added dataset for images
This commit is contained in:
Alejandro Moreo
parent
a1dff57db0
commit
4916919833
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@ -6,12 +6,15 @@ Change Log 0.2.1
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- Added temperature calibration utilities for Bayesian confidence-aware methods.
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- Added compositional CLR and ILR transformations.
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- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
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- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST
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- Added TemperatureScalingFromLogits for calibrating pretrained logits.
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- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
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- Added ReadMe method by Daniel Hopkins and Gary King.
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- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
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- Improved unit testing and separated integration tests.
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- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
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- Added visualization tools for 3-class problems in the simplex, see also the new example no.19
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Change Log 0.2.0
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-----------------
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@ -330,6 +369,7 @@
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<h1>All modules for which code is available</h1>
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<ul><li><a href="quapy/classification/calibration.html">quapy.classification.calibration</a></li>
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@ -339,6 +379,7 @@
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">quapy.classification.neural</span></li>
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<h1>Source code for quapy.classification.neural</h1><div class="highlight"><pre>
|
||||
<span></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">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">"""</span>
|
||||
<span class="sd"> Trains a neural network for text classification.</span>
|
||||
|
||||
|
|
@ -107,7 +407,7 @@
|
|||
<span class="sd"> according to the evaluation in the held-out validation split (default '../checkpoint/classifier_net.dat')</span>
|
||||
<span class="sd"> """</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">'TextClassifierNet'</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>
|
||||
|
|
@ -142,7 +442,7 @@
|
|||
|
||||
<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">"""Reinitialize the network parameters</span>
|
||||
|
||||
<span class="sd"> :param vocab_size: the size of the vocabulary</span>
|
||||
|
|
@ -155,7 +455,7 @@
|
|||
|
||||
<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">"""Get hyper-parameters for this estimator</span>
|
||||
|
||||
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
|
||||
|
|
@ -165,7 +465,7 @@
|
|||
|
||||
<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">"""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>
|
||||
|
|
@ -191,14 +491,14 @@
|
|||
|
||||
|
||||
<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">""" Gets the device in which the network is allocated</span>
|
||||
|
||||
<span class="sd"> :return: device</span>
|
||||
<span class="sd"> """</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>
|
||||
|
|
@ -218,7 +518,7 @@
|
|||
<span class="n">status</span><span class="p">[</span><span class="s2">"f1"</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">'macro'</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>
|
||||
|
|
@ -236,7 +536,7 @@
|
|||
<span class="n">status</span><span class="p">[</span><span class="s2">"f1"</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">'macro'</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">'[</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"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'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">"tr"</span><span class="p">][</span><span class="s2">"loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'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">"tr"</span><span class="p">][</span><span class="s2">"acc"</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% '</span>
|
||||
|
|
@ -248,7 +548,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Fits the model according to the given training data.</span>
|
||||
|
||||
|
|
@ -297,7 +597,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances</span>
|
||||
|
||||
|
|
@ -310,7 +610,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances</span>
|
||||
|
||||
|
|
@ -329,7 +629,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Returns the embeddings of the instances</span>
|
||||
|
||||
|
|
@ -351,7 +651,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Transforms labelled instances into a Torch's :class:`torch.utils.data.DataLoader` object</span>
|
||||
|
||||
|
|
@ -359,19 +659,19 @@
|
|||
<span class="sd"> :param labels: array-like of shape `(n_samples, n_classes)` with the class labels</span>
|
||||
<span class="sd"> """</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">'doc'</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">'label'</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">"""</span>
|
||||
<span class="sd"> Converts the labelled collection into a Torch DataLoader with dynamic padding for</span>
|
||||
<span class="sd"> the batch</span>
|
||||
|
|
@ -384,7 +684,7 @@
|
|||
<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"> """</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">'doc'</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">'PAD_INDEX'</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">'label'</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>
|
||||
|
|
@ -402,7 +702,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Abstract Text classifier (`torch.nn.Module`)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
|
@ -410,7 +710,7 @@
|
|||
<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">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
|
|
@ -425,7 +725,7 @@
|
|||
|
||||
<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">"""Performs the forward pass.</span>
|
||||
|
||||
<span class="sd"> :param x: a batch of instances, typically generated by a torch's `DataLoader`</span>
|
||||
|
|
@ -439,7 +739,7 @@
|
|||
|
||||
<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">"""Gets the number of dimensions of the embedding space</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
|
|
@ -449,7 +749,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances in `x`</span>
|
||||
|
||||
|
|
@ -464,7 +764,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Performs Xavier initialization of the network parameters</span>
|
||||
<span class="sd"> """</span>
|
||||
|
|
@ -476,7 +776,7 @@
|
|||
<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">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
|
|
@ -486,7 +786,7 @@
|
|||
|
||||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
|
|
@ -498,7 +798,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
|
||||
<span class="sd"> Long Short Term Memory networks.</span>
|
||||
|
|
@ -512,7 +812,7 @@
|
|||
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
|
||||
<span class="sd"> """</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>
|
||||
|
|
@ -534,7 +834,7 @@
|
|||
<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">'lstm_class_nlayers'</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">'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="n">opt</span><span class="p">[</span><span class="s1">'lstm_class_nlayers'</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">'hidden_size'</span><span class="p">])</span>
|
||||
|
|
@ -544,7 +844,7 @@
|
|||
|
||||
<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">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
|
|
@ -563,7 +863,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
|
|
@ -573,7 +873,7 @@
|
|||
|
||||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
|
|
@ -585,7 +885,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
|
||||
<span class="sd"> Convolutional Neural Networks.</span>
|
||||
|
|
@ -602,7 +902,7 @@
|
|||
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</span>
|
||||
<span class="sd"> """</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>
|
||||
|
||||
|
|
@ -627,7 +927,7 @@
|
|||
<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>
|
||||
|
|
@ -635,7 +935,7 @@
|
|||
|
||||
<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">"""Embeds documents (i.e., performs the forward pass up to the</span>
|
||||
<span class="sd"> next-to-last layer).</span>
|
||||
|
||||
|
|
@ -660,7 +960,7 @@
|
|||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator</span>
|
||||
|
||||
|
|
@ -670,7 +970,7 @@
|
|||
|
||||
|
||||
<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">"""</span>
|
||||
<span class="sd"> Return the size of the vocabulary</span>
|
||||
|
||||
|
|
@ -685,31 +985,75 @@
|
|||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</article>
|
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</p>
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@ -1,78 +1,374 @@
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<h1>Source code for quapy.data.datasets</h1><div class="highlight"><pre>
|
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<span></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>
|
||||
|
|
@ -195,6 +491,9 @@
|
|||
<span class="s1">'T4'</span><span class="p">:</span> <span class="mi">250</span><span class="p">,</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<span class="n">IMAGE_DATASETS</span><span class="o">=</span><span class="p">[</span><span class="s1">'cifar10'</span><span class="p">,</span> <span class="s1">'cifar100'</span><span class="p">,</span> <span class="s1">'cifar100coarse'</span><span class="p">,</span> <span class="s1">'svhn'</span><span class="p">,</span> <span class="s1">'fashionmnist'</span><span class="p">,</span> <span class="s1">'mnist'</span><span class="p">]</span>
|
||||
<span class="n">IMAGE_EMBEDDINGS</span><span class="o">=</span><span class="p">[</span><span class="s1">'features'</span><span class="p">,</span> <span class="s1">'logits'</span><span class="p">,</span> <span class="s1">'predictions'</span><span class="p">]</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fetch_reviews">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_reviews">[docs]</a>
|
||||
|
|
@ -1165,33 +1464,177 @@
|
|||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">train_gen</span><span class="p">,</span> <span class="n">test_gen</span></div>
|
||||
|
||||
|
||||
|
||||
<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">-></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">"""</span>
|
||||
<span class="sd"> Loads a pre-generated embedding set (train, val, or test) of an image dataset from `Zenodo <https://zenodo.org/records/21131944>`_.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Embeddings were extracted using `this script <https://github.com/pglez82/visiondatasets_quapy>`_. </span>
|
||||
|
||||
<span class="sd"> :param dataset_name: the name of the dataset: valid ones are 'cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist'</span>
|
||||
<span class="sd"> :param embedding: the type of embedding: valid ones are 'features' (next-to-last representations), 'logits' (pre-activation values), 'predictions' (posterior probabilities)</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :return: a tuple (train, val, test) where each entry is an instance of :class:`quapy.data.base.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="n">IMAGE_DATASETS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1"> does not match any known dataset. Valid ones are </span><span class="si">{</span><span class="n">IMAGE_DATASETS</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">assert</span> <span class="n">embedding</span> <span class="ow">in</span> <span class="n">IMAGE_EMBEDDINGS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'Name </span><span class="si">{</span><span class="n">embedding</span><span class="si">}</span><span class="s1"> does not match any known type of embedding. Valid ones are </span><span class="si">{</span><span class="n">IMAGE_EMBEDDINGS</span><span class="si">}</span><span class="s1">'</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>
|
||||
|
||||
<span class="n">dataset_network</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'cifar10'</span><span class="p">:</span> <span class="s1">'resnet18'</span><span class="p">,</span>
|
||||
<span class="s1">'cifar100'</span><span class="p">:</span> <span class="s1">'resnet18'</span><span class="p">,</span>
|
||||
<span class="s1">'cifar100coarse'</span><span class="p">:</span> <span class="s1">'resnet18'</span><span class="p">,</span>
|
||||
<span class="s1">'svhn'</span><span class="p">:</span> <span class="s1">'resnet18'</span><span class="p">,</span>
|
||||
<span class="s1">'fashionmnist'</span><span class="p">:</span> <span class="s1">'basiccnn'</span><span class="p">,</span>
|
||||
<span class="s1">'mnist'</span><span class="p">:</span> <span class="s1">'basiccnn'</span><span class="p">,</span>
|
||||
<span class="p">}</span>
|
||||
|
||||
<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="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">'</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'</span>
|
||||
<span class="n">URL</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'https://zenodo.org/records/21131944/files/</span><span class="si">{</span><span class="n">target_file</span><span class="si">}</span><span class="s1">'</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">'image'</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="n">file_path</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">'image'</span><span class="p">,</span> <span class="n">target_file</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">file_path</span><span class="p">)</span>
|
||||
<span class="n">npz_file</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">npz_file</span>
|
||||
|
||||
<span class="n">embedding_dict</span> <span class="o">=</span> <span class="n">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="o">=</span><span class="n">embedding</span><span class="p">)</span>
|
||||
