 estimates.&quot;</span>
<span class="c1"># (González-Castro, et al., 2013).</span>
@ -1587,7 +1587,7 @@
<span class="c1"># the authors proposed to search for the prevalence yielding the best matching as a linear search</span>
<span class="c1"># at small steps (modern implementations resort to an optimization procedure,</span>
<span class="c1"># see class DistributionMatching)</span>
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">class1_prev</span> <span class="o">=</span> <span class="n">prev</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">Px_train</span> <span class="o">=</span> <span class="n">class1_prev</span> <span class="o">*</span> <span class="n">Pxy1_density</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">class1_prev</span><span class="p">)</span> <span class="o">*</span> <span class="n">Pxy0_density</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">HellingerDistance</span><span class="p">(</span><span class="n">Px_train</span><span class="p">,</span> <span class="n">Px_test</span><span class="p">)</span>
@ -1602,7 +1602,7 @@
<div class="viewcode-block" id="DyS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DyS">[docs]</a>
<span class="k">class</span> <span class="nc">DyS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DyS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `DyS framework &lt;https://ojs.aaai.org/index.php/AAAI/article/view/4376&gt;`_ (DyS).</span>
<span class="sd"> DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that</span>
@ -1631,7 +1631,7 @@
<span class="sd"> :param n_jobs: number of parallel workers.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tol</span> <span class="o">=</span> <span class="n">tol</span>
@ -1639,7 +1639,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">n_bins</span> <span class="o">=</span> <span class="n">n_bins</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_ternary_search</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">tol</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_ternary_search</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">tol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Find maximum of unimodal function f() within [left, right]</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -1657,7 +1657,7 @@
<div class="viewcode-block" id="DyS.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DyS.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function of DyS.</span>
@ -1675,13 +1675,13 @@
<div class="viewcode-block" id="DyS.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DyS.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="n">Px</span> <span class="o">=</span> <span class="n">classif_posteriors</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">]</span> <span class="c1"># takes only the P(y=+1|x)</span>
<span class="n">Px_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">Px</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_bins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">distribution_distance</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">distribution_distance</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">Px_train</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">Pxy1_density</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">prev</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">Pxy0_density</span>
<span class="k">return</span> <span class="n">divergence</span><span class="p">(</span><span class="n">Px_train</span><span class="p">,</span> <span class="n">Px_test</span><span class="p">)</span>
@ -1693,7 +1693,7 @@
<div class="viewcode-block" id="SMM">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.SMM">[docs]</a>
<span class="k">class</span> <span class="nc">SMM</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">SMM</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `SMM method &lt;https://ieeexplore.ieee.org/document/9260028&gt;`_ (SMM).</span>
<span class="sd"> SMM is a simplification of matching distribution methods where the representation of the examples</span>
@ -1712,12 +1712,12 @@
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="SMM.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.SMM.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function of SMM.</span>
@ -1735,7 +1735,7 @@
<div class="viewcode-block" id="SMM.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.SMM.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_posteriors</span><span class="p">):</span>
<span class="n">Px</span> <span class="o">=</span> <span class="n">classif_posteriors</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">]</span> <span class="c1"># takes only the P(y=+1|x)</span>
<span class="n">Px_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">Px</span><span class="p">)</span>
@ -1747,7 +1747,7 @@
<div class="viewcode-block" id="DMy">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DMy">[docs]</a>
<span class="k">class</span> <span class="nc">DMy</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DMy</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of posterior</span>
<span class="sd"> probabilities. This implementation takes the number of bins, the divergence, and the possibility to work on CDF</span>
@ -1780,7 +1780,7 @@
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span>
@ -1792,7 +1792,7 @@
<div class="viewcode-block" id="DMy.HDy">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DMy.HDy">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">HDy</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">HDy</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Historical HDy preset expressed as a configuration of :class:`DMy`.</span>
@ -1821,7 +1821,7 @@
<span class="k">return</span> <span class="n">AggregativeMedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">base</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
<span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">post_dims</span> <span class="o">=</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">post_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
@ -1839,7 +1839,7 @@
<div class="viewcode-block" id="DMy.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DMy.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the aggregation function of a distribution matching method. This comes down to generating the</span>
<span class="sd"> validation distributions out of the training data.</span>
@ -1867,7 +1867,7 @@
<div class="viewcode-block" id="DMy.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.DMy.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
<span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
@ -1882,7 +1882,7 @@
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
<span class="n">n_classes</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">ch</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">ch</span><span class="p">])</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_channels</span><span class="p">)]</span>
@ -1895,7 +1895,7 @@
<div class="viewcode-block" id="newELM">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newELM">[docs]</a>
<span class="k">def</span> <span class="nf">newELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Explicit Loss Minimization (ELM) quantifiers.</span>
<span class="sd"> Quantifiers based on ELM represent a family of methods based on structured output learning;</span>
@ -1925,7 +1925,7 @@
<div class="viewcode-block" id="newSVMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newSVMQ">[docs]</a>
<span class="k">def</span> <span class="nf">newSVMQ</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newSVMQ</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> SVM(Q) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the `Q` loss combining a</span>
<span class="sd"> classification-oriented loss and a quantification-oriented loss, as proposed by</span>
@ -1954,7 +1954,7 @@
<div class="viewcode-block" id="newSVMKLD">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newSVMKLD">[docs]</a>
<span class="k">def</span> <span class="nf">newSVMKLD</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newSVMKLD</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence</span>
<span class="sd"> as proposed by `Esuli et al. 2015 &lt;https://dl.acm.org/doi/abs/10.1145/2700406&gt;`_.</span>
@ -1982,7 +1982,7 @@
<div class="viewcode-block" id="newSVMNKLD">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newSVMNKLD">[docs]</a>
<span class="k">def</span> <span class="nf">newSVMNKLD</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newSVMNKLD</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> SVM(NKLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence</span>
<span class="sd"> normalized via the logistic function, as proposed by</span>
@ -2011,7 +2011,7 @@
<div class="viewcode-block" id="newSVMAE">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newSVMAE">[docs]</a>
<span class="k">def</span> <span class="nf">newSVMAE</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newSVMAE</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Absolute Error as first used by</span>
<span class="sd"> `Moreo and Sebastiani, 2021 &lt;https://arxiv.org/abs/2011.02552&gt;`_.</span>
@ -2039,7 +2039,7 @@
<div class="viewcode-block" id="newSVMRAE">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.newSVMRAE">[docs]</a>
<span class="k">def</span> <span class="nf">newSVMRAE</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newSVMRAE</span><span class="p">(</span><span class="n">svmperf_base</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Relative Absolute Error as first</span>
<span class="sd"> used by `Moreo and Sebastiani, 2021 &lt;https://arxiv.org/abs/2011.02552&gt;`_.</span>
@ -2067,7 +2067,7 @@
<div class="viewcode-block" id="OneVsAllAggregative">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.OneVsAllAggregative">[docs]</a>
<span class="k">class</span> <span class="nc">OneVsAllAggregative</span><span class="p">(</span><span class="n">OneVsAllGeneric</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAllAggregative</span><span class="p">(</span><span class="n">OneVsAllGeneric</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets.</span>
<span class="sd"> The method maintains one binary quantifier for each class, and then l1-normalizes the outputs so that the</span>
@ -2083,7 +2083,7 @@
<span class="sd"> is removed and no longer available at predict time.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">parallel_backend</span><span class="o">=</span><span class="s1">&#39;multiprocessing&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">parallel_backend</span><span class="o">=</span><span class="s1">&#39;multiprocessing&#39;</span><span class="p">):</span>
<span class="k">if</span> <span class="n">binary_quantifier</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">()</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
@ -2096,7 +2096,7 @@
<div class="viewcode-block" id="OneVsAllAggregative.classify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.OneVsAllAggregative.classify">[docs]</a>
<span class="k">def</span> <span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">classify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> If the base quantifier is not probabilistic, returns a matrix of shape `(n,m,)` with `n` the number of</span>
<span class="sd"> instances and `m` the number of classes. The entry `(i,j)` is a binary value indicating whether instance</span>
@ -2120,26 +2120,26 @@
<div class="viewcode-block" id="OneVsAllAggregative.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.OneVsAllAggregative.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_aggregate</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
<div class="viewcode-block" id="OneVsAllAggregative.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.OneVsAllAggregative.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_aggregate_fit</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_delayed_binary_classification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_classification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delayed_binary_aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">):</span>
<span class="c1"># the estimation for the positive class prevalence</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="n">c</span><span class="p">])[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_delayed_binary_aggregate_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_aggregate_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="c1"># trains the aggregation function of the cth quantifier</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="n">c</span><span class="p">],</span> <span class="n">labels</span> <span class="o">==</span> <span class="n">c</span><span class="p">)</span></div>
@ -2147,7 +2147,7 @@
<div class="viewcode-block" id="AggregativeMedianEstimator">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator">[docs]</a>
<span class="k">class</span> <span class="nc">AggregativeMedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AggregativeMedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
@ -2160,7 +2160,7 @@
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
@ -2168,17 +2168,17 @@
<div class="viewcode-block" id="AggregativeMedianEstimator.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
<div class="viewcode-block" id="AggregativeMedianEstimator.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
@ -2186,7 +2186,7 @@
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">_delayed_fit_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">cls_params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
@ -2194,7 +2194,7 @@
<span class="n">predictions</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delayed_fit_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">q_params</span><span class="p">)</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
@ -2204,8 +2204,8 @@
<div class="viewcode-block" id="AggregativeMedianEstimator.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
@ -2243,13 +2243,13 @@
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="AggregativeMedianEstimator.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.aggregative.AggregativeMedianEstimator.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
@ -2265,7 +2265,7 @@
<span class="c1"># imports</span>
<span class="c1"># ---------------------------------------------------------------</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_threshold_optim</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_threshold_optim</span>
<span class="n">T50</span> <span class="o">=</span> <span class="n">_threshold_optim</span><span class="o">.</span><span class="n">T50</span>
<span class="n">MAX</span> <span class="o">=</span> <span class="n">_threshold_optim</span><span class="o">.</span><span class="n">MAX</span>
@ -2273,7 +2273,7 @@
<span class="n">MS</span> <span class="o">=</span> <span class="n">_threshold_optim</span><span class="o">.</span><span class="n">MS</span>
<span class="n">MS2</span> <span class="o">=</span> <span class="n">_threshold_optim</span><span class="o">.</span><span class="n">MS2</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_kdey</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_kdey</span>
<span class="n">KDEyML</span> <span class="o">=</span> <span class="n">_kdey</span><span class="o">.</span><span class="n">KDEyML</span>
<span class="n">KDEyHD</span> <span class="o">=</span> <span class="n">_kdey</span><span class="o">.</span><span class="n">KDEyHD</span>