<span class="n">labels_dict</span> <span class="o">=</span> <span class="n">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="o">=</span><span class="s1">'targets'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">embedding_dict</span><span class="p">[</span><span class="s1">'train'</span><span class="p">],</span> <span class="n">labels_dict</span><span class="p">[</span><span class="s1">'train'</span><span class="p">])</span>
|
||||
<span class="n">val</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">embedding_dict</span><span class="p">[</span><span class="s1">'val'</span><span class="p">],</span> <span class="n">labels_dict</span><span class="p">[</span><span class="s1">'val'</span><span class="p">],</span> <span class="n">classes</span><span class="o">=</span><span class="n">train</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">embedding_dict</span><span class="p">[</span><span class="s1">'test'</span><span class="p">],</span> <span class="n">labels_dict</span><span class="p">[</span><span class="s1">'test'</span><span class="p">],</span> <span class="n">classes</span><span class="o">=</span><span class="n">train</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">train</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="n">train</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="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="n">train</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="n">train</span><span class="o">.</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">test</span>
|
||||
|
||||
|
||||
<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="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">-></span> <span class="n">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Loads an image dataset with pre-generated embeddings. Available datasets include:</span>
|
||||
|
||||
<span class="sd"> - 'cifar10', 'cifar100', 'cifar100coarse': see `Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Ontario, 2009. <https://cave.cs.toronto.edu/kriz/learning-features-2009-TR.pdf>`_</span>
|
||||
<span class="sd"> - 'mnist': `Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. The MNIST database of handwritten digits. 1998. <http://yann.lecun.com/exdb/mnist/>`_</span>
|
||||
<span class="sd"> - 'fashionmnist': `Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. <https://arxiv.org/abs/1708.07747>`_</span>
|
||||
<span class="sd"> - 'svhn': `Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, volume 2011, page 4. Granada, 2011. <https://static.googleusercontent.com/media/research.google.com/es//pubs/archive/37648.pdf>`_</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> The image dataset are stored in `Zenodo <https://zenodo.org/records/21131944>`_ and were extracted using `this script <https://github.com/pglez82/visiondatasets_quapy>`_. </span>
|
||||
|
||||
<span class="sd"> These embeddings were generated using a resnet18 or a simple cnn. In all cases, the network was trained using ~60% of the data, validated on ~25% of the data, and the remaining ~15% was used for test. Splits were created with stratification.</span>
|
||||
<span class="sd"> Once the network is trained, it was used with frozen weights to generate embeddings for the training, validation, and test, in different formats (see below).</span>
|
||||
<span class="sd"> It would therefore be convenient to use only heldout data (validation and test) for training and testing quantifiers (this is the default behavior), although the training+validation data can be accessed with `heldout_only=False`.</span>
|
||||
|
||||
<span class="sd"> :param dataset_name: the name of the dataset: valid ones are 'cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist'</span>
|
||||
<span class="sd"> :param embedding: the type of embedding: valid ones are 'features' (next-to-last representations), 'logits' (pre-activation outputs), 'predictions' (post-softmax outputs, or predicted posterior probabilities)</span>
|
||||
<span class="sd"> :param heldout_only: whether to discard the part of the training data used to train the neural model that generated the embeddings (default: True); set to False</span>
|
||||
<span class="sd"> to obtain, as the training data, the original training+validation splits.</span>
|
||||
<span class="sd"> :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default</span>
|
||||
<span class="sd"> ~/quay_data/ directory)</span>
|
||||
<span class="sd"> :return: an instance of :class:`quapy.data.base.Dataset`</span>
|
||||
<span class="sd"> """</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>
|
||||
|
||||
<span class="n">network_train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">_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="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">heldout_only</span><span class="p">:</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">val</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">train</span> <span class="o">=</span> <span class="n">network_train</span> <span class="o">+</span> <span class="n">val</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">dataset_name</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="c1">#train, val, test = _fetch_image_embedding_splits(dataset_name='mnist', embedding='logits')</span>
|
||||
<span class="c1">#print(train)</span>
|
||||
<span class="c1">#print(val)</span>
|
||||
<span class="c1">#print(test)</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s1">'svhn'</span><span class="p">,</span> <span class="n">embedding</span><span class="o">=</span><span class="s1">'features'</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="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for quapy.functional</h1><div class="highlight"><pre>
|
||||
<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>
|
||||
|
|
@ -568,7 +826,7 @@
|
|||
<span class="sd"> :param method: string indicating the search strategy. Possible values are::</span>
|
||||
<span class="sd"> 'optim_minimize': uses scipy.optim</span>
|
||||
<span class="sd"> 'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1]</span>
|
||||
<span class="sd"> 'ternary_search': implements the ternary search (not yet implemented)</span>
|
||||
<span class="sd"> 'ternary_search': carries out a ternary search for binary problems in the interval [0,1]</span>
|
||||
<span class="sd"> :return: np.ndarray, a prevalence vector</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'optim_minimize'</span><span class="p">:</span>
|
||||
|
|
@ -576,7 +834,7 @@
|
|||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'linear_search'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'ternary_search'</span><span class="p">:</span>
|
||||
<span class="n">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
|
|
@ -640,7 +898,32 @@
|
|||
<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="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="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
||||
<span class="sd"> :param loss: (callable) the function to minimize</span>
|
||||
<span class="sd"> :param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector</span>
|
||||
<span class="sd"> :return: (ndarray) the best prevalence vector found</span>
|
||||
<span class="sd"> """</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="s1">'ternary search is only available for binary problems'</span>
|
||||
|
||||
<span class="n">left</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span>
|
||||
<span class="n">tol</span> <span class="o">=</span> <span class="mf">1e-5</span>
|
||||
<span class="k">while</span> <span class="nb">abs</span><span class="p">(</span><span class="n">right</span> <span class="o">-</span> <span class="n">left</span><span class="p">)</span> <span class="o">>=</span> <span class="n">tol</span><span class="p">:</span>
|
||||
<span class="n">left_third</span> <span class="o">=</span> <span class="n">left</span> <span class="o">+</span> <span class="p">(</span><span class="n">right</span> <span class="o">-</span> <span class="n">left</span><span class="p">)</span> <span class="o">/</span> <span class="mi">3</span>
|
||||
<span class="n">right_third</span> <span class="o">=</span> <span class="n">right</span> <span class="o">-</span> <span class="p">(</span><span class="n">right</span> <span class="o">-</span> <span class="n">left</span><span class="p">)</span> <span class="o">/</span> <span class="mi">3</span>
|
||||
|
||||
<span class="n">left_loss</span> <span class="o">=</span> <span class="n">loss</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">left_third</span><span class="p">,</span> <span class="n">left_third</span><span class="p">]))</span>
|
||||
<span class="n">right_loss</span> <span class="o">=</span> <span class="n">loss</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">right_third</span><span class="p">,</span> <span class="n">right_third</span><span class="p">]))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">left_loss</span> <span class="o"><</span> <span class="n">right_loss</span><span class="p">:</span>
|
||||
<span class="n">right</span> <span class="o">=</span> <span class="n">right_third</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
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|
||||
<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="mi">1</span> <span class="o">-</span> <span class="n">prev</span><span class="p">,</span> <span class="n">prev</span><span class="p">])</span></div>
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<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
|
||||
<span></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">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Real</span>
|
||||
|
|
@ -80,6 +338,7 @@
|
|||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KernelDensity</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.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</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">AggregativeSoftQuantifier</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.special</span><span class="w"> </span><span class="kn">import</span> <span class="n">logsumexp</span>
|
||||
|
|
@ -229,7 +488,7 @@
|
|||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which</span>
|
||||
<span class="sd"> the authors show that minimizing the distribution mathing criterion for KLD is akin to performing</span>
|
||||
<span class="sd"> maximum likelihood (ML).</span>
|
||||
|
||||
|
|
@ -333,7 +592,7 @@
|
|||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which</span>
|
||||
<span class="sd"> the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
|
||||
|
||||
<span class="sd"> The distribution matching optimization problem comes down to solving:</span>
|
||||
|
|
@ -444,7 +703,7 @@
|
|||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as</span>
|
||||
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which</span>
|
||||
<span class="sd"> `Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which</span>
|
||||
<span class="sd"> the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
|
||||
|
||||
<span class="sd"> The distribution matching optimization problem comes down to solving:</span>
|
||||
|
|
@ -502,13 +761,11 @@
|
|||
|
||||
<span class="n">P</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="n">n</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">assert</span> <span class="nb">all</span><span class="p">(</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="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> \
|
||||
<span class="s1">'label name gaps not allowed in current implementation'</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</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="p">)</span>
|
||||
|
||||
<span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span>
|
||||
<span class="c1"># (i.e., the weight in its KDE model)</span>
|
||||
<span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">counts_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="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">counts_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="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
|
||||
|
||||
<span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span>
|
||||
<span class="n">tr_tr_sums</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">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
|
|
@ -566,31 +823,75 @@
|
|||
|
||||
</pre></div>
|
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|
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<article class="bd-article">
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<h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
|
||||
<span></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">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>
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Implementation of `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_, a neural network for</span>
|
||||
<span class="sd"> quantification. This implementation uses `PyTorch <https://pytorch.org/>`_ and can take advantage of GPU</span>
|
||||
|
|
@ -100,7 +401,7 @@
|
|||
<span class="sd"> >>> # use samples of 100 elements</span>
|
||||
<span class="sd"> >>> qp.environ['SAMPLE_SIZE'] = 100</span>
|
||||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # load the kindle dataset as text, and convert words to numerical indexes</span>
|
||||
<span class="sd"> >>> # load the Kindle dataset as text, and convert words to numerical indexes</span>
|
||||
<span class="sd"> >>> dataset = qp.datasets.fetch_reviews('kindle', pickle=True)</span>
|
||||
<span class="sd"> >>> qp.train.preprocessing.index(dataset, min_df=5, inplace=True)</span>
|
||||
<span class="sd"> >>></span>
|
||||
|
|
@ -110,12 +411,14 @@
|
|||
<span class="sd"> >>></span>
|
||||
<span class="sd"> >>> # train QuaNet (QuaNet is an alias to QuaNetTrainer)</span>
|
||||
<span class="sd"> >>> model = QuaNet(classifier, qp.environ['SAMPLE_SIZE'], device='cuda')</span>
|
||||
<span class="sd"> >>> model.fit(dataset.training)</span>
|
||||
<span class="sd"> >>> estim_prevalence = model.quantify(dataset.test.instances)</span>
|
||||
<span class="sd"> >>> model.fit(*dataset.training.Xy)</span>
|
||||
<span class="sd"> >>> estim_prevalence = model.predict(dataset.test.instances)</span>
|
||||
|
||||
<span class="sd"> :param classifier: an object implementing `fit` (i.e., that can be trained on labelled data),</span>
|
||||
<span class="sd"> `predict_proba` (i.e., that can generate posterior probabilities of unlabelled examples) and</span>
|
||||
<span class="sd"> `transform` (i.e., that can generate embedded representations of the unlabelled instances).</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param sample_size: integer, the sample size; default is None, meaning that the sample size should be</span>