48
docs/build/html/_modules/quapy/method/base.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
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@ -370,23 +370,23 @@
<article class="bd-article">
<h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Base Quantifier abstract class</span>
<span class="c1"># ------------------------------------</span>
<div class="viewcode-block" id="BaseQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
<span class="sd"> a pair X, y, the method :meth:`predict`, and the :meth:`set_params` and</span>
@ -396,7 +396,7 @@
<div class="viewcode-block" id="BaseQuantifier.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates a quantifier.</span>
@ -410,7 +410,7 @@
<div class="viewcode-block" id="BaseQuantifier.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.predict">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generate class prevalence estimates for the sample&#39;s instances</span>
@ -422,7 +422,7 @@
<div class="viewcode-block" id="BaseQuantifier.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Alias to :meth:`predict`, for old compatibility</span>
@ -436,13 +436,13 @@
<div class="viewcode-block" id="BinaryQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a>
<span class="k">class</span> <span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes</span>
<span class="sd"> (typically, to be interpreted as one class and its complement).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="k">assert</span> <span class="n">n_classes</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> works only on problems of binary classification. &#39;</span> \
<span class="sa">f</span><span class="s1">&#39;Use the class OneVsAll to enable </span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> work on single-label data.&#39;</span></div>
@ -451,14 +451,14 @@
<div class="viewcode-block" id="OneVsAll">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a>
<span class="k">class</span> <span class="nc">OneVsAll</span><span class="p">:</span>
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAll</span><span class="p">:</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="newOneVsAll">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a>
<span class="k">def</span> <span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
@ -470,13 +470,13 @@
<div class="viewcode-block" id="OneVsAllGeneric">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a>
<span class="k">class</span> <span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span>
<span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevalence values sum up to 1.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
@ -487,7 +487,7 @@
<div class="viewcode-block" id="OneVsAllGeneric.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span><span class="o">!=</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> expect non-binary data&#39;</span>
@ -496,7 +496,7 @@
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;threading&#39;</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span>
@ -505,7 +505,7 @@
<div class="viewcode-block" id="OneVsAllGeneric.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
@ -514,10 +514,10 @@
<span class="c1"># def classes_(self):</span>
<span class="c1"># return sorted(self.dict_binary_quantifiers.keys())</span>
<span class="k">def</span> <span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">bindata</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span></div>

130
docs/build/html/_modules/quapy/method/meta.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
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@ -370,25 +370,25 @@
<article class="bd-article">
<h1>Source code for quapy.method.meta</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">List</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">List</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchQ</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_neural</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_neural</span>
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
<span class="n">_neural</span> <span class="o">=</span> <span class="kc">None</span>
@ -401,7 +401,7 @@
<div class="viewcode-block" id="MedianEstimator">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a>
<span class="k">class</span> <span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
@ -413,7 +413,7 @@
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
@ -421,17 +421,17 @@
<div class="viewcode-block" id="MedianEstimator.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
<div class="viewcode-block" id="MedianEstimator.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
@ -441,7 +441,7 @@
<div class="viewcode-block" id="MedianEstimator.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
@ -455,13 +455,13 @@
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
@ -477,7 +477,7 @@
<div class="viewcode-block" id="Ensemble">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a>
<span class="k">class</span> <span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="n">VALID_POLICIES</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ave&#39;</span><span class="p">,</span> <span class="s1">&#39;ptr&#39;</span><span class="p">,</span> <span class="s1">&#39;ds&#39;</span><span class="p">}</span> <span class="o">|</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
@ -517,7 +517,7 @@
<span class="sd"> :param verbose: set to True (default is False) to get some information in standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">red_size</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
@ -542,13 +542,13 @@
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="o">=</span> <span class="n">max_sample_size</span>
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;[Ensemble] &#39;</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span>
<div class="viewcode-block" id="Ensemble.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@ -589,7 +589,7 @@
<div class="viewcode-block" id="Ensemble.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
<span class="p">)</span>
@ -605,7 +605,7 @@
<div class="viewcode-block" id="Ensemble.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
<span class="sd"> with the abstract class).</span>
@ -624,7 +624,7 @@
<div class="viewcode-block" id="Ensemble.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
<span class="sd"> with the abstract class).</span>
@ -639,13 +639,13 @@
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).</span>
<span class="sd"> For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of</span>
<span class="sd"> the samples used for training the rest of the models in the ensemble.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_on_samples</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.evaluation</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluate_on_samples</span>
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_name</span><span class="p">)</span>
<span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
@ -655,7 +655,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">_select_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Selects the predictions made by models that have been trained on samples with a prevalence that is most similar</span>
<span class="sd"> to a first approximation of the test prevalence as made by all models in the ensemble.</span>
@ -666,7 +666,7 @@
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ptr_differences</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> In the original article, there are some aspects regarding this method that are not mentioned. The paper says</span>
<span class="sd"> that the distribution of posterior probabilities from training and test examples is compared by means of the</span>
@ -697,7 +697,7 @@
<span class="k">return</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">posteriors_generator</span>
<span class="k">def</span> <span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
<span class="n">test_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="n">test_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">test_posteriors</span><span class="p">)</span>
<span class="n">tr_distributions</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
@ -706,7 +706,7 @@
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates that the quantifier is not aggregative.</span>
@ -715,7 +715,7 @@
<span class="k">return</span> <span class="kc">False</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates that the quantifier is not probabilistic.</span>
@ -727,7 +727,7 @@
<div class="viewcode-block" id="get_probability_distribution">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a>
<span class="k">def</span> <span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets a histogram out of the posterior probabilities (only for the binary case).</span>
@ -742,11 +742,11 @@
<span class="k">def</span> <span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[:</span><span class="n">k</span><span class="p">]]</span>
<span class="k">def</span> <span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">keep_samples</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;fit-start for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">, sample_size=</span><span class="si">{</span><span class="n">sample_size</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
@ -774,12 +774,12 @@
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tr_prevalence</span><span class="p">,</span> <span class="n">tr_distribution</span><span class="p">,</span> <span class="n">sample</span> <span class="k">if</span> <span class="n">keep_samples</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="n">quantifier</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions</span>
<span class="sd"> are &gt;= min_class_prev (for min_val&gt;0, this makes the sampling not truly uniform)</span>
@ -804,7 +804,7 @@
<span class="sa">f</span><span class="s1">&#39;&gt;= </span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> is unlikely (it failed after </span><span class="si">{</span><span class="n">max_trials</span><span class="si">}</span><span class="s1"> trials)&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</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_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</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_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">optim</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
@ -823,7 +823,7 @@
<span class="k">return</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span> <span class="ow">or</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
@ -837,7 +837,7 @@
<div class="viewcode-block" id="ensembleFactory">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a>
<span class="k">def</span> <span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model</span>
@ -891,7 +891,7 @@
<div class="viewcode-block" id="ECC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a>
<span class="k">def</span> <span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -917,7 +917,7 @@
<div class="viewcode-block" id="EACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a>
<span class="k">def</span> <span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -943,7 +943,7 @@
<div class="viewcode-block" id="EPACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a>
<span class="k">def</span> <span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.</span>
@ -968,7 +968,7 @@
<div class="viewcode-block" id="EHDy">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a>
<span class="k">def</span> <span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -994,7 +994,7 @@
<div class="viewcode-block" id="EEMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a>
<span class="k">def</span> <span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.</span>
@ -1019,7 +1019,7 @@
<div class="viewcode-block" id="merge">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.merge">[docs]</a>
<span class="k">def</span> <span class="nf">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">)</span>
<span class="k">if</span> <span class="n">merge_fun</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@ -1034,11 +1034,11 @@
<div class="viewcode-block" id="SCMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ">[docs]</a>
<span class="k">class</span> <span class="nc">SCMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">SCMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="n">MERGE_FUNCTIONS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">[</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">quantifiers</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">merge_fun</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">merge_fun</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="si">}</span><span class="s1">&#39;</span>
@ -1047,7 +1047,7 @@
<div class="viewcode-block" id="SCMQ.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregation_fit">[docs]</a>
<span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
@ -1056,7 +1056,7 @@
<div class="viewcode-block" id="SCMQ.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregate">[docs]</a>
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">quantifier_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">quantifier_i</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">)</span>
@ -1068,8 +1068,8 @@
<div class="viewcode-block" id="MCSQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ">[docs]</a>
<span class="k">class</span> <span class="nc">MCSQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifier</span><span class="p">:</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MCSQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifier</span><span class="p">:</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span> <span class="o">=</span> <span class="p">[]</span>
@ -1080,7 +1080,7 @@
<div class="viewcode-block" id="MCSQ.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
@ -1088,7 +1088,7 @@
<div class="viewcode-block" id="MCSQ.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.quantify">[docs]</a>
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
@ -1100,8 +1100,8 @@
<div class="viewcode-block" id="MCMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ">[docs]</a>
<span class="k">class</span> <span class="nc">MCMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MCMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">classifier</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
@ -1109,7 +1109,7 @@
<div class="viewcode-block" id="MCMQ.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
@ -1117,7 +1117,7 @@
<div class="viewcode-block" id="MCMQ.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.quantify">[docs]</a>
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>