|
||||
<span class="sd"> taken from qp.environ["SAMPLE_SIZE"]</span>
|
||||
<span class="sd"> :param n_epochs: integer, maximum number of training epochs</span>
|
||||
|
|
@ -135,8 +438,9 @@
|
|||
<span class="sd"> :param device: string, indicate "cpu" or "cuda"</span>
|
||||
<span class="sd"> """</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>
|
||||
<span class="n">n_epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
|
||||
<span class="n">tr_iter_per_poch</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
|
||||
|
|
@ -159,6 +463,7 @@
|
|||
<span class="sa">f</span><span class="s1">'the classifier </span><span class="si">{</span><span class="n">classifier</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"> does not seem to be able to produce posterior probabilities '</span> \
|
||||
<span class="sa">f</span><span class="s1">'since it does not implement the method "predict_proba"'</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">fit_classifier</span> <span class="o">=</span> <span class="n">fit_classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span> <span class="o">=</span> <span class="n">n_epochs</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tr_iter</span> <span class="o">=</span> <span class="n">tr_iter_per_poch</span>
|
||||
|
|
@ -184,20 +489,23 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</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">get_params</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="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">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Trains QuaNet.</span>
|
||||
|
||||
<span class="sd"> :param data: the training data on which to train QuaNet. If `fit_classifier=True`, the data will be split in</span>
|
||||
<span class="sd"> :param X: the training instances on which to train QuaNet. If `fit_classifier=True`, the data will be split in</span>
|
||||
<span class="sd"> 40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If</span>
|
||||
<span class="sd"> `fit_classifier=False`, the data will be split in 66/34 for training QuaNet and validating it, respectively.</span>
|
||||
<span class="sd"> :param fit_classifier: if True, trains the classifier on a split containing 40% of the data</span>
|
||||
<span class="sd"> :param y: the labels of X</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</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">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">fit_classifier</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_classifier</span><span class="p">:</span>
|
||||
<span class="n">classifier_data</span><span class="p">,</span> <span class="n">unused_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
|
||||
<span class="n">train_data</span><span class="p">,</span> <span class="n">valid_data</span> <span class="o">=</span> <span class="n">unused_data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.66</span><span class="p">)</span> <span class="c1"># 0.66 split of 60% makes 40% and 20%</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">classifier_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
|
|
@ -217,13 +525,13 @@
|
|||
<span class="n">train_data_embed</span> <span class="o">=</span> <span class="n">LabelledCollection</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">transform</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">instances</span><span class="p">),</span> <span class="n">train_data</span><span class="o">.</span><span class="n">labels</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="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'cc'</span><span class="p">:</span> <span class="n">CC</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
|
||||
<span class="s1">'acc'</span><span class="p">:</span> <span class="n">ACC</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">valid_data</span><span class="p">),</span>
|
||||
<span class="s1">'pcc'</span><span class="p">:</span> <span class="n">PCC</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
|
||||
<span class="s1">'pacc'</span><span class="p">:</span> <span class="n">PACC</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">valid_data</span><span class="p">),</span>
|
||||
<span class="s1">'cc'</span><span class="p">:</span> <span class="n">CC</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
|
||||
<span class="s1">'acc'</span><span class="p">:</span> <span class="n">ACC</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
|
||||
<span class="s1">'pcc'</span><span class="p">:</span> <span class="n">PCC</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
|
||||
<span class="s1">'pacc'</span><span class="p">:</span> <span class="n">PACC</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
|
||||
<span class="p">}</span>
|
||||
<span class="k">if</span> <span class="n">classifier_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">[</span><span class="s1">'emq'</span><span class="p">]</span> <span class="o">=</span> <span class="n">EMQ</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">classifier_data</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">[</span><span class="s1">'emq'</span><span class="p">]</span> <span class="o">=</span> <span class="n">EMQ</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="n">fit_classifier</span><span class="o">=</span><span class="kc">False</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">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'tr-loss'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
|
||||
|
|
@ -263,7 +571,8 @@
|
|||
|
||||
<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>
|
||||
|
|
@ -274,9 +583,11 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">prevs_estim</span>
|
||||
|
||||
<div class="viewcode-block" id="QuaNetTrainer.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.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="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">instances</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">instances</span><span class="p">)</span>
|
||||
<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="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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
|
||||
|
|
@ -286,7 +597,8 @@
|
|||
<span class="n">prevalence</span> <span class="o">=</span> <span class="n">prevalence</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<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>
|
||||
|
|
@ -336,12 +648,17 @@
|
|||
<span class="sa">f</span><span class="s1">'val-mseloss=</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">"va-loss"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> val-maeloss=</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">"va-mae"</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> '</span>
|
||||
<span class="sa">f</span><span class="s1">'patience=</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">patience</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
<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="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">'classifier__'</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>
|
||||
|
||||
<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>
|
||||
|
||||
<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="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>
|
||||
|
|
@ -352,7 +669,8 @@
|
|||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'unknown parameter '</span><span class="p">,</span> <span class="n">key</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">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>
|
||||
|
|
@ -360,25 +678,34 @@
|
|||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the use of parameters </span><span class="si">{</span><span class="n">intersection</span><span class="si">}</span><span class="s1"> is ambiguous sine those can refer to '</span>
|
||||
<span class="sa">f</span><span class="s1">'the parameters of QuaNet or the learner </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</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="p">)</span>
|
||||
|
||||
<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>
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Removes the checkpoint</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">)</span></div>
|
||||
|
||||
<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>
|
||||
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Removes anything contained in the checkpoint directory</span>
|
||||
<span class="sd"> """</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>
|
||||
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Torch-like wrapper for the Mean Absolute Error</span>
|
||||
|
||||
|
|
@ -389,7 +716,10 @@
|
|||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Implements the `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_ forward pass.</span>
|
||||
<span class="sd"> See :class:`QuaNetTrainer` for training QuaNet.</span>
|
||||
|
|
@ -406,7 +736,7 @@
|
|||
<span class="sd"> :param order_by: integer, class for which the document embeddings are to be sorted</span>
|
||||
<span class="sd"> """</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>
|
||||
|
|
@ -441,10 +771,10 @@
|
|||
<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">'cuda'</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">'cpu'</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>
|
||||
|
|
@ -452,7 +782,9 @@
|
|||
<span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span> <span class="o">=</span> <span class="n">var_hidden</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">var_cell</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span>
|
||||
|
||||
<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>
|
||||
<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="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>
|
||||
|
|
@ -482,7 +814,9 @@
|
|||
<span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</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">prevalence</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">prevalence</span></div></div>
|
||||
<span class="k">return</span> <span class="n">prevalence</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -490,31 +824,75 @@
|
|||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
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</article>
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<h1>Source code for quapy.method.aggregative</h1><div class="highlight"><pre>
|
||||
<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">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">argparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">ArgumentError</span>
|
||||
|
|
@ -100,6 +358,7 @@
|
|||
<span class="n">_rlls_predicted_marginal</span><span class="p">,</span>
|
||||
<span class="n">_rlls_compute_3deltaC</span><span class="p">,</span>
|
||||
<span class="n">_rlls_compute_weights</span><span class="p">,</span>
|
||||
<span class="n">_labels_to_indices</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
|
||||
|
|
@ -1226,6 +1485,10 @@
|
|||
<span class="sd"> class-conditional distributions of the posterior probabilities returned for the positive and negative validation</span>
|
||||
<span class="sd"> examples, respectively. The parameters of the mixture thus represent the estimates of the class prevalence values.</span>
|
||||
|
||||
<span class="sd"> This dedicated class is kept for backward compatibility as the historical</span>
|
||||
<span class="sd"> HDy implementation. The same historical preset is also available as</span>
|
||||
<span class="sd"> :meth:`DMy.HDy`.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
|
||||
|
|
@ -1277,9 +1540,6 @@
|
|||
<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">prev_estimations</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="c1"># for bins in np.linspace(10, 110, 11, dtype=int): #[10, 20, 30, ..., 100, 110]</span>
|
||||
<span class="c1"># Pxy0_density, _ = np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True)</span>
|
||||
<span class="c1"># Pxy1_density, _ = np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True)</span>
|
||||
<span class="k">for</span> <span class="n">bins</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
|
||||
<span class="n">Pxy0_density</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Pxy0_density</span><span class="p">[</span><span class="n">bins</span><span class="p">]</span>
|
||||
<span class="n">Pxy1_density</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Pxy1_density</span><span class="p">[</span><span class="n">bins</span><span class="p">]</span>
|
||||
|
|
@ -1289,13 +1549,12 @@
|
|||
<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="n">prev_selected</span><span class="p">,</span> <span class="n">min_dist</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
|
||||
<span class="k">for</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_linspace</span><span class="p">(</span><span class="n">grid_points</span><span class="o">=</span><span class="mi">101</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mf">0.0</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="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="n">Pxy0_density</span>
|
||||
<span class="n">hdy</span> <span class="o">=</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>
|
||||
<span class="k">if</span> <span class="n">prev_selected</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">hdy</span> <span class="o"><</span> <span class="n">min_dist</span><span class="p">:</span>
|
||||
<span class="n">prev_selected</span><span class="p">,</span> <span class="n">min_dist</span> <span class="o">=</span> <span class="n">prev</span><span class="p">,</span> <span class="n">hdy</span>
|
||||
<span class="n">prev_estimations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prev_selected</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>
|
||||
|
||||
<span class="n">prev_estimations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">linear_search</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="mi">2</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">class1_prev</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_estimations</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">class1_prev</span><span class="p">)</span></div>
|
||||
|
|
@ -1476,6 +1735,10 @@
|
|||
|
||||
<span class="sd"> :param cdf: whether to use CDF instead of PDF (default False)</span>
|
||||
|
||||
<span class="sd"> :param search: string indicating the search strategy used to estimate the prevalence values.</span>
|
||||
<span class="sd"> Valid options are `optim_minimize` (default, works for binary and multiclass problems),</span>
|
||||
<span class="sd"> `linear_search` (binary only), and `ternary_search` (binary only)</span>
|
||||
|
||||
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
|
|
@ -1488,13 +1751,37 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
|
||||
<span class="c1"># @classmethod</span>
|
||||
<span class="c1"># def HDy(cls, classifier, val_split=5, n_jobs=None):</span>
|
||||
<span class="c1"># from quapy.method.meta import MedianEstimator</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># hdy = DMy(classifier=classifier, val_split=val_split, search='linear_search', divergence='HD')</span>
|
||||
<span class="c1"># hdy = AggregativeMedianEstimator(hdy, param_grid={'nbins': np.linspace(10, 110, 11).astype(int)}, n_jobs=n_jobs)</span>
|
||||
<span class="c1"># return hdy</span>
|
||||
<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="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">"""</span>
|
||||
<span class="sd"> Historical HDy preset expressed as a configuration of :class:`DMy`.</span>
|
||||
|
||||
<span class="sd"> This preset reproduces the original HDy setup by using Hellinger</span>
|
||||
<span class="sd"> distance, PDF matching, linear search, and a median sweep over</span>
|
||||
<span class="sd"> `nbins` in `[10, 20, ..., 110]`.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner</span>
|
||||
<span class="sd"> :param val_split: validation specification for generating posteriors</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :return: an instance of :class:`AggregativeMedianEstimator` configured</span>
|
||||
<span class="sd"> to reproduce the historical HDy preset</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">base</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span>
|
||||
<span class="n">classifier</span><span class="o">=</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">nbins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">divergence</span><span class="o">=</span><span class="s1">'HD'</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">'linear_search'</span><span class="p">,</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'nbins'</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>
|
||||
<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="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>
|
||||
|
|
@ -1528,7 +1815,9 @@
|
|||
<span class="sd"> :param labels: array-like with the true labels associated to each posterior</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">posteriors</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</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">classifier</span><span class="o">.</span><span class="n">classes_</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">classifier</span><span class="o">.</span><span class="n">classes_</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>
|
||||
<span class="n">true_labels</span> <span class="o">=</span> <span class="n">_labels_to_indices</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</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="n">func</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_distributions</span><span class="p">,</span>
|
||||
|
|
@ -1965,35 +2254,80 @@
|
|||
<span class="n">SLD</span> <span class="o">=</span> <span class="n">EMQ</span>
|
||||
<span class="n">DistributionMatchingY</span> <span class="o">=</span> <span class="n">DMy</span>
|
||||
<span class="n">HellingerDistanceY</span> <span class="o">=</span> <span class="n">HDy</span>
|
||||
<span class="n">HistoricalHDy</span> <span class="o">=</span> <span class="n">DMy</span><span class="o">.</span><span class="n">HDy</span>
|
||||
<span class="n">MedianSweep</span> <span class="o">=</span> <span class="n">MS</span>
|
||||
<span class="n">MedianSweep2</span> <span class="o">=</span> <span class="n">MS2</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</article>
|
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@ -1,78 +1,336 @@
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<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
|
||||
<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>
|
||||
|
|
@ -85,6 +343,7 @@
|
|||
<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>
|
||||
|
|
@ -145,6 +404,9 @@
|
|||
<span class="sd"> or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger</span>
|
||||
<span class="sd"> Distance)</span>
|
||||
<span class="sd"> :param cdf: whether to use CDF instead of PDF (default False)</span>
|
||||
<span class="sd"> :param search: string indicating the search strategy used to estimate the prevalence values.</span>
|
||||
<span class="sd"> Valid options are `optim_minimize` (default, works for binary and multiclass problems),</span>
|
||||
<span class="sd"> `linear_search` (binary only), and `ternary_search` (binary only)</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
|
|
@ -183,7 +445,6 @@
|
|||
|
||||
|
||||
<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>
|
||||
|
|
@ -214,7 +475,9 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nfeats</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="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</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="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="n">classes</span> <span class="o">=</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="n">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</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="n">classes</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</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="p">[</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">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span>
|
||||
|
|
@ -252,8 +515,6 @@
|
|||
|
||||
|
||||
|
||||
|
||||
|
||||
<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="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>
|
||||
|
|
@ -280,8 +541,8 @@
|
|||
<span class="sd"> ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which</span>
|
||||
<span class="sd"> `Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where</span>
|
||||
<span class="sd"> `K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.</span>
|
||||
<span class="sd"> Of course, this approach is computationally prohibited for large `K`, so the computation is restricted to data</span>
|
||||
<span class="sd"> matrices with `K<=25` (although we recommend even smaller values of `K`). A much faster model is "naive", which</span>
|
||||
<span class="sd"> Of course, this approach is computationally prohibited for large `K`, so the authors advised against computing it</span>
|
||||
<span class="sd"> for matrices with `K>25` (although we recommend even smaller values of `K`). A much faster model is "naive", which</span>
|
||||
<span class="sd"> considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this</span>
|
||||
<span class="sd"> case, `bagging_range` can be set to much larger values. Default is "full" (i.e., original ReadMe behavior).</span>
|
||||
<span class="sd"> :param bootstrap_trials: int, number of bootstrap trials (default 300)</span>
|
||||
|
|
@ -325,7 +586,6 @@
|
|||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</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="n">Xsize</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">0</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># Bootstrap loop</span>
|
||||
|
|
@ -405,14 +665,16 @@
|
|||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'the empirical distribution can only be computed efficiently for dimensions '</span>
|
||||
<span class="sa">f</span><span class="s1">'less or equal than </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># we convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5)</span>
|
||||
<span class="c1"># we first convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5);</span>
|
||||
<span class="c1"># this will speed up subsequent comparisons a lot</span>
|
||||
<span class="n">K</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">binary_powers</span> <span class="o">=</span> <span class="mi">1</span> <span class="o"><<</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">K</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="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
|
||||
<span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span>
|
||||
<span class="n">binary_powers</span> <span class="o">=</span> <span class="mi">1</span> <span class="o"><<</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">K</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="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
|
||||
<span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span> <span class="c1"># e.g., [0 0 1 0 1] @ [16, 8, 4, 2, 1] = 5</span>
|
||||
|
||||
<span class="c1"># count occurrences and compute probs</span>
|
||||
<span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">X_as_binary_numbers</span><span class="p">,</span> <span class="n">minlength</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="n">K</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="n">probs</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">probs</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>
|
||||
|
|
@ -422,8 +684,6 @@
|
|||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
|
@ -437,34 +697,80 @@
|
|||
<span class="c1"># aliases</span>
|
||||
<span class="c1">#---------------------------------------------------------------</span>
|
||||
|
||||
<span class="n">HDx</span> <span class="o">=</span> <span class="n">DMx</span><span class="o">.</span><span class="n">HDx</span>
|
||||
<span class="n">DistributionMatchingX</span> <span class="o">=</span> <span class="n">DMx</span>
|
||||
<span class="n">HellingerDistanceX</span> <span class="o">=</span> <span class="n">HDx</span>
|
||||
</pre></div>
|
||||
|
||||
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|
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@ -1,88 +1,349 @@
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<article class="bd-article">
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<h1>Source code for quapy.plot</h1><div class="highlight"><pre>
|
||||
<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">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.pyplot</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_cmap</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">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">cm</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">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">math</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="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">'figure.figsize'</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>
|
||||
|
|
@ -651,6 +912,126 @@
|
|||
|
||||
|
||||
|
||||
<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="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>
|
||||
<span class="n">density_color</span><span class="o">=</span><span class="s1">'#1f77b4'</span><span class="p">,</span>
|
||||
<span class="n">density_alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
|
||||
<span class="n">resolution</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
|
||||
<span class="n">class_names</span><span class="o">=</span><span class="kc">None</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_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">legend_loc</span><span class="o">=</span><span class="s1">'lower center'</span><span class="p">,</span>
|
||||
<span class="n">legend_bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.08</span><span class="p">),</span>
|
||||
<span class="n">legend_ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
|
||||
<span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">6.8</span><span class="p">,</span> <span class="mf">6.2</span><span class="p">),</span>
|
||||
<span class="n">class_name_fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">title_fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span>
|
||||
<span class="n">legend_fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
|
||||
<span class="n">ax</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">"""</span>
|
||||
<span class="sd"> Plots data on the ternary simplex for three-class quantification problems.</span>
|
||||
|
||||
<span class="sd"> This utility is convenient for visualising prevalence vectors, posterior triplets,</span>
|
||||
<span class="sd"> confidence regions, or any other points that lie on the 2-dimensional probability</span>
|
||||
<span class="sd"> simplex. The plot can combine three optional layer types:</span>
|
||||
|
||||
<span class="sd"> * `point_layers`: scatter layers for one or more prevalence clouds or reference points</span>
|
||||
<span class="sd"> * `region_layers`: shaded regions defined by callables on prevalence vectors</span>
|
||||
<span class="sd"> * `density_function`: a scalar function evaluated on the simplex and rendered as a heatmap</span>
|
||||
|
||||
<span class="sd"> Each entry in `point_layers` is a dictionary with a mandatory `points` field</span>
|
||||
<span class="sd"> containing an array-like of shape `(n_points, 3)` or `(3,)`. Optional fields are</span>
|
||||
<span class="sd"> `label` for the legend and `style` for matplotlib scatter keyword arguments.</span>
|
||||
|
||||
<span class="sd"> Each entry in `region_layers` is a dictionary with a mandatory `fn` field containing</span>
|
||||
<span class="sd"> a callable that receives prevalence vectors and returns region-membership scores.</span>
|
||||
<span class="sd"> Optional fields are `label`, `color`, and `alpha`.</span>
|
||||
|
||||
<span class="sd"> :param point_layers: optional list of point-layer dictionaries</span>
|
||||
<span class="sd"> :param region_layers: optional list of region-layer dictionaries</span>
|
||||
<span class="sd"> :param density_function: optional callable receiving prevalence vectors and returning</span>
|
||||
<span class="sd"> scalar values</span>
|
||||
<span class="sd"> :param density_color: color used for the density heatmap</span>
|
||||
<span class="sd"> :param density_alpha: opacity for the density heatmap</span>
|
||||
<span class="sd"> :param resolution: number of grid steps per axis used for rendering regions and densities</span>
|
||||
<span class="sd"> :param class_names: optional list or tuple with the three class names</span>
|
||||
<span class="sd"> :param title: optional plot title</span>
|
||||
<span class="sd"> :param show_legend: whether to display the legend</span>
|
||||
<span class="sd"> :param legend_loc: location string passed to matplotlib for the legend</span>
|
||||
<span class="sd"> :param legend_bbox_to_anchor: optional legend anchor box</span>
|
||||