86
docs/build/html/_modules/quapy/method/non_aggregative.html vendored
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@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
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@ -370,26 +370,26 @@
<article class="bd-article">
<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Counter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">resample</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">normalize</span>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Counter</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">normalize</span>
<span class="kn">from</span> <span class="nn">quapy.method.confidence</span> <span class="kn">import</span> <span class="n">WithConfidenceABC</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span>
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">get_divergence</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.method._helper</span> <span class="kn">import</span> <span class="n">_labels_to_indices</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">lsq_linear</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.confidence</span><span class="w"> </span><span class="kn">import</span> <span class="n">WithConfidenceABC</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_divergence</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.optimize</span><span class="w"> </span><span class="kn">import</span> <span class="n">lsq_linear</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse</span>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a>
<span class="k">class</span> <span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior</span>
<span class="sd"> probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).</span>
@ -398,12 +398,12 @@
<span class="sd"> any quantification method should beat.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the training prevalence and stores it.</span>
@ -418,7 +418,7 @@
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
@ -432,7 +432,7 @@
<div class="viewcode-block" id="DMx">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a>
<span class="k">class</span> <span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.</span>
<span class="sd"> This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</span>
@ -448,7 +448,7 @@
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span>
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="n">cdf</span>
@ -458,7 +458,7 @@
<div class="viewcode-block" id="DMx.HDx">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.HDx">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Hellinger Distance x &lt;https://www.sciencedirect.com/science/article/pii/S0020025512004069&gt;`_ (HDx).</span>
<span class="sd"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
@ -474,7 +474,7 @@
<span class="sd"> :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span>
<span class="sd"> González-Castro, Alaiz-Rodríguez, Alegre (2013)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.meta</span><span class="w"> </span><span class="kn">import</span> <span class="n">MedianEstimator</span>
<span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;linear_search&#39;</span><span class="p">)</span>
<span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span>
@ -482,7 +482,7 @@
<span class="k">return</span> <span class="n">hdx</span></div>
<span class="k">def</span> <span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span>
<span class="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
@ -499,7 +499,7 @@
<div class="viewcode-block" id="DMx.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates the validation distributions out of the training data (covariates).</span>
<span class="sd"> The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span>
@ -526,7 +526,7 @@
<div class="viewcode-block" id="DMx.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
<span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
@ -542,7 +542,7 @@
<span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
<span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span>
@ -555,7 +555,7 @@
<div class="viewcode-block" id="ReadMe">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe">[docs]</a>
<span class="k">class</span> <span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> ReadMe is a non-aggregative quantification system proposed by</span>
<span class="sd"> `Daniel Hopkins and Gary King, 2007. A method of automated nonparametric content analysis for</span>
@ -589,6 +589,8 @@
<span class="sd"> :param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)</span>
<span class="sd"> :param region: str in &#39;intervals&#39;, &#39;ellipse&#39;, &#39;ellipse-clr&#39;; indicates the preferred method for</span>
<span class="sd"> defining the confidence region (see :class:`WithConfidenceABC`)</span>
<span class="sd"> :param bonferroni: bool (default False), whether to apply Bonferroni correction when</span>
<span class="sd"> `region=&#39;intervals&#39;`. This parameter has no effect for ellipse-based regions.</span>
<span class="sd"> :param random_state: int or None, allows replicability (default None)</span>
<span class="sd"> :param verbose: bool, whether to display information during the process (default False)</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -596,13 +598,14 @@
<span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span> <span class="o">=</span> <span class="mi">25</span>
<span class="n">PROBABILISTIC_MODELS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;naive&quot;</span><span class="p">,</span> <span class="s2">&quot;full&quot;</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">prob_model</span><span class="o">=</span><span class="s2">&quot;full&quot;</span><span class="p">,</span>
<span class="n">bootstrap_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">bagging_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">bagging_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
<span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
<span class="n">region</span><span class="o">=</span><span class="s1">&#39;intervals&#39;</span><span class="p">,</span>
<span class="n">bonferroni</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">prob_model</span> <span class="ow">in</span> <span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="p">,</span> \
@ -613,12 +616,13 @@
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span> <span class="o">=</span> <span class="n">bagging_range</span>
<span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span> <span class="o">=</span> <span class="n">confidence_level</span>
<span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span> <span class="o">=</span> <span class="n">bonferroni</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="ReadMe.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
@ -638,8 +642,10 @@
<div class="viewcode-block" id="ReadMe.predict_conf">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict_conf">[docs]</a>
<span class="k">def</span> <span class="nf">predict_conf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_conf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">confidence_level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">confidence_level</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">boots_prevalences</span> <span class="o">=</span> <span class="p">[]</span>
@ -657,7 +663,7 @@
<span class="n">boots_prevalences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bagging_estimates</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">)</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">,</span> <span class="n">bonferroni</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span><span class="p">)</span>
<span class="n">prev_estim</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">point_estimate</span><span class="p">()</span>
<span class="k">return</span> <span class="n">prev_estim</span><span class="p">,</span> <span class="n">conf</span></div>
@ -665,12 +671,12 @@
<div class="viewcode-block" id="ReadMe.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prev_estim</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_conf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prev_estim</span></div>
<span class="k">def</span> <span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">Xte</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">Xte</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Single ReadMe estimate.&quot;&quot;&quot;</span>
<span class="n">PX_given_Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xtr</span><span class="p">[</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)])</span>
<span class="n">PX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xte</span><span class="p">)</span>
@ -679,17 +685,17 @@
<span class="n">pY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pY</span> <span class="o">/</span> <span class="n">pY</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_check_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;the &quot;full&quot; model requires estimating empirical distributions; due to the high computational cost,</span>
<span class="sd"> this function is only made available for binary matrices&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_binary_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;the empirical distribution can only be computed efficiently on binary matrices&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_is_binary_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_is_binary_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">data</span> <span class="k">if</span> <span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">else</span> <span class="n">X</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="n">data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_compute_P</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_P</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;naive&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multinomial_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span><span class="p">:</span>
@ -697,7 +703,7 @@
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span><span class="si">}</span><span class="s1">; valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_empirical_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_empirical_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the empirical distribution can only be computed efficiently for dimensions &#39;</span>
@ -715,14 +721,14 @@
<span class="k">return</span> <span class="n">probs</span>
<span class="k">def</span> <span class="nf">_multinomial_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_multinomial_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">PX</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">PX</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">PX</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">PX</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ncols</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>

96
docs/build/html/_modules/quapy/model_selection.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../_static/custom.css?v=9f2a2228" />
@ -370,29 +370,29 @@
<article class="bd-article">
<h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">signal</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</span>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">wraps</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">clone</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">clone</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">evaluation</span>
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span>
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">timeout</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluation</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">timeout</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
<div class="viewcode-block" id="Status">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a>
<span class="k">class</span> <span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span>
@ -402,28 +402,28 @@
<div class="viewcode-block" id="ConfigStatus">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a>
<span class="k">class</span> <span class="nc">ConfigStatus</span><span class="p">:</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ConfigStatus</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="n">status</span>
<span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;:params:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s1"> :status:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s1"> &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<div class="viewcode-block" id="ConfigStatus.success">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a>
<span class="k">def</span> <span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
<div class="viewcode-block" id="ConfigStatus.failed">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a>
<span class="k">def</span> <span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
</div>
@ -431,7 +431,7 @@
<div class="viewcode-block" id="GridSearchQ">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a>
<span class="k">class</span> <span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Grid Search optimization targeting a quantification-oriented metric.</span>
<span class="sd"> Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span>
@ -454,7 +454,7 @@
<span class="sd"> :param verbose: set to True to get information through the stdout</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
@ -476,11 +476,11 @@
<span class="bp">self</span><span class="o">.</span><span class="n">__check_error_measure</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">&#39;unknown protocol&#39;</span>
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__check_error_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__check_error_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
@ -491,10 +491,10 @@
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">&#39;</span>
<span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">predictions</span>
@ -503,12 +503,12 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[classifier fit] hyperparams=</span><span class="si">{</span><span class="n">cls_params</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
<span class="k">def</span> <span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cls_params</span><span class="p">,</span> <span class="o">**</span><span class="n">q_params</span><span class="p">}</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">predictions</span>
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
@ -519,10 +519,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
@ -532,7 +532,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
<span class="k">def</span> <span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Decides whether to break down the fit phase in two (classifier-fit followed by aggregation-fit).</span>
<span class="sd"> In order to do so, some conditions should be met: a) the quantifier is of type aggregative,</span>
@ -547,7 +547,7 @@
<span class="k">return</span> <span class="kc">False</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span>
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
@ -584,7 +584,7 @@
<span class="k">return</span> <span class="n">aggr_outs</span>
<span class="k">def</span> <span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
@ -596,7 +596,7 @@
<span class="p">)</span>
<span class="k">return</span> <span class="n">scores</span>
<span class="k">def</span> <span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;hyperparams=[</span><span class="si">{</span><span class="n">params</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1"> got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
@ -604,7 +604,7 @@
<div class="viewcode-block" id="GridSearchQ.fit">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span>
<span class="sd"> the error metric.</span>
@ -675,7 +675,7 @@
<div class="viewcode-block" id="GridSearchQ.predict">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
<span class="sd"> :param X: sample contanining the instances</span>
@ -688,7 +688,7 @@
<div class="viewcode-block" id="GridSearchQ.set_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Sets the hyper-parameters to explore.</span>
<span class="sd"> :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span>
@ -698,7 +698,7 @@
<div class="viewcode-block" id="GridSearchQ.get_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns the dictionary of hyper-parameters to explore (`param_grid`)</span>
<span class="sd"> :param deep: Unused</span>
@ -709,7 +709,7 @@
<div class="viewcode-block" id="GridSearchQ.best_model">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a>
<span class="k">def</span> <span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination</span>
<span class="sd"> of hyper-parameters that minimized the error function.</span>
@ -721,7 +721,7 @@
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;best_model called before fit&#39;</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Endorses one job with two returned values: the status, and the time of execution</span>
@ -734,7 +734,7 @@
<span class="n">output</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">exception</span>
<span class="k">else</span><span class="p">:</span>
@ -762,7 +762,7 @@
<div class="viewcode-block" id="cross_val_predict">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a>
<span class="k">def</span> <span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Akin to `scikit-learn&#39;s cross_val_predict &lt;https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html&gt;`_</span>
<span class="sd"> but for quantification.</span>
@ -788,7 +788,7 @@
<div class="viewcode-block" id="expand_grid">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a>
<span class="k">def</span> <span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Expands a param_grid dictionary as a list of configurations.</span>
<span class="sd"> Example:</span>
@ -810,7 +810,7 @@
<div class="viewcode-block" id="group_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a>
<span class="k">def</span> <span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific</span>
<span class="sd"> hyper-parameters, and another for que quantifier-specific hyper-parameters</span>

68
docs/build/html/_modules/quapy/plot.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../../_static/custom.css?v=9f2a2228" />
@ -370,19 +370,19 @@
<article class="bd-article">
<h1>Source code for quapy.plot</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">math</span>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">math</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">LinearSegmentedColormap</span><span class="p">,</span> <span class="n">ListedColormap</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">import</span> <span class="n">get_cmap</span>
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">ScalarFormatter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind_from_stats</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">cm</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.colors</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mcolors</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.colors</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearSegmentedColormap</span><span class="p">,</span> <span class="n">ListedColormap</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_cmap</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.ticker</span><span class="w"> </span><span class="kn">import</span> <span class="n">ScalarFormatter</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">ttest_ind_from_stats</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.dpi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span>
@ -391,7 +391,7 @@
<div class="viewcode-block" id="binary_diagonal">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a>
<span class="k">def</span> <span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train_prev</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span>
@ -471,7 +471,7 @@
<div class="viewcode-block" id="binary_bias_global">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a>
<span class="k">def</span> <span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)</span>
<span class="sd"> for each quantification method with respect to a given positive class.</span>
@ -517,7 +517,7 @@
<div class="viewcode-block" id="binary_bias_bins">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a>
<span class="k">def</span> <span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
<span class="n">vertical_xticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span>
@ -543,7 +543,7 @@
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pylab</span><span class="w"> </span><span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
@ -619,7 +619,7 @@
<div class="viewcode-block" id="error_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a>
<span class="k">def</span> <span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">error_name</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">show_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">show_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@ -755,7 +755,7 @@
<div class="viewcode-block" id="brokenbar_supremacy_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a>
<span class="k">def</span> <span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">binning</span><span class="o">=</span><span class="s1">&#39;isomerous&#39;</span><span class="p">,</span>
<span class="n">x_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">y_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">ttest_alpha</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">tail_density_threshold</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span>
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -951,7 +951,7 @@
<div class="viewcode-block" id="plot_simplex">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.plot_simplex">[docs]</a>
<span class="k">def</span> <span class="nf">plot_simplex</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_simplex</span><span class="p">(</span>
<span class="n">point_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">region_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">density_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -1069,7 +1069,7 @@
<span class="k">def</span> <span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
<span class="n">ndims</span> <span class="o">=</span> <span class="n">true_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;true&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">)),</span> <span class="s1">&#39;estim&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">))})</span>
<span class="n">method_order</span><span class="o">=</span><span class="p">[]</span>
@ -1083,14 +1083,14 @@
<span class="k">return</span> <span class="n">method_order</span><span class="p">,</span> <span class="n">true_prevs_</span><span class="p">,</span> <span class="n">estim_prevs_</span>
<span class="k">def</span> <span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
<span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</span>
<span class="k">if</span> <span class="n">NUM_COLORS</span><span class="o">&gt;</span><span class="mi">10</span><span class="p">:</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
<span class="c1"># if savepath is specified, then saves the plot in that path; otherwise the plot is shown</span>
<span class="k">if</span> <span class="n">savepath</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">create_parent_dir</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
@ -1100,7 +1100,7 @@
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))})</span>
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@ -1121,7 +1121,7 @@
<span class="k">return</span> <span class="n">data</span>
<span class="k">def</span> <span class="nf">_simplex_to_cartesian</span><span class="p">(</span><span class="n">prevalences</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_simplex_to_cartesian</span><span class="p">(</span><span class="n">prevalences</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
@ -1131,14 +1131,14 @@
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
<span class="k">def</span> <span class="nf">_barycentric_from_xy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_barycentric_from_xy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">p3</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">p2</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">p3</span>
<span class="n">p1</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">p2</span> <span class="o">-</span> <span class="n">p3</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">):</span>
<span class="n">simplex_ymax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">simplex_ymax</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span>
@ -1148,7 +1148,7 @@
<span class="k">return</span> <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">mask</span>
<span class="k">def</span> <span class="nf">_evaluate_simplex_function</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_evaluate_simplex_function</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">function</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
@ -1161,14 +1161,14 @@
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">function</span><span class="p">(</span><span class="n">point</span><span class="p">)</span> <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">points</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_region_colormap</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.35</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_region_colormap</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.35</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ListedColormap</span><span class="p">([</span>
<span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">),</span>
<span class="p">(</span><span class="o">*</span><span class="n">mcolors</span><span class="o">.</span><span class="n">to_rgb</span><span class="p">(</span><span class="n">color</span><span class="p">),</span> <span class="n">alpha</span><span class="p">),</span>
<span class="p">])</span>
<span class="k">def</span> <span class="nf">_plot_simplex_points</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">point_layers</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_points</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">point_layers</span><span class="p">):</span>
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">point_layers</span><span class="p">:</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">layer</span><span class="p">[</span><span class="s1">&#39;points&#39;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="n">style</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;s&#39;</span><span class="p">:</span> <span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="mf">0.8</span><span class="p">}</span>
@ -1176,7 +1176,7 @@
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">_simplex_to_cartesian</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">),</span> <span class="o">**</span><span class="n">style</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_plot_simplex_regions</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">region_layers</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_regions</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">region_layers</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">simplex_mask</span> <span class="o">=</span> <span class="n">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">)</span>
<span class="n">valid_points</span> <span class="o">=</span> <span class="n">pts_bary</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span>
@ -1195,7 +1195,7 @@
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;blue&#39;</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">layer</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">_plot_simplex_density</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">density_function</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_density</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">density_function</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">simplex_mask</span> <span class="o">=</span> <span class="n">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">)</span>
<span class="n">valid_points</span> <span class="o">=</span> <span class="n">pts_bary</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span>
<span class="n">density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">simplex_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
@ -1211,7 +1211,7 @@
<div class="viewcode-block" id="calibration_plot">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.calibration_plot">[docs]</a>
<span class="k">def</span> <span class="nf">calibration_plot</span><span class="p">(</span><span class="n">prob_classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">calibration_plot</span><span class="p">(</span><span class="n">prob_classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">prob_classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;calibration plot only works for binary problems&#39;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">posteriors</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span>
@ -1238,8 +1238,8 @@
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="n">data</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_UCIBinaryDataset</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">UCI_BINARY_DATASETS</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_test</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>

120
docs/build/html/_modules/quapy/protocol.html vendored
View File

@ -29,7 +29,7 @@
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@ -370,31 +370,31 @@
<article class="bd-article">
<h1>Source code for quapy.protocol</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">ExitStack</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">exists</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">exists</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">glob</span><span class="w"> </span><span class="kn">import</span> <span class="n">glob</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">collections.abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Number</span>
<div class="viewcode-block" id="AbstractProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a>
<span class="k">class</span> <span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract parent class for sample generation protocols.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the protocol. Yields one sample at a time along with its prevalence</span>
@ -405,7 +405,7 @@
<div class="viewcode-block" id="AbstractProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates the total number of samples that the protocol generates.</span>
@ -418,16 +418,16 @@
<div class="viewcode-block" id="IterateProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a>
<span class="k">class</span> <span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A very simple protocol which simply iterates over a list of previously generated samples</span>
<span class="sd"> :param samples: a list of :class:`quapy.data.base.LabelledCollection`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample from the initial list at a time</span>
@ -439,7 +439,7 @@
<div class="viewcode-block" id="IterateProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples in this protocol</span>
@ -452,18 +452,18 @@
<div class="viewcode-block" id="ProtocolFromIndex">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex">[docs]</a>
<span class="k">class</span> <span class="nc">ProtocolFromIndex</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ProtocolFromIndex</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A protocol from a list of indexes</span>
<span class="sd"> :param data: a :class:`quapy.data.base.LabelledCollection`</span>
<span class="sd"> :param indexes: a list of indexes</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">indexes</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">indexes</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indexes</span> <span class="o">=</span> <span class="n">indexes</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample at a time extracted using the indexes</span>
@ -475,7 +475,7 @@
<div class="viewcode-block" id="ProtocolFromIndex.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples in this protocol</span>
@ -488,7 +488,7 @@
<div class="viewcode-block" id="AbstractStochasticSeededProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a>
<span class="k">class</span> <span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,</span>
<span class="sd"> via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.</span>
@ -505,21 +505,21 @@
<span class="n">_random_state</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># means &quot;not set&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span>
<span class="nd">@random_state</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function has to return all the necessary parameters to replicate the samples</span>
@ -531,7 +531,7 @@
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract one sample determined by the given parameters</span>
@ -541,7 +541,7 @@
<span class="o">...</span></div>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample at a time. The type of object returned depends on the `collator` function. The</span>
<span class="sd"> default behaviour returns tuples of the form `(sample, prevalence)`.</span>
@ -560,7 +560,7 @@
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a>
<span class="k">def</span> <span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The collator prepares the sample to accommodate the desired output format before returning the output.</span>
<span class="sd"> This collator simply returns the sample as it is. Classes inheriting from this abstract class can</span>
@ -577,7 +577,7 @@
<div class="viewcode-block" id="OnLabelledCollectionProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a>
<span class="k">class</span> <span class="nc">OnLabelledCollectionProtocol</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">OnLabelledCollectionProtocol</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -586,7 +586,7 @@
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a>
<span class="k">def</span> <span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the labelled collection on which this protocol acts.</span>
@ -597,7 +597,7 @@
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a>
<span class="k">def</span> <span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a copy of this protocol that acts on a modified version of the original</span>
<span class="sd"> :class:`qp.data.LabelledCollection` in which the original instances have been replaced</span>
@ -624,7 +624,7 @@
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a collator function, i.e., a function that prepares the yielded data</span>
@ -646,7 +646,7 @@
<div class="viewcode-block" id="OnLabelledCollectionProtocol.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.sample">[docs]</a>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given the index of the instances.</span>
@ -660,7 +660,7 @@
<div class="viewcode-block" id="APP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a>
<span class="k">class</span> <span class="nc">APP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">APP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implementation of the artificial prevalence protocol (APP).</span>
<span class="sd"> The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,</span>
@ -684,7 +684,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">APP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -706,7 +706,7 @@
<div class="viewcode-block" id="APP.prevalence_grid">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates vectors of prevalence values from an exhaustive grid of prevalence values. The</span>
<span class="sd"> number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,</span>
@ -734,7 +734,7 @@
<div class="viewcode-block" id="APP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the APP protocol.</span>
@ -749,7 +749,7 @@
<div class="viewcode-block" id="APP.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated</span>
@ -762,7 +762,7 @@
<div class="viewcode-block" id="NPP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a>
<span class="k">class</span> <span class="nc">NPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">NPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing</span>
<span class="sd"> samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.</span>
@ -777,7 +777,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">NPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -788,7 +788,7 @@
<div class="viewcode-block" id="NPP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the NPP protocol.</span>
@ -803,7 +803,7 @@
<div class="viewcode-block" id="NPP.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -816,7 +816,7 @@
<div class="viewcode-block" id="UPP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a>
<span class="k">class</span> <span class="nc">UPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">UPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,</span>
<span class="sd"> relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with</span>
@ -835,7 +835,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">UPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -846,7 +846,7 @@
<div class="viewcode-block" id="UPP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the UPP protocol.</span>
@ -861,7 +861,7 @@
<div class="viewcode-block" id="UPP.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -874,7 +874,7 @@
<div class="viewcode-block" id="DirichletProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol">[docs]</a>
<span class="k">class</span> <span class="nc">DirichletProtocol</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DirichletProtocol</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A protocol that establishes a prior Dirichlet distribution for the prevalence of the samples.</span>
<span class="sd"> Note that providing an all-ones vector of Dirichlet parameters is equivalent to invoking the</span>
@ -892,7 +892,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">alpha</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
@ -915,7 +915,7 @@
<div class="viewcode-block" id="DirichletProtocol.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.samples_parameters">[docs]</a>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples.</span>
@ -928,7 +928,7 @@
<div class="viewcode-block" id="DirichletProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -941,7 +941,7 @@
<div class="viewcode-block" id="DomainMixer">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a>
<span class="k">class</span> <span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span>
@ -960,7 +960,7 @@
<span class="sd"> will be the same every time the protocol is called)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">domainA</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
<span class="n">domainB</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
@ -994,7 +994,7 @@
<div class="viewcode-block" id="DomainMixer.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a>
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the this protocol.</span>
@ -1014,7 +1014,7 @@
<div class="viewcode-block" id="DomainMixer.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given a pair of indexes of the instances from A and B.</span>
@ -1029,7 +1029,7 @@
<div class="viewcode-block" id="DomainMixer.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a>
<span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats * mixture_points&quot;)</span>