<span class="sd"> :param legend_ncol: number of legend columns</span>
|
||||
<span class="sd"> :param figsize: figure size used when `ax` is not provided</span>
|
||||
<span class="sd"> :param class_name_fontsize: fontsize used for simplex vertex labels</span>
|
||||
<span class="sd"> :param title_fontsize: fontsize used for the optional title</span>
|
||||
<span class="sd"> :param legend_fontsize: fontsize used for the legend</span>
|
||||
<span class="sd"> :param ax: optional matplotlib axes object; if not provided, a new figure is created</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot; if not indicated, the plot is shown when</span>
|
||||
<span class="sd"> `ax` is not provided</span>
|
||||
<span class="sd"> :return: returns `(fig, ax)` matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">class_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">class_names</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'Y=1'</span><span class="p">,</span> <span class="s1">'Y=2'</span><span class="p">,</span> <span class="s1">'Y=3'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</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">'expected exactly 3 class names, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">own_figure</span> <span class="o">=</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">own_figure</span><span class="p">:</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">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">figure</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">density_function</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">_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">density_color</span><span class="p">,</span> <span class="n">density_alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">region_layers</span><span class="p">:</span>
|
||||
<span class="n">_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">if</span> <span class="n">point_layers</span><span class="p">:</span>
|
||||
<span class="n">_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="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">triangle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
|
||||
<span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</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="mf">0.5</span><span class="p">,</span> <span class="n">simplex_ymax</span><span class="p">],</span>
|
||||
<span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span>
|
||||
<span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">triangle</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">triangle</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">class_names</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'top'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">class_name_fontsize</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">class_names</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'top'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">class_name_fontsize</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">simplex_ymax</span> <span class="o">+</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">class_names</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'bottom'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">class_name_fontsize</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">title_fontsize</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">'equal'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">simplex_ymax</span> <span class="o">+</span> <span class="mf">0.1</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">show_legend</span><span class="p">:</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">labels</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">legend_loc</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="n">legend_bbox_to_anchor</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="n">legend_ncol</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">legend_fontsize</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">pad</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</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>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">savepath</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">own_figure</span><span class="p">:</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<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">'true'</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">'estim'</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>
|
||||
|
|
@ -703,6 +1084,94 @@
|
|||
<span class="k">return</span> <span class="n">data</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>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'plot_simplex expects prevalence vectors of shape (_, 3); found </span><span class="si">{</span><span class="n">prevalences</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">prevalences</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">prevalences</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">prevalences</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</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="p">)</span>
|
||||
<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="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="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>
|
||||
<span class="n">grid_x</span><span class="p">,</span> <span class="n">grid_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</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="o">=</span> <span class="n">_barycentric_from_xy</span><span class="p">(</span><span class="n">grid_x</span><span class="p">,</span> <span class="n">grid_y</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">pts_bary</span> <span class="o">>=</span> <span class="mi">0</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">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="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>
|
||||
<span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">points</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">return</span> <span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">points</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="k">return</span> <span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
|
||||
<span class="k">pass</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">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="w"> </span><span class="nf">_region_colormap</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'blue'</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="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">'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="n">style</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'s'</span><span class="p">:</span> <span class="mi">25</span><span class="p">,</span> <span class="s1">'alpha'</span><span class="p">:</span> <span class="mf">0.8</span><span class="p">}</span>
|
||||
<span class="n">style</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'style'</span><span class="p">,</span> <span class="p">{}))</span>
|
||||
<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">'label'</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="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>
|
||||
|
||||
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">region_layers</span><span class="p">:</span>
|
||||
<span class="n">mask</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">simplex_mask</span><span class="o">.</span><span class="n">shape</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">values</span> <span class="o">=</span> <span class="n">_evaluate_simplex_function</span><span class="p">(</span><span class="n">layer</span><span class="p">[</span><span class="s1">'fn'</span><span class="p">],</span> <span class="n">valid_points</span><span class="p">)</span>
|
||||
<span class="n">mask</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</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">mask</span><span class="p">,</span>
|
||||
<span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span>
|
||||
<span class="n">cmap</span><span class="o">=</span><span class="n">_region_colormap</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'color'</span><span class="p">,</span> <span class="s1">'blue'</span><span class="p">),</span> <span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'alpha'</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">)),</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'label'</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<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">'color'</span><span class="p">,</span> <span class="s1">'blue'</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">'alpha'</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">'label'</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>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="n">_evaluate_simplex_function</span><span class="p">(</span><span class="n">density_function</span><span class="p">,</span> <span class="n">valid_points</span><span class="p">)</span>
|
||||
<span class="n">min_v</span><span class="p">,</span> <span class="n">max_v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">values</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">max_v</span> <span class="o">></span> <span class="n">min_v</span><span class="p">:</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="p">(</span><span class="n">values</span> <span class="o">-</span> <span class="n">min_v</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">max_v</span> <span class="o">-</span> <span class="n">min_v</span><span class="p">)</span>
|
||||
<span class="n">density</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
|
||||
|
||||
<span class="n">cmap</span> <span class="o">=</span> <span class="n">LinearSegmentedColormap</span><span class="o">.</span><span class="n">from_list</span><span class="p">(</span><span class="s1">'simplex_density'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">color</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</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">density</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
|
||||
<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="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>
|
||||
|
|
@ -742,31 +1211,75 @@
|
|||
<span class="n">calibration_plot</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="o">*</span><span class="n">test</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
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<h1>Source code for quapy.util</h1><div class="highlight"><pre>
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<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">contextlib</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
|
||||
|
|
@ -245,7 +541,7 @@
|
|||
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
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<span class="sd"> Dowloads a function (using :meth:`download_file`) if the file does not exist.</span>
|
||||
<span class="sd"> Downloads a file (using :meth:`download_file`) if the file does not exist.</span>
|
||||
|
||||
<span class="sd"> :param url: the url</span>
|
||||
<span class="sd"> :param archive_filename: destination filename</span>
|
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|
|
@ -479,31 +775,75 @@
|
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|
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</pre></div>
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@ -44,12 +44,15 @@ extensions = [
|
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'sphinx.ext.napoleon',
|
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'sphinx.ext.intersphinx',
|
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'myst_parser',
|
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'sphinx_design',
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]
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autosectionlabel_prefix_document = True
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source_suffix = ['.rst', '.md']
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myst_enable_extensions = ['colon_fence']
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templates_path = ['_templates']
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# List of patterns, relative to source directory, that match files and
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@ -65,7 +68,22 @@ exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
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html_theme = 'pydata_sphinx_theme'
|
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# html_theme = 'furo'
|
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# need to be installed: pip install furo (not working...)
|
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# html_static_path = ['_static']
|
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html_static_path = ['_static']
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html_css_files = ['custom.css']
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html_theme_options = {
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'logo': {
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'image_light': '_static/quapy_logo.png',
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'image_dark': '_static/quapy_logo_dark.png',
|
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},
|
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'icon_links': [
|
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{
|
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'name': 'GitHub',
|
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'url': 'https://github.com/HLT-ISTI/QuaPy',
|
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'icon': 'fa-brands fa-github',
|
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'type': 'fontawesome',
|
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},
|
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],
|
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}
|
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|
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# intersphinx configuration
|
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intersphinx_mapping = {
|
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|
|
|
|||
|
|
@ -1,16 +1,68 @@
|
|||
```{toctree}
|
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:hidden:
|
||||
|
||||
self
|
||||
Home <self>
|
||||
manuals
|
||||
API <quapy>
|
||||
```
|
||||
|
||||
# Quickstart
|
||||
# QuaPy
|
||||
|
||||
QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify) written in Python.