413
docs/source/manuals/methods.md
View File

@ -2,11 +2,17 @@
Quantification methods can be categorized as belonging to
`aggregative`, `non-aggregative`, and `meta-learning` groups.
Most methods included in QuaPy at the moment are of type `aggregative`
(though we plan to add many more methods in the near future), i.e.,
are methods characterized by the fact that
quantification is performed as an aggregation function of the individual
products of classification.
`Aggregative` quantifiers rely on a surrogate classifier as an intermediate
step, and devise different aggregation functions over the classifier outputs.
By contrast, `non-aggregative` methods perform quantification without requiring
an underlying classifier.
`Meta-learning` refers to quantification methods that are constructed over simpler
quantification methods, and implement high-level orchestration functions.
Beyond these three traditional categories of methods, we here present an additional,
orthogonal one: `Bayesian quantifiers`, i.e., quantification methods that do not simply
return point-estimates of class prevalence, but are also able to provide a measure of
uncertaintly around them.
Any quantifier in QuaPy shoud extend the class `BaseQuantifier`,
and implement some abstract methods:
@ -184,6 +190,10 @@ will be raised otherwise.
Lastly, everything we said about ACC and PCC
applies to PACC as well.
A Bayesian counterpart of the ACC family is also available; see the
{ref}`Bayesian Quantification Methods section <manuals/methods:Bayesian Quantification Methods>`
for `BayesianCC`.
_New in v0.1.9_: quantifiers ACC and PACC now have three additional arguments: `method`, `solver` and `norm`:
* Argument `method` specifies how to solve, for `p`, the linear system `q = Mp` (where `q` is the unadjusted counts for the
@ -213,56 +223,23 @@ Options are:
* `"condsoftmax"` applies softmax normalization only if the prevalence vector lies outside of the probability simplex.
#### BayesianCC
### Threshold Optimization methods
The `BayesianCC` is a variant of ACC introduced in
[Ziegler, A. and Czyż, P. "Bayesian quantification with black-box estimators", arXiv (2023)](https://arxiv.org/abs/2302.09159),
which models the probabilities `q = Mp` using latent random variables with weak Bayesian priors, rather than
plug-in probability estimates. In particular, it uses Markov Chain Monte Carlo sampling to find the values of
`p` compatible with the observed quantities.
The `aggregate` method returns the posterior mean and the `get_prevalence_samples` method can be used to find
uncertainty around `p` estimates (conditional on the observed data and the trained classifier)
and is suitable for problems in which the `q = Mp` matrix is nearly non-invertible.
QuaPy implements Forman's threshold optimization methods;
see, e.g., [(Forman 2006)](https://dl.acm.org/doi/abs/10.1145/1150402.1150423)
and [(Forman 2008)](https://link.springer.com/article/10.1007/s10618-008-0097-y).
These include: `T50`, `MAX`, `X`, Median Sweep (`MS`), and its variant `MS2`.
Note that this quantification method requires `val_split` to be a `float` and installation of additional dependencies (`$ pip install quapy[bayes]`) needed to run Markov chain Monte Carlo sampling. Markov Chain Monte Carlo is is slower than matrix inversion methods, but is guaranteed to sample proper probability vectors, so no clipping strategies are required.
An example presenting how to run the method and use posterior samples is available in `examples/bayesian_quantification.py`.
These methods are binary-only and implement different heuristics for
improving the stability of the denominator of the ACC adjustment (`tpr-fpr`).
The methods are called "threshold" since said heuristics have to do
with different choices of the underlying classifier's threshold.
### Regularized Learning under Label Shift (RLLS)
`RLLS` is available at `qp.method.aggregative.RLLS` and ports the regularized
importance-weight estimation procedure of
[Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A. (2019). Regularized
Learning for Domain Adaptation under Label Shifts.
ICLR 2019](https://arxiv.org/abs/1903.09734) to QuaPy's aggregative interface.
The method estimates the label-shift importance weights `w = q(y)/p(y)` from
the classifier's validation posteriors (or, in `mode='hard'`, its argmax
predictions) and the corresponding source labels, regularizing the estimation
by an amount controlled by `alpha` (scaled by a finite-sample confidence term
governed by `delta`). The resulting weights are then used to rescale the
training prevalence into the target prevalence estimate.
### Expectation Maximization (EMQ) / Maximum Likelihood for Label Shift (MLLS)
Like ACC and PACC, RLLS requires validation predictions and therefore expects
`val_split` to be set (as an integer for k-fold cross-validation, a float for
a held-out split, or an explicit `(X, y)` tuple) whenever `fit_classifier=True`.
This method relies on the optional `cvxpy` dependency, which must be
installed separately (`$ pip install cvxpy`).
```python
import quapy as qp
from quapy.method.aggregative import RLLS
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = RLLS(LogisticRegression(max_iter=2000), val_split=5)
model.fit(*train.Xy)
estim_prevalence = model.predict(test.X)
```
### Expectation Maximization (EMQ)
The Expectation Maximization Quantifier (EMQ), also known as
the SLD, is available at `qp.method.aggregative.EMQ` or via the
The Expectation Maximization Quantifier (EMQ) (also known as
SLD after the name of the proponets, or Maximum Likelihood for Label Shift, MLLS) , is available at `qp.method.aggregative.EMQ` or via the
alias `qp.method.aggregative.ExpectationMaximizationQuantifier`.
The method is described in:
@ -306,41 +283,42 @@ or Temperature Scaling (`ts`); default is `None` (no calibration).
You can use the class method `EMQ_BCTS` to effortlessly instantiate EMQ with the best performing
heuristics found by [Alexandari et al. (2020)](http://proceedings.mlr.press/v119/alexandari20a.html). See the API documentation for further details.
#### BayesianMAPLS
For a Bayesian label-shift counterpart based on the same general family of ideas,
see the {ref}`Bayesian Quantification Methods section <manuals/methods:Bayesian Quantification Methods>`
for `BayesianMAPLS`.
`BayesianMAPLS` is a Bayesian variant of EMQ/MLLS proposed by
Ye, C. et al. (2024). Label shift estimation for class-imbalance problem: A
Bayesian approach. Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV 2024). QuaPy's implementation is
adapted from the [authors' reference code](https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/mapls.py).
Rather than returning a single point estimate for the class prevalence, it
places a Dirichlet prior over the sought prevalence vector (in an
unconstrained, Isometric-Log-Ratio-transformed space) and samples from the
resulting posterior via Markov Chain Monte Carlo (using `numpyro`/`jax`),
conditioned on a preliminary MAP estimate obtained via the underlying `mapls`
routine. Like `BayesianCC`, its `aggregate` method returns the posterior mean,
while `predict_conf` additionally returns a confidence region (`intervals`,
`ellipse`, `ellipse-clr`, or `ellipse-ilr`) built from the posterior samples.
### Regularized Learning under Label Shift (RLLS)
This method requires installation of additional dependencies
(`$ pip install quapy[bayes]`) needed to run MCMC sampling; parameters
`num_warmup` and `num_samples` control the length of the chain, and `prior`
allows choosing between a uniform Dirichlet prior (default) or one of the
data-dependent priors ("map"/"map2") proposed in the original paper.
`RLLS` is available at `qp.method.aggregative.RLLS` and ports the regularized
importance-weight estimation procedure of
[Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A. (2019). Regularized
Learning for Domain Adaptation under Label Shifts.
ICLR 2019](https://arxiv.org/abs/1903.09734) to QuaPy's aggregative interface.
The method estimates the label-shift importance weights `w = q(y)/p(y)` from
the classifier's validation posteriors (or, in `mode='hard'`, its argmax
predictions) and the corresponding source labels, regularizing the estimation
by an amount controlled by `alpha` (scaled by a finite-sample confidence term
governed by `delta`). The resulting weights are then used to rescale the
training prevalence into the target prevalence estimate.
Like ACC and PACC, RLLS requires validation predictions and therefore expects
`val_split` to be set (as an integer for k-fold cross-validation, a float for
a held-out split, or an explicit `(X, y)` tuple) whenever `fit_classifier=True`.
This method relies on the optional `cvxpy` dependency, which must be
installed separately (`$ pip install cvxpy`).
```python
import quapy as qp
from quapy.method._bayesian import BayesianMAPLS
from quapy.method.aggregative import RLLS
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianMAPLS(LogisticRegression())
model = RLLS(LogisticRegression(max_iter=2000), val_split=5)
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
estim_prevalence = model.predict(test.X)
```
### Hellinger Distance y (HDy)
Implementation of the method based on the Hellinger Distance y (HDy) proposed by
@ -390,49 +368,10 @@ framework proposed by [Maletzke et al (2020)](https://ojs.aaai.org/index.php/AAA
and the "SMM" method proposed by [Hassan et al (2019)](https://ieeexplore.ieee.org/document/9260028)
(thanks to _Pablo González_ for the contributions!)
#### PQ
A Bayesian distribution-matching counterpart is also available; see the
{ref}`Bayesian Quantification Methods section <manuals/methods:Bayesian Quantification Methods>`
for `PQ` (Precise Quantifier).
`PQ` (Precise Quantifier), available at `qp.method.confidence.PQ`, is a
Bayesian distribution-matching variant of `HDy` proposed in
[Igiraneza, A.B., Fraser, C., and Hinch, R. (2025). Estimating prevalence
with precision and accuracy.](https://arxiv.org/abs/2507.06061)
Rather than matching a single test histogram against a mixture of two
class-conditional histograms via a divergence measure (as `HDy` does), `PQ`
places the histogram-matching problem in a Bayesian setting and samples the
full posterior distribution over the (binary) prevalence value via Markov
Chain Monte Carlo (using `stan`). Its `aggregate` method returns the
posterior mean, while `predict_conf` additionally returns a confidence
region built from the posterior samples (`intervals`, `ellipse`, or
`ellipse-clr`).
`PQ` accepts `nbins` (the number of histogram bins, quantile-based by default,
or uniform if `fixed_bins=True`), and the usual MCMC controls `num_warmup`,
`num_samples`, and `stan_seed`. This method relies on the optional `stan`
dependency, installed via `$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method.confidence import PQ
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = PQ(LogisticRegression())
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
### Threshold Optimization methods
QuaPy implements Forman's threshold optimization methods;
see, e.g., [(Forman 2006)](https://dl.acm.org/doi/abs/10.1145/1150402.1150423)
and [(Forman 2008)](https://link.springer.com/article/10.1007/s10618-008-0097-y).
These include: `T50`, `MAX`, `X`, Median Sweep (`MS`), and its variant `MS2`.
These methods are binary-only and implement different heuristics for
improving the stability of the denominator of the ACC adjustment (`tpr-fpr`).
The methods are called "threshold" since said heuristics have to do
with different choices of the underlying classifier's threshold.
### Explicit Loss Minimization
@ -540,38 +479,9 @@ All KDE-based methods depend on the hyperparameter `bandwidth` of the kernel. Ty
that can be explored in model selection range in [0.01, 0.25]. Previous experiments reveal the methods' performance
varies smoothly at small variations of this hyperparameter.
#### BayesianKDEy
`BayesianKDEy`, available at `qp.method._bayesian.BayesianKDEy`, is a Bayesian
version of KDEy. Instead of solving for the single prevalence vector that
minimizes a divergence between the test distribution and a KDE-based mixture
model (as the KDEy variants above do), `BayesianKDEy` places a Dirichlet
prior over the prevalence vector and samples its posterior via Markov Chain
Monte Carlo (using `numpyro`/`jax`), conditioned on the same KDE mixture
components. Its `aggregate` method returns the posterior mean, while
`predict_conf` additionally returns a confidence region built from the
posterior samples.
In addition to the `kernel` and `bandwidth` hyperparameters (with the same
`gaussian`/`aitchison`/`ilr` kernel choice, and `shrinkage` regularization
for the latter two, available in `KDEyML`), `BayesianKDEy` exposes the usual
MCMC controls: `num_warmup`, `num_samples`, `mcmc_seed`, a `temperature` for
posterior calibration, and `prior` for choosing the Dirichlet prior
(`'uniform'` by default, or a custom scalar/array). This method relies on
the optional MCMC dependencies, installed via
`$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method._bayesian import BayesianKDEy
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianKDEy(LogisticRegression(), bandwidth=0.1)
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
A Bayesian counterpart is available as well; see the
{ref}`Bayesian Quantification Methods section <manuals/methods:Bayesian Quantification Methods>`