|
||||
```{div} hero-copy
|
||||
QuaPy is an open-source Python framework for quantification, also known as
|
||||
supervised prevalence estimation or learning to quantify. It is designed with
|
||||
research and experimental analysis in mind, and combines datasets, protocols,
|
||||
evaluation measures, visualization tools, and a broad collection of
|
||||
quantification methods in a single coherent workflow.
|
||||
```
|
||||
|
||||
QuaPy is based on the concept of "data sample", and provides implementations of the most important aspects of the quantification workflow, such as (baseline and advanced) quantification methods, quantification-oriented model selection mechanisms, evaluation measures, and evaluations protocols used for evaluating quantification methods. QuaPy also makes available commonly used datasets, and offers visualization tools for facilitating the analysis and interpretation of the experimental results.
|
||||
`````{grid} 1 1 2 2
|
||||
:gutter: 3
|
||||
:class-container: landing-grid
|
||||
|
||||
QuaPy is hosted on GitHub at [https://github.com/HLT-ISTI/QuaPy](https://github.com/HLT-ISTI/QuaPy).
|
||||
````{grid-item-card} Quickstart
|
||||
:class-card: landing-card
|
||||
Install QuaPy and run your first quantifier in a few lines of code.
|
||||
+++
|
||||
```{button-link} #installation
|
||||
:color: primary
|
||||
Get Started
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} Manuals
|
||||
:class-card: landing-card
|
||||
Hands-on guides with methodological context, literature pointers, and reproducible workflows.
|
||||
+++
|
||||
```{button-ref} manuals
|
||||
:ref-type: doc
|
||||
:color: primary
|
||||
Open Manuals
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} API
|
||||
:class-card: landing-card
|
||||
Browse the full reference for `quapy`, including methods, datasets, utilities, and research-oriented extensions.
|
||||
+++
|
||||
```{button-ref} quapy
|
||||
:ref-type: doc
|
||||
:color: primary
|
||||
Browse API
|
||||
```
|
||||
````
|
||||
|
||||
````{grid-item-card} GitHub
|
||||
:class-card: landing-card
|
||||
Explore the source code, open issues, and current development branch activity.
|
||||
+++
|
||||
```{button-link} https://github.com/HLT-ISTI/QuaPy
|
||||
:color: primary
|
||||
Open GitHub
|
||||
```
|
||||
````
|
||||
|
||||
`````
|
||||
|
||||
## Installation
|
||||
|
||||
|
|
@ -18,16 +70,37 @@ QuaPy is hosted on GitHub at [https://github.com/HLT-ISTI/QuaPy](https://github.
|
|||
pip install quapy
|
||||
```
|
||||
|
||||
## Usage
|
||||
## Why QuaPy
|
||||
|
||||
The following script fetches a dataset of tweets, trains, applies, and evaluates a quantifier based on the *Adjusted Classify & Count* quantification method, using, as the evaluation measure, the *Mean Absolute Error* (MAE) between the predicted and the true class prevalence values of the test set:
|
||||
QuaPy is built around the concept of a data sample and supports the main tasks
|
||||
in the quantification workflow: training quantifiers, generating evaluation
|
||||
samples, measuring quantification error, selecting models under distribution
|
||||
shift, and visualizing experimental behaviour. The framework is especially
|
||||
suited for research settings, where one often needs not only implementations,
|
||||
but also methodological context, literature links, and reproducible evaluation
|
||||
procedures.
|
||||
|
||||
Some of the main features are:
|
||||
|
||||
* Implementation of many popular quantification methods, including Classify & Count and its variants,
|
||||
Expectation Maximization, HDy, QuaNet, quantification ensembles, and Bayesian extensions.
|
||||
* Evaluation protocols for generating test samples under prior probability shift.
|
||||
* A broad set of quantification-oriented evaluation metrics.
|
||||
* Ready-to-use textual, numeric, and benchmark competition datasets.
|
||||
* Method documentation that points back to the relevant literature and original papers.
|
||||
* Native support for binary and single-label multiclass quantification.
|
||||
* Visualization tools for analysing predictions, drift, confidence regions, and ternary prevalences.
|
||||
|
||||
## First Example
|
||||
|
||||
The following script fetches a binary dataset, trains an Adjusted Classify & Count quantifier,
|
||||
and evaluates the resulting prevalence prediction with Mean Absolute Error.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
|
||||
|
||||
# create an "Adjusted Classify & Count" quantifier
|
||||
model = qp.method.aggregative.ACC()
|
||||
Xtr, ytr = training.Xy
|
||||
model.fit(Xtr, ytr)
|
||||
|
|
@ -39,43 +112,14 @@ error = qp.error.mae(true_prevalence, estim_prevalence)
|
|||
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
||||
```
|
||||
|
||||
Quantification is useful in scenarios characterized by prior probability shift. In other words, we would be little interested in estimating the class prevalence values of the test set if we could assume the IID assumption to hold, as this prevalence would be roughly equivalent to the class prevalence of the training set. For this reason, any quantification model should be tested across many samples, even ones characterized by class prevalence values different or very different from those found in the training set. QuaPy implements sampling procedures and evaluation protocols that automate this workflow. See the [](./manuals) for detailed examples.
|
||||
|
||||
## Manuals
|
||||
|
||||
The following manuals illustrate several aspects of QuaPy through examples:
|
||||
|
||||
```{toctree}
|
||||
:maxdepth: 3
|
||||
|
||||
manuals
|
||||
```
|
||||
|
||||
```{toctree}
|
||||
:hidden:
|
||||
|
||||
API <quapy>
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
* Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization,
|
||||
quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
|
||||
* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
|
||||
* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
|
||||
* Datasets frequently used in quantification (textual and numeric), including:
|
||||
* 32 UCI Machine Learning datasets.
|
||||
* 11 Twitter quantification-by-sentiment datasets.
|
||||
* 3 product reviews quantification-by-sentiment datasets.
|
||||
* 4 tasks from LeQua 2022 competition and 4 tasks from LeQua 2024 competition
|
||||
* IFCB for Plancton quantification
|
||||
* Native support for binary and single-label multiclass quantification scenarios.
|
||||
* Model selection functionality that minimizes quantification-oriented loss functions.
|
||||
* Visualization tools for analysing the experimental results.
|
||||
Quantification is especially useful when the class prevalence of the test data
|
||||
may differ from that of the training data. QuaPy implements protocols that make
|
||||
it easy to evaluate methods across many such shifts. See the [](./manuals) for
|
||||
worked examples.
|
||||
|
||||
## Citing QuaPy
|
||||
|
||||
If you find QuaPy useful (and we hope you will), please consider citing the original paper in your research.
|
||||
If you find QuaPy useful, please consider citing the original paper.
|
||||
|
||||
```bibtex
|
||||
@inproceedings{moreo2021quapy,
|
||||
|
|
@ -89,7 +133,8 @@ If you find QuaPy useful (and we hope you will), please consider citing the orig
|
|||
|
||||
## Contributing
|
||||
|
||||
In case you want to contribute improvements to quapy, please generate pull request to the "devel" branch.
|
||||
If you want to contribute improvements to QuaPy, please open a pull request
|
||||
against the `devel` branch.
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
|
|
@ -98,6 +143,6 @@ In case you want to contribute improvements to quapy, please generate pull reque
|
|||
:alt: SoBigData++
|
||||
```
|
||||
|
||||
This work has been supported by the QuaDaSh project
|
||||
_"Finanziato dall’Unione europea---Next Generation EU,
|
||||
This work has been supported by the QuaDaSh project
|
||||
_"Finanziato dall'Unione europea---Next Generation EU,
|
||||
Missione 4 Componente 2 CUP B53D23026250001"_.
|
||||
|
|
|
|||
|
|
@ -412,11 +412,112 @@ ECML-PKDD 2024, Vilnius, Lithuania.
|
|||
```
|
||||
|
||||
|
||||
## Image Embedding Datasets
|
||||
|
||||
QuaPy also provides a collection of image datasets in the form of pre-generated
|
||||
embeddings, hosted in [Zenodo](https://zenodo.org/records/21131944).
|
||||
These
|
||||
embeddings were generated using [this extraction scripts](https://github.com/pglez82/visiondatasets_quapy).
|
||||
|
||||
The current public interface is:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
|
||||
data = qp.datasets.fetch_image_embeddings(
|
||||
dataset_name='cifar10',
|
||||
embedding='features',
|
||||
heldout_only=True,
|
||||
)
|
||||
train, test = data.train_test
|
||||
```
|
||||
|
||||
The available datasets are:
|
||||
|
||||
```python
|
||||
qp.datasets.IMAGE_DATASETS
|
||||
# ['cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist']
|
||||
```
|
||||
|
||||
The available embedding types are:
|
||||
|
||||
```python
|
||||
qp.datasets.IMAGE_EMBEDDINGS
|
||||
# ['features', 'logits', 'predictions']
|
||||
```
|
||||
|
||||
where:
|
||||
|
||||
* `features` are the penultimate-layer representations
|
||||
* `logits` are the pre-activation outputs of the neural model
|
||||
* `predictions` are the post-softmax posterior probabilities
|
||||
|
||||
The datasets correspond to frozen neural representations extracted from models
|
||||
trained on image classification tasks. QuaPy downloads them automatically on
|
||||
first use and stores them locally for fast reuse.
|
||||
|
||||
### Train/Test Semantics
|
||||
|
||||
Each dataset is internally organised into three splits: `train`, `val`, and
|
||||
`test`. The `train` split was used to train the neural model that produced the
|
||||
embeddings, while `val` and `test` were not seen during neural training.
|
||||
|
||||
For this reason, the default setting is:
|
||||
|
||||
```python
|
||||
data = qp.datasets.fetch_image_embeddings(..., heldout_only=True)
|
||||
```
|
||||
|
||||
which returns:
|
||||
|
||||
* `train = val`
|
||||
* `test = test`
|
||||
|
||||
This is often the most convenient choice for quantification experiments, since
|
||||
it avoids training quantifiers on examples that were already used to train the
|
||||
embedding model.
|
||||
|
||||
If instead you want to use all the available non-test data, you can set:
|
||||
|
||||
```python
|
||||
data = qp.datasets.fetch_image_embeddings(..., heldout_only=False)
|
||||
```
|
||||
|
||||
In this case, the returned training set is the union of the original neural
|
||||
training split and the validation split.
|
||||
|
||||
### Sources
|
||||
|
||||
The image datasets currently available through `fetch_image_embeddings` are:
|
||||
|
||||
* cifar10, cifar100, cifar100coarse:
|
||||
[Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, 2009.](https://cave.cs.toronto.edu/kriz/learning-features-2009-TR.pdf)
|
||||
* mnist:
|
||||
[Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. The MNIST database of handwritten digits. 1998.](http://yann.lecun.com/exdb/mnist/)
|
||||
* fashionmnist:
|
||||
[Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.](https://arxiv.org/abs/1708.07747)
|
||||
* svhn:
|
||||
[Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y. Ng, et al. Reading digits in natural images with unsupervised feature learning. NIPS Workshop, 2011.](https://static.googleusercontent.com/media/research.google.com/es//pubs/archive/37648.pdf)
|
||||
|
||||
Some statistics are shown in the following table:
|
||||
|
||||
| Dataset | backbone | classes | neural network train size | validation size | test size | feature dim | logit dim | prediction dim | type |
|
||||
|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| cifar100 | resnet18 | 100 | 35000 | 15000 | 10000 | 512 | 100 | 100 | dense |
|
||||
| cifar10 | resnet18 | 10 | 35000 | 15000 | 10000 | 512 | 10 | 10 | dense |
|
||||
| cifar100coarse | resnet18 | 20 | 35000 | 15000 | 10000 | 512 | 20 | 20 | dense |
|
||||
| mnist | basiccnn | 10 |42000 | 18000 | 10000 | 128 | 10 | 10 | dense |
|
||||
| fashionmnist | basiccnn | 10 | 42000 | 18000 | 10000 | 128 | 10 | 10 | dense |
|
||||
| svhn | resnet18 | 10 | 51280 | 21977 | 26032 | 512 | 10 | 10 | dense |
|
||||
|
||||
|
||||
|
||||
|
||||
## IFCB Plankton dataset
|
||||
|
||||
IFCB is a dataset of plankton species in water samples hosted in `Zenodo <https://zenodo.org/records/10036244>`_.
|
||||
This dataset is based on the data available publicly at `WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>`_
|
||||
and in the scripts for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.
|
||||
IFCB is a dataset of plankton species in water samples hosted in [Zenodo](https://zenodo.org/records/10036244).
|
||||
This dataset is based on the data available publicly at [WHOI-Plankton repo](https://github.com/hsosik/WHOI-Plankton)
|
||||
and the scripts for the processing are available at [P. González's repo](https://github.com/pglez82/IFCB_Zenodo).
|
||||
|
||||
This dataset comes with precomputed features for testing quantification algorithms.
|
||||
|
||||
|
|
|
|||
|
|
@ -251,3 +251,37 @@ In those cases, however, it is likely that the variances of each
|
|||
method get higher, to the detriment of the visualization.
|
||||
We recommend to set _show_std=False_ in those cases
|
||||
in order to hide the color bands.
|
||||
|
||||
## Simplex Visualisation
|
||||
|
||||
For three-class problems, prevalence vectors lie on the 2-dimensional probability simplex.
|
||||
The function `qp.plot.plot_simplex` provides a lightweight ternary plot that can combine
|
||||
scatter layers, shaded regions, and density overlays.
|
||||
|
||||
A simplex plot is specified through optional layers. Point layers are dictionaries with
|
||||
fields `points`, `label`, and `style`; region layers are dictionaries with fields `fn`,
|
||||
`label`, `color`, and `alpha`.
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
import quapy as qp
|
||||
|
||||
true_prev = np.array([0.20, 0.35, 0.45])
|
||||
train_prev = np.array([0.50, 0.30, 0.20])
|
||||
posterior_cloud = np.random.default_rng(0).dirichlet(alpha=40 * true_prev, size=200)
|
||||
|
||||
qp.plot.plot_simplex(
|
||||
point_layers=[
|
||||
{'points': posterior_cloud, 'label': 'posterior cloud', 'style': {'s': 12, 'alpha': 0.25, 'color': 'steelblue'}},
|
||||
{'points': true_prev, 'label': 'true prevalence', 'style': {'s': 90, 'color': 'black'}},
|
||||
{'points': train_prev, 'label': 'training prevalence', 'style': {'s': 90, 'color': 'darkorange'}},
|
||||
],
|
||||
density_function=lambda p: np.exp(-40 * np.sum((p - true_prev) ** 2, axis=1)),
|
||||
class_names=['class A', 'class B', 'class C'],
|
||||
savepath='./plots/simplex.png',
|
||||
)
|
||||
```
|
||||
|
||||
See the dedicated
|
||||
[example](https://github.com/HLT-ISTI/QuaPy/blob/master/examples/19.visualizing_simplex.py)
|
||||
for a slightly richer illustration.
|
||||
|
|
|
|||
|
|
@ -119,6 +119,9 @@ LEQUA2024_SAMPLE_SIZE = {
|
|||
'T4': 250,
|
||||
}
|
||||
|
||||
IMAGE_DATASETS=['cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist']
|
||||
IMAGE_EMBEDDINGS=['features', 'logits', 'predictions']
|
||||
|
||||
|
||||
def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset:
|
||||
"""
|
||||
|
|
@ -1062,3 +1065,100 @@ def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=No
|
|||
return train, test_gen
|
||||
else:
|
||||
return train_gen, test_gen
|
||||
|
||||
|
||||
def _fetch_image_embedding_splits(dataset_name, embedding, data_home=None) -> tuple[LabelledCollection,LabelledCollection,LabelledCollection]:
|
||||
"""
|
||||
Loads a pre-generated embedding set (train, val, or test) of an image dataset from `Zenodo <https://zenodo.org/records/21131944>`_.
|
||||
|
||||
Embeddings were extracted using `this script <https://github.com/pglez82/visiondatasets_quapy>`_.
|
||||
|
||||
:param dataset_name: the name of the dataset: valid ones are 'cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist'
|
||||
:param embedding: the type of embedding: valid ones are 'features' (next-to-last representations), 'logits' (pre-activation values), 'predictions' (posterior probabilities)
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:return: a tuple (train, val, test) where each entry is an instance of :class:`quapy.data.base.LabelledCollection`
|
||||
"""
|
||||
assert dataset_name in IMAGE_DATASETS, \
|
||||
f'Name {dataset_name} does not match any known dataset. Valid ones are {IMAGE_DATASETS}'
|
||||
assert embedding in IMAGE_EMBEDDINGS, \
|
||||
f'Name {embedding} does not match any known type of embedding. Valid ones are {IMAGE_EMBEDDINGS}'
|
||||
if data_home is None:
|
||||
data_home = get_quapy_home()
|
||||
|
||||
dataset_network = {
|
||||
'cifar10': 'resnet18',
|
||||
'cifar100': 'resnet18',
|
||||
'cifar100coarse': 'resnet18',
|
||||
'svhn': 'resnet18',
|
||||
'fashionmnist': 'basiccnn',
|
||||
'mnist': 'basiccnn',
|
||||
}
|
||||
|
||||
trained_network = dataset_network[dataset_name]
|
||||
|
||||
def download_embedding_npz(dataset_name, trained_network, embedding):
|
||||
target_file = f'{dataset_name}_{trained_network}_{embedding}.npz'
|
||||
URL = f'https://zenodo.org/records/21131944/files/{target_file}'
|
||||
os.makedirs(join(data_home, 'image'), exist_ok=True)
|
||||
file_path = join(data_home, 'image', target_file)
|
||||
download_file_if_not_exists(URL, file_path)
|
||||
npz_file = np.load(file_path)
|
||||
return npz_file
|
||||
|
||||
embedding_dict = download_embedding_npz(dataset_name, trained_network, embedding=embedding)
|
||||
labels_dict = download_embedding_npz(dataset_name, trained_network, embedding='targets')
|
||||
|
||||
train = LabelledCollection(embedding_dict['train'], labels_dict['train'])
|
||||
val = LabelledCollection(embedding_dict['val'], labels_dict['val'], classes=train.classes)
|
||||
test = LabelledCollection(embedding_dict['test'], labels_dict['test'], classes=train.classes)
|
||||
|
||||
print(f'{len(train)} | {len(val)} | {len(test)} | {train.X.shape[1]} | {train.n_classes} | {train.n_classes}')
|
||||
|
||||
return train, val, test
|
||||
|
||||
|
||||
def fetch_image_embeddings(dataset_name, embedding, heldout_only=True, data_home=None) -> Dataset:
|
||||
"""
|
||||
Loads an image dataset with pre-generated embeddings. Available datasets include:
|
||||
|
||||
- 'cifar10', 'cifar100', 'cifar100coarse': see `Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Ontario, 2009. <https://cave.cs.toronto.edu/kriz/learning-features-2009-TR.pdf>`_
|
||||
- 'mnist': `Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. The MNIST database of handwritten digits. 1998. <http://yann.lecun.com/exdb/mnist/>`_
|
||||
- 'fashionmnist': `Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017. <https://arxiv.org/abs/1708.07747>`_
|
||||
- 'svhn': `Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, volume 2011, page 4. Granada, 2011. <https://static.googleusercontent.com/media/research.google.com/es//pubs/archive/37648.pdf>`_
|
||||
|
||||
The image dataset are stored in `Zenodo <https://zenodo.org/records/21131944>`_ and were extracted using `this script <https://github.com/pglez82/visiondatasets_quapy>`_.
|
||||
|
||||
These embeddings were generated using a resnet18 or a simple cnn. In all cases, the network was trained using ~60% of the data, validated on ~25% of the data, and the remaining ~15% was used for test. Splits were created with stratification.
|
||||
Once the network is trained, it was used with frozen weights to generate embeddings for the training, validation, and test, in different formats (see below).
|
||||
It would therefore be convenient to use only heldout data (validation and test) for training and testing quantifiers (this is the default behavior), although the training+validation data can be accessed with `heldout_only=False`.
|
||||
|
||||
:param dataset_name: the name of the dataset: valid ones are 'cifar10', 'cifar100', 'cifar100coarse', 'svhn', 'fashionmnist', 'mnist'
|
||||
:param embedding: the type of embedding: valid ones are 'features' (next-to-last representations), 'logits' (pre-activation outputs), 'predictions' (post-softmax outputs, or predicted posterior probabilities)
|
||||
:param heldout_only: whether to discard the part of the training data used to train the neural model that generated the embeddings (default: True); set to False
|
||||
to obtain, as the training data, the original training+validation splits.