for `BayesianKDEy`.
## Non-Aggregative Methods
@ -678,7 +588,7 @@ subset, and the resulting estimates are averaged. ReadMe additionally
combines this bagging procedure with bootstrap resampling of the training
instances in order to derive confidence regions around the point estimate;
accordingly, `ReadMe` implements the `WithConfidenceABC` interface (see the
{ref}`confidence regions section <manuals/methods:Confidence Regions for Class Prevalence Estimation>`),
{ref}`confidence regions section <confidence-regions-for-class-prevalence-estimation>`),
and exposes a `predict_conf` method in addition to `predict`.
`ReadMe` accepts the following hyperparameters:
@ -701,9 +611,11 @@ and exposes a `predict_conf` method in addition to `predict`.
* `confidence_level`: the confidence level for the confidence region
(default 0.95)
* `region`: the type of confidence region to construct, one of `"intervals"`
(default), `"ellipse"`, or `"ellipse-clr"` (see the
{ref}`confidence regions section <manuals/methods:Confidence Regions for Class Prevalence Estimation>`
(default), `"ellipse"`, `"ellipse-clr"`, or `"ellipse-ilr"` (see the
{ref}`confidence regions section <confidence-regions-for-class-prevalence-estimation>`
for details)
* `bonferroni`: whether to apply Bonferroni correction when `region="intervals"`
(default `False`); this parameter has no effect for ellipse-based regions
* `random_state`: an int for replicability, or `None` (default)
* `verbose`: whether to display progress information (default False)
@ -891,28 +803,191 @@ model.fit(*dataset.training.Xy)
estim_prevalence = model.predict(dataset.test.X)
```
## Confidence Regions for Class Prevalence Estimation
(confidence-regions-for-class-prevalence-estimation)=
## Quantifiers with Uncertainty Quantification
_(New in v0.2.0!)_ Some quantification methods go beyond providing a single point estimate of class prevalence values and also produce confidence regions, which characterize the uncertainty around the point estimate. In QuaPy, two such methods are currently implemented:
* Aggregative Bootstrap: The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. The method is described in the paper [Moreo, A., Salvati, N.
An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
pp 12-33, Porto (Portugal)](https://lq-2025.github.io/proceedings/CompleteVolume.pdf). Key features of this method include:
* Optimized Computation: The bootstrap is applied to pre-classified instances, significantly speeding up training and inference.
During training, bootstrap repetitions are performed only after training the classifier once. These repetitions are used to train multiple aggregation functions.
During inference, bootstrap is applied over pre-classified test instances.
* General Applicability: Aggregative Bootstrap can be applied to any aggregative quantifier.
For further information, check the [example](https://github.com/HLT-ISTI/QuaPy/tree/master/examples/16.confidence_regions.py) provided.
* BayesianCC: is a Bayesian variant of the Adjusted Classify & Count (ACC) quantifier; see more details in the [example](https://github.com/HLT-ISTI/QuaPy/tree/master/examples/14.bayesian_quantification.py) provided.
_(New in v0.2.0!)_ Some quantification methods go beyond providing a single point estimate of class prevalence values and also produce confidence regions, which characterize the uncertainty around the point estimate. In QuaPy, two such families are currently implemented: bootstrap methods and Bayesian methods.
Confidence regions are constructed around a point estimate, which is typically computed as the mean value of a set of samples.
The confidence region can be instantiated in three ways:
* Confidence intervals: are standard confidence intervals generated for each class independently (_method="intervals"_).
The confidence region can be instantiated in four ways:
* Confidence intervals: are standard confidence intervals generated for each class independently (_method="intervals"_). Since confidence intervals are independently derived for each class, Bonferroni correction can be applied.
* Confidence ellipse in the simplex: an ellipse constructed around the mean point; the ellipse lies on the simplex and takes
into account possible inter-class dependencies in the data (_method="ellipse"_).
into account possible inter-class dependencies in the data (_method="ellipse"_).
* Confidence ellipse in the Centered-Log Ratio (CLR) space: the underlying assumption of the ellipse is that the components are
normally distributed. However, we know elements from the simplex have an inner structure. A better approach is to first
transform the components into an unconstrained space (the CLR), and then construct the ellipse in such space (_method="ellipse-clr"_).
transform the components into an unconstrained space (the CLR), and then construct the ellipse in such space (_method="ellipse-clr"_).
* Confidence ellipse in the Isometric-Log Ratio (ILR) space: analogous to the CLR-based ellipse, but built in the ILR space
(_method="ellipse-ilr"_).
### Aggregative Bootstrap
The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. The method is described in the paper [Moreo, A., Salvati, N.
An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
pp 12-33, Porto (Portugal)](https://lq-2025.github.io/proceedings/CompleteVolume.pdf).
This implementation is optimized for aggregative quantifiers. The bootstrap is applied to pre-classified instances, significantly speeding up training and inference.
During training, bootstrap repetitions are performed only after training the classifier once. These repetitions are used to train multiple aggregation functions.
During inference, bootstrap is applied over pre-classified test instances.
Aggregative Bootstrap can be applied to any aggregative quantifier. For further information, check the [example](https://github.com/HLT-ISTI/QuaPy/tree/master/examples/16.confidence_regions.py) provided. A minimal working example is:
```python
import quapy as qp
from quapy.method.aggregative import PACC
from quapy.method.confidence import AggregativeBootstrap
train, test = qp.datasets.fetch_UCIMulticlassDataset('molecular').train_test
model = AggregativeBootstrap(
PACC(),
n_test_samples=200,
confidence_level=0.95,
region='ellipse-clr', # choose among: intervals, ellipse, ellipse-clr, ellipse-ilr
random_state=0,
)
model.fit(*train.Xy)
point_estimate, conf_region = model.predict_conf(test.X)
```
Here `region` makes the type of uncertainty region explicit. In practice, `intervals` is often the simplest default, while `ellipse`, `ellipse-clr`, and `ellipse-ilr` provide coupled regions over the simplex. If `region='intervals'`, you can additionally set `bonferroni=True` to apply Bonferroni correction; this flag has no effect for ellipse-based regions.
Beyond aggregative quantifiers, Bootstrap sampling can be applied to any type of quantification method, although
the speedup procedure described above is not applied.
### Bayesian Quantification Methods
QuaPy also provides a number of Bayesian quantifiers. While these methods are
usually related to an existing point-estimation family (e.g., ACC, EMQ/MLLS,
HDy, or KDEy), they differ enough in their goals and outputs to deserve a
separate presentation. In particular, Bayesian quantifiers typically return a
posterior mean rather than a single optimization result, expose posterior
samples or confidence regions, and often require additional probabilistic
inference dependencies.
The optional dependencies needed for these methods can be installed with:
```sh
pip install quapy[bayes]
```
#### BayesianCC (a Bayesian implementation of ACC)
The `BayesianCC` is a variant of ACC introduced in
[Ziegler, A. and Czyż, P. "Bayesian quantification with black-box estimators", arXiv (2023)](https://arxiv.org/abs/2302.09159),
which models the probabilities `q = Mp` using latent random variables with weak Bayesian priors, rather than
plug-in probability estimates. In particular, it uses Markov Chain Monte Carlo sampling to find the values of
`p` compatible with the observed quantities.
The `aggregate` method returns the posterior mean and the `get_prevalence_samples` method can be used to find
uncertainty around `p` estimates (conditional on the observed data and the trained classifier)
and is suitable for problems in which the `q = Mp` matrix is nearly non-invertible.
Note that this quantification method requires `val_split` to be a `float` and installation of additional dependencies (`$ pip install quapy[bayes]`) needed to run Markov chain Monte Carlo sampling. Markov Chain Monte Carlo is is slower than matrix inversion methods, but is guaranteed to sample proper probability vectors, so no clipping strategies are required.
An example presenting how to run the method and use posterior samples is available in `examples/bayesian_quantification.py`.
#### BayesianMAPLS (a Bayesian implementation of EMQ/MLLS)
`BayesianMAPLS` is a Bayesian variant of EMQ/MLLS proposed by
Ye, C. et al. (2024). Label shift estimation for class-imbalance problem: A
Bayesian approach. Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV 2024). QuaPy's implementation is
adapted from the [authors' reference code](https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/mapls.py).
Rather than returning a single point estimate for the class prevalence, it
places a Dirichlet prior over the sought prevalence vector (in an
unconstrained, Isometric-Log-Ratio-transformed space) and samples from the
resulting posterior via Markov Chain Monte Carlo (using `numpyro`/`jax`),
conditioned on a preliminary MAP estimate obtained via the underlying `mapls`
routine. Like `BayesianCC`, its `aggregate` method returns the posterior mean,
while `predict_conf` additionally returns a confidence region (`intervals`,
`ellipse`, `ellipse-clr`, or `ellipse-ilr`) built from the posterior samples.
For interval regions, all Bayesian methods also accept `bonferroni=True` to
apply Bonferroni correction; this flag has no effect for ellipse-based regions.
This method requires installation of additional dependencies
(`$ pip install quapy[bayes]`) needed to run MCMC sampling; parameters
`num_warmup` and `num_samples` control the length of the chain, and `prior`
allows choosing between a uniform Dirichlet prior (default) or one of the
data-dependent priors ("map"/"map2") proposed in the original paper.
```python
import quapy as qp
from quapy.method._bayesian import BayesianMAPLS
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianMAPLS(LogisticRegression())
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
#### PQ: Precise Quantifier (a Bayesian implementation of HDy)
`PQ` (Precise Quantifier), available at `qp.method.confidence.PQ`, is a
Bayesian distribution-matching variant of `HDy` proposed in
[Igiraneza, A.B., Fraser, C., and Hinch, R. (2025). Estimating prevalence
with precision and accuracy.](https://arxiv.org/abs/2507.06061)
Rather than matching a single test histogram against a mixture of two
class-conditional histograms via a divergence measure (as `HDy` does), `PQ`
places the histogram-matching problem in a Bayesian setting and samples the
full posterior distribution over the (binary) prevalence value via Markov
Chain Monte Carlo (using `stan`). Its `aggregate` method returns the
posterior mean, while `predict_conf` additionally returns a confidence
region built from the posterior samples (`intervals`, `ellipse`, or
`ellipse-clr`).
`PQ` accepts `nbins` (the number of histogram bins, quantile-based by default,
or uniform if `fixed_bins=True`), and the usual MCMC controls `num_warmup`,
`num_samples`, and `stan_seed`. This method relies on the optional `stan`
dependency, installed via `$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method.confidence import PQ
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = PQ(LogisticRegression())
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
#### BayesianKDEy (a Bayesian implementation of KDEyML)
`BayesianKDEy`, available at `qp.method._bayesian.BayesianKDEy`, is a Bayesian
version of KDEy. Instead of solving for the single prevalence vector that
minimizes a divergence between the test distribution and a KDE-based mixture
model (as the KDEy variants above do), `BayesianKDEy` places a Dirichlet
prior over the prevalence vector and samples its posterior via Markov Chain
Monte Carlo (using `numpyro`/`jax`), conditioned on the same KDE mixture
components. Its `aggregate` method returns the posterior mean, while
`predict_conf` additionally returns a confidence region built from the
posterior samples.
In addition to the `kernel` and `bandwidth` hyperparameters (with the same
`gaussian`/`aitchison`/`ilr` kernel choice, and `shrinkage` regularization
for the latter two, available in `KDEyML`), `BayesianKDEy` exposes the usual
MCMC controls: `num_warmup`, `num_samples`, `mcmc_seed`, a `temperature` for
posterior calibration, and `prior` for choosing the Dirichlet prior
(`'uniform'` by default, or a custom scalar/array). This method relies on
the optional MCMC dependencies, installed via
`$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method._bayesian import BayesianKDEy
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianKDEy(LogisticRegression(), bandwidth=0.1)
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```

3
examples/16.confidence_regions.py
View File

@ -75,7 +75,8 @@ There are different ways for constructing confidence regions implemented in QuaP
convenient for taking into account the inner structure of the probability simplex)
use: AggregativeBootstrap(PACC(), confidence_level=0.95, method='ellipse-clr')
Other methods that return confidence regions in QuaPy include the BayesianCC method.
Other methods that return confidence regions in QuaPy include the Bayesian methods (BayesianCC,
BayesianMAPLS, PQ, and BayesianKDEy).
"""

12
quapy/method/_bayesian.py
View File

@ -271,6 +271,9 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
intervals (default), or to `ellipse` for constructing an ellipse in
the probability simplex, or to `ellipse-clr` for constructing an
ellipse in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni
correction when `region='intervals'`. This parameter has no effect
for ellipse-based regions.
:param temperature: temperature (>0) for posterior calibration
(default 1.)
:param prior: an array-like with the alpha parameters of a Dirichlet
@ -292,6 +295,7 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
mcmc_seed: int = 0,
confidence_level: float = 0.95,
region: str = 'intervals',
bonferroni: bool = False,
temperature: float = 1.0,
prior='uniform',
verbose: bool = False,
@ -315,6 +319,7 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
self.mcmc_seed = mcmc_seed
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
self.temperature = _validate_temperature(temperature)
self.prior = prior
self.verbose = verbose
@ -358,6 +363,7 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
self.prevalence_samples,
confidence_level=confidence_level,
method=self.region,
bonferroni=self.bonferroni,
)
return point_estimate, region
@ -433,6 +439,9 @@ class BayesianMAPLS(AggregativeSoftQuantifier, WithConfidenceABC):
intervals (default), or to `ellipse` for constructing an ellipse in
the probability simplex, or to `ellipse-clr` for constructing an
ellipse in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni
correction when `region='intervals'`. This parameter has no effect
for ellipse-based regions.
:param temperature: temperature (>0) for posterior calibration
(default 1.)
:param prior: an array-like with the alpha parameters of a Dirichlet
@ -455,6 +464,7 @@ class BayesianMAPLS(AggregativeSoftQuantifier, WithConfidenceABC):
mcmc_seed: int = 0,
confidence_level: float = 0.95,
region: str = 'intervals',
bonferroni: bool = False,
temperature: float = 1.0,
prior='uniform',
mapls_chain_init=True,
@ -482,6 +492,7 @@ class BayesianMAPLS(AggregativeSoftQuantifier, WithConfidenceABC):
self.mcmc_seed = mcmc_seed
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
self.temperature = _validate_temperature(temperature)
self.prior = prior
self.mapls_chain_init = mapls_chain_init
@ -553,6 +564,7 @@ class BayesianMAPLS(AggregativeSoftQuantifier, WithConfidenceABC):
self.prevalence_samples,
confidence_level=confidence_level,
method=self.region,
bonferroni=self.bonferroni,
)
return point_estimate, region

48
quapy/method/confidence.py
View File

@ -176,7 +176,7 @@ class WithConfidenceABC(ABC):
return self.predict_conf(instances=instances, confidence_level=confidence_level)
@classmethod
def construct_region(cls, prev_estims, confidence_level=0.95, method='intervals')->ConfidenceRegionABC:
def construct_region(cls, prev_estims, confidence_level=0.95, method='intervals', bonferroni=False)->ConfidenceRegionABC:
"""
Construct a confidence region given many prevalence estimations.
@ -186,10 +186,16 @@ class WithConfidenceABC(ABC):
constructing confidence intervals (default), or to `ellipse` for constructing an
ellipse in the probability simplex, or to `ellipse-clr` for constructing an ellipse
in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni correction when
`method='intervals'`. This parameter has no effect for ellipse-based regions.
"""
region = None
if method == 'intervals':
region = ConfidenceIntervals(prev_estims, confidence_level=confidence_level)
region = ConfidenceIntervals(
prev_estims,
confidence_level=confidence_level,
bonferroni_correction=bonferroni,
)
elif method == 'ellipse':
region = ConfidenceEllipseSimplex(prev_estims, confidence_level=confidence_level)
elif method == 'ellipse-clr':
@ -337,7 +343,7 @@ class ConfidenceIntervals(ConfidenceRegionABC):
"""
Instantiates a region based on (independent) Confidence Intervals.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
:param bonferroni_correction: bool (default False), if True, a Bonferroni correction
is applied to the significance level (`alpha`) before computing confidence intervals.
@ -432,7 +438,7 @@ class ConfidenceEllipseSimplex(ConfidenceRegionABC):
"""
Instantiates a Confidence Ellipse in the probability simplex.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
"""
@ -496,7 +502,7 @@ class ConfidenceEllipseTransformed(ConfidenceRegionABC):
"""
Instantiates a Confidence Ellipse in a transformed space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
"""
@ -549,7 +555,7 @@ class ConfidenceEllipseCLR(ConfidenceEllipseTransformed):
"""
Instantiates a Confidence Ellipse in the Centered-Log Ratio (CLR) space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
"""
def __init__(self, samples, confidence_level=0.95):
@ -560,7 +566,7 @@ class ConfidenceEllipseILR(ConfidenceEllipseTransformed):
"""
Instantiates a Confidence Ellipse in the Isometric-Log Ratio (CLR) space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
"""
def __init__(self, samples, confidence_level=0.95):
@ -572,7 +578,7 @@ class ConfidenceIntervalsTransformed(ConfidenceRegionABC):
"""
Instantiates a Confidence Interval region in a transformed space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
:param bonferroni_correction: bool (default False), if True, a Bonferroni correction
is applied to the significance level (`alpha`) before computing confidence intervals.
@ -636,7 +642,7 @@ class ConfidenceIntervalsCLR(ConfidenceIntervalsTransformed):
"""
Instantiates a Confidence Intervals in the Centered-Log Ratio (CLR) space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
:param bonferroni_correction: bool (default False), if True, a Bonferroni correction
is applied to the significance level (`alpha`) before computing confidence intervals.
@ -652,7 +658,7 @@ class ConfidenceIntervalsILR(ConfidenceIntervalsTransformed):
"""
Instantiates a Confidence Intervals in the Isometric-Log Ratio (CLR) space.
:param samples: np.ndarray of shape (n_bootstrap_samples, n_classes)
:param samples: np.ndarray of shape (n_samples, n_classes)
:param confidence_level: float, the confidence level (default 0.95)
:param bonferroni_correction: bool (default False), if True, a Bonferroni correction
is applied to the significance level (`alpha`) before computing confidence intervals.
@ -693,6 +699,8 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
:param region: string, set to `intervals` for constructing confidence intervals (default), or to
`ellipse` for constructing an ellipse in the probability simplex, or to `ellipse-clr` for
constructing an ellipse in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni correction when
`region='intervals'`. This parameter has no effect for ellipse-based regions.
:param random_state: int for replicating samples, None (default) for non-replicable samples
"""
@ -702,6 +710,7 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
n_test_samples=500,
confidence_level=0.95,
region='intervals',
bonferroni=False,
random_state=None,
verbose=False):
@ -719,6 +728,7 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
self.n_test_samples = n_test_samples
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
self.random_state = random_state
self.verbose = verbose
@ -771,7 +781,7 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
prev_i = quantifier.aggregate(sample_i)
prevs.append(prev_i)
conf = WithConfidenceABC.construct_region(prevs, confidence_level, method=self.region)
conf = WithConfidenceABC.construct_region(prevs, confidence_level, method=self.region, bonferroni=self.bonferroni)
prev_estim = conf.point_estimate()
return prev_estim, conf
@ -791,7 +801,7 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
prevs.extend(results)
prevs = np.array(prevs)
conf = WithConfidenceABC.construct_region(prevs, confidence_level, method=self.region)
conf = WithConfidenceABC.construct_region(prevs, confidence_level, method=self.region, bonferroni=self.bonferroni)
prev_estim = conf.point_estimate()
return prev_estim, conf
@ -850,6 +860,8 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
:param region: string, set to `intervals` for constructing confidence intervals (default), or to
`ellipse` for constructing an ellipse in the probability simplex, or to `ellipse-clr` for
constructing an ellipse in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni correction when
`region='intervals'`. This parameter has no effect for ellipse-based regions.
:param prior: an array-like with the alpha parameters of a Dirichlet prior, a scalar real value
to be broadcast to all classes, or the string 'uniform' for a uniform, uninformative prior (default)
"""
@ -862,6 +874,7 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
mcmc_seed: int = 0,
confidence_level: float = 0.95,
region: str = 'intervals',
bonferroni: bool = False,
temperature = 1.,
prior = 'uniform'):
@ -885,6 +898,7 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
self.mcmc_seed = mcmc_seed
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
self.temperature = temperature
self.prior = prior
@ -964,7 +978,7 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
classif_predictions = self.classify(instances)
point_estimate = self.aggregate(classif_predictions)
samples = self.get_prevalence_samples() # available after calling "aggregate" function
region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region)
region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region, bonferroni=self.bonferroni)
return point_estimate, region
@ -992,6 +1006,8 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
:param region: string, set to `intervals` for constructing confidence intervals (default), or to
`ellipse` for constructing an ellipse in the probability simplex, or to `ellipse-clr` for
constructing an ellipse in the Centered-Log Ratio (CLR) unconstrained space.
:param bonferroni: bool (default False), whether to apply Bonferroni correction when
`region='intervals'`. This parameter has no effect for ellipse-based regions.
"""
def __init__(self,
classifier: BaseEstimator=None,
@ -1003,7 +1019,8 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
num_samples: int = 1_000,
stan_seed: int = 0,
confidence_level: float = 0.95,
region: str = 'intervals'):
region: str = 'intervals',
bonferroni: bool = False):
if num_warmup <= 0:
raise ValueError(f'parameter {num_warmup=} must be a positive integer')
@ -1025,6 +1042,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
self.stan_code = bayesian.load_stan_file()
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
def aggregation_fit(self, classif_predictions, labels):
y_pred = classif_predictions[:, self.pos_label]
@ -1064,7 +1082,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
confidence_level = self.confidence_level
point_estimate = self.aggregate(predictions)
samples = self.prev_distribution
region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region)
region = WithConfidenceABC.construct_region(samples, confidence_level=confidence_level, method=self.region, bonferroni=self.bonferroni)
return point_estimate, region
def predict_conf(self, instances, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC):

10
quapy/method/non_aggregative.py
View File

@ -194,6 +194,8 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
:param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)
:param region: str in 'intervals', 'ellipse', 'ellipse-clr'; indicates the preferred method for
defining the confidence region (see :class:`WithConfidenceABC`)
:param bonferroni: bool (default False), whether to apply Bonferroni correction when
`region='intervals'`. This parameter has no effect for ellipse-based regions.
:param random_state: int or None, allows replicability (default None)
:param verbose: bool, whether to display information during the process (default False)
"""
@ -208,6 +210,7 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
bagging_range=15,
confidence_level=0.95,
region='intervals',
bonferroni=False,
random_state=None,
verbose=False):
assert prob_model in ReadMe.PROBABILISTIC_MODELS, \
@ -218,6 +221,7 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
self.bagging_range = bagging_range
self.confidence_level = confidence_level
self.region = region
self.bonferroni = bonferroni
self.random_state = random_state
self.verbose = verbose
@ -238,8 +242,10 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
return self
def predict_conf(self, X, confidence_level=0.95) -> (np.ndarray, ConfidenceRegionABC):
def predict_conf(self, X, confidence_level=None) -> (np.ndarray, ConfidenceRegionABC):
self._check_matrix(X)
if confidence_level is None:
confidence_level = self.confidence_level
n_features = X.shape[1]
boots_prevalences = []
@ -257,7 +263,7 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
boots_prevalences.append(np.mean(bagging_estimates, axis=0))
conf = WithConfidenceABC.construct_region(boots_prevalences, confidence_level, method=self.region)
conf = WithConfidenceABC.construct_region(boots_prevalences, confidence_level, method=self.region, bonferroni=self.bonferroni)
prev_estim = conf.point_estimate()
return prev_estim, conf

29
quapy/tests/test_confidence.py
View File

@ -4,7 +4,7 @@ import numpy as np
from sklearn.linear_model import LogisticRegression
from quapy.method.aggregative import PACC
from quapy.method.confidence import ConfidenceIntervals, ConfidenceEllipseSimplex, AggregativeBootstrap
from quapy.method.confidence import ConfidenceIntervals, ConfidenceEllipseSimplex, AggregativeBootstrap, WithConfidenceABC
from quapy.tests._synthetic import make_dataset
@ -28,6 +28,16 @@ class TestConfidenceRegions(unittest.TestCase):
point_estimate = region.point_estimate()
self.assertIn(point_estimate, region)
def test_construct_region_applies_bonferroni_only_to_intervals(self):
samples = _dirichlet_samples()
region_plain = WithConfidenceABC.construct_region(samples, confidence_level=0.9, method='intervals')
region_bonf = WithConfidenceABC.construct_region(samples, confidence_level=0.9, method='intervals', bonferroni=True)
ellipse = WithConfidenceABC.construct_region(samples, confidence_level=0.9, method='ellipse', bonferroni=True)
self.assertAlmostEqual(region_plain.alpha, 0.1)
self.assertAlmostEqual(region_bonf.alpha, 0.1 / samples.shape[1])
self.assertIsInstance(ellipse, ConfidenceEllipseSimplex)
def test_simplex_portion_is_cached_and_consistent(self):
# regression test for the @lru_cache-on-bound-method memory leak fix:
# results must still be memoized per instance, and two instances must not share state
@ -57,6 +67,23 @@ class TestConfidenceRegions(unittest.TestCase):
self.assertEqual(len(point_estimate), 3)
self.assertEqual(region.coverage(point_estimate), 1.)
def test_aggregative_bootstrap_exposes_bonferroni(self):
dataset = make_dataset(n_train=150, n_test=50, n_classes=3, n_features=12, random_state=7)
learner = LogisticRegression(max_iter=2000)
learner.fit(*dataset.training.Xy)
quantifier = AggregativeBootstrap(
PACC(learner, fit_classifier=False),
n_test_samples=20,
confidence_level=0.9,
region='intervals',
bonferroni=True,
random_state=0,
)
quantifier.fit(*dataset.training.Xy)
_, region = quantifier.predict_conf(dataset.test.X)
self.assertAlmostEqual(region.alpha, 0.1 / 3)
if __name__ == '__main__':
unittest.main()