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:return: an instance of :class:`quapy.data.base.Dataset`
|
||||
"""
|
||||
if data_home is None:
|
||||
data_home = get_quapy_home()
|
||||
|
||||
network_train, val, test = _fetch_image_embedding_splits(dataset_name, embedding, data_home)
|
||||
|
||||
if heldout_only:
|
||||
train = val
|
||||
else:
|
||||
train = network_train + val
|
||||
|
||||
return Dataset(train, test, name=dataset_name)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#train, val, test = _fetch_image_embedding_splits(dataset_name='mnist', embedding='logits')
|
||||
#print(train)
|
||||
#print(val)
|
||||
#print(test)
|
||||
|
||||
dataset = fetch_image_embeddings(dataset_name='svhn', embedding='features', heldout_only=True)
|
||||
print(dataset)
|
||||
|
|
@ -125,7 +125,7 @@ class KDEyML(AggregativeSoftQuantifier, KDEBase):
|
|||
"""
|
||||
Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as
|
||||
the divergence measure to be minimized. This method was first proposed in the paper
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which
|
||||
the authors show that minimizing the distribution mathing criterion for KLD is akin to performing
|
||||
maximum likelihood (ML).
|
||||
|
||||
|
|
@ -220,7 +220,7 @@ class KDEyHD(AggregativeSoftQuantifier, KDEBase):
|
|||
"""
|
||||
Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as
|
||||
the divergence measure to be minimized. This method was first proposed in the paper
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which
|
||||
the authors proposed a Monte Carlo approach for minimizing the divergence.
|
||||
|
||||
The distribution matching optimization problem comes down to solving:
|
||||
|
|
@ -322,7 +322,7 @@ class KDEyCS(AggregativeSoftQuantifier):
|
|||
"""
|
||||
Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as
|
||||
the divergence measure to be minimized. This method was first proposed in the paper
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://arxiv.org/abs/2401.00490>`_, in which
|
||||
`Kernel Density Estimation for Multiclass Quantification <https://link.springer.com/article/10.1007/s10994-024-06726-5>`_ (`arXiv <https://arxiv.org/abs/2401.00490>`_), in which
|
||||
the authors proposed a Monte Carlo approach for minimizing the divergence.
|
||||
|
||||
The distribution matching optimization problem comes down to solving:
|
||||
|
|
|
|||
|
|
@ -1038,6 +1038,10 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
|||
class-conditional distributions of the posterior probabilities returned for the positive and negative validation
|
||||
examples, respectively. The parameters of the mixture thus represent the estimates of the class prevalence values.
|
||||
|
||||
This dedicated class is kept for backward compatibility as the historical
|
||||
HDy implementation. The same historical preset is also available as
|
||||
:meth:`DMy.HDy`.
|
||||
|
||||
:param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be
|
||||
the one indicated in `qp.environ['DEFAULT_CLS']`
|
||||
|
||||
|
|
@ -1273,13 +1277,34 @@ class DMy(AggregativeSoftQuantifier):
|
|||
self.search = search
|
||||
self.n_jobs = n_jobs
|
||||
|
||||
# @classmethod
|
||||
# def HDy(cls, classifier, val_split=5, n_jobs=None):
|
||||
# from quapy.method.meta import MedianEstimator
|
||||
#
|
||||
# hdy = DMy(classifier=classifier, val_split=val_split, search='linear_search', divergence='HD')
|
||||
# hdy = AggregativeMedianEstimator(hdy, param_grid={'nbins': np.linspace(10, 110, 11).astype(int)}, n_jobs=n_jobs)
|
||||
# return hdy
|
||||
@classmethod
|
||||
def HDy(cls, classifier: BaseEstimator = None, fit_classifier=True, val_split=5, n_jobs=None):
|
||||
"""
|
||||
Historical HDy preset expressed as a configuration of :class:`DMy`.
|
||||
|
||||
This preset reproduces the original HDy setup by using Hellinger
|
||||
distance, PDF matching, linear search, and a median sweep over
|
||||
`nbins` in `[10, 20, ..., 110]`.
|
||||
|
||||
:param classifier: a scikit-learn's BaseEstimator, or None
|
||||
:param fit_classifier: whether to train the learner
|
||||
:param val_split: validation specification for generating posteriors
|
||||
:param n_jobs: number of parallel workers
|
||||
:return: an instance of :class:`AggregativeMedianEstimator` configured
|
||||
to reproduce the historical HDy preset
|
||||
"""
|
||||
base = cls(
|
||||
classifier=classifier,
|
||||
fit_classifier=fit_classifier,
|
||||
val_split=val_split,
|
||||
nbins=10,
|
||||
divergence='HD',
|
||||
cdf=False,
|
||||
search='linear_search',
|
||||
n_jobs=n_jobs,
|
||||
)
|
||||
param_grid = {'nbins': np.linspace(10, 110, 11, dtype=int)}
|
||||
return AggregativeMedianEstimator(base_quantifier=base, param_grid=param_grid, n_jobs=n_jobs)
|
||||
|
||||
def _get_distributions(self, posteriors):
|
||||
histograms = []
|
||||
|
|
@ -1703,5 +1728,6 @@ ExpectationMaximizationQuantifier = EMQ
|
|||
SLD = EMQ
|
||||
DistributionMatchingY = DMy
|
||||
HellingerDistanceY = HDy
|
||||
HistoricalHDy = DMy.HDy
|
||||
MedianSweep = MS
|
||||
MedianSweep2 = MS2
|
||||
|
|
|
|||
|
|
@ -97,7 +97,6 @@ class DMx(BaseQuantifier):
|
|||
return hdx
|
||||
|
||||
def __get_distributions(self, X):
|
||||
|
||||
histograms = []
|
||||
for feat_idx in range(self.nfeats):
|
||||
feature = X[:, feat_idx]
|
||||
|
|
@ -161,8 +160,6 @@ class DMx(BaseQuantifier):
|
|||
return F.argmin_prevalence(loss, n_classes, method=self.search)
|
||||
|
||||
|
||||
|
||||
|
||||
class ReadMe(BaseQuantifier, WithConfidenceABC):
|
||||
"""
|
||||
ReadMe is a non-aggregative quantification system proposed by
|
||||
|
|
@ -187,8 +184,8 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
|
|||
ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which
|
||||
`Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where
|
||||
`K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.
|
||||
Of course, this approach is computationally prohibited for large `K`, so the computation is restricted to data
|
||||
matrices with `K<=25` (although we recommend even smaller values of `K`). A much faster model is "naive", which
|
||||
Of course, this approach is computationally prohibited for large `K`, so the authors advised against computing it
|
||||
for matrices with `K>25` (although we recommend even smaller values of `K`). A much faster model is "naive", which
|
||||
considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this
|
||||
case, `bagging_range` can be set to much larger values. Default is "full" (i.e., original ReadMe behavior).
|
||||
:param bootstrap_trials: int, number of bootstrap trials (default 300)
|
||||
|
|
@ -230,7 +227,6 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
|
|||
self.rng = np.random.default_rng(self.random_state)
|
||||
self.classes_ = np.unique(y)
|
||||
|
||||
|
||||
Xsize = X.shape[0]
|
||||
|
||||
# Bootstrap loop
|
||||
|
|
@ -303,14 +299,16 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
|
|||
raise ValueError(f'the empirical distribution can only be computed efficiently for dimensions '
|
||||
f'less or equal than {self.MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION}')
|
||||
|
||||
# we convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5)
|
||||
# we first convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5);
|
||||
# this will speed up subsequent comparisons a lot
|
||||
K = X.shape[1]
|
||||
binary_powers = 1 << np.arange(K-1, -1, -1) # (2^K, ..., 32, 16, 8, 4, 2, 1)
|
||||
X_as_binary_numbers = X @ binary_powers
|
||||
binary_powers = 1 << np.arange(K-1, -1, -1) # (2^K, ..., 32, 16, 8, 4, 2, 1)
|
||||
X_as_binary_numbers = X @ binary_powers # e.g., [0 0 1 0 1] @ [16, 8, 4, 2, 1] = 5
|
||||
|
||||
# count occurrences and compute probs
|
||||
counts = np.bincount(X_as_binary_numbers, minlength=2 ** K).astype(float)
|
||||
probs = counts / counts.sum()
|
||||
|
||||
return probs
|
||||
|
||||
def _multinomial_distribution(self, X):
|
||||
|
|
@ -319,8 +317,6 @@ class ReadMe(BaseQuantifier, WithConfidenceABC):
|
|||
return PX.ravel()
|
||||
|
||||
|
||||
|
||||
|
||||
def _get_features_range(X):
|
||||
feat_ranges = []
|
||||
ncols = X.shape[1]
|
||||
|
|
@ -334,4 +330,6 @@ def _get_features_range(X):
|
|||
# aliases
|
||||
#---------------------------------------------------------------
|
||||
|
||||
DistributionMatchingX = DMx
|
||||
HDx = DMx.HDx
|
||||
DistributionMatchingX = DMx
|
||||
HellingerDistanceX = HDx
|
||||
220
quapy/plot.py
220
quapy/plot.py
|
|
@ -1,12 +1,15 @@
|
|||
from collections import defaultdict
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.pyplot import get_cmap
|
||||
import numpy as np
|
||||
from matplotlib import cm
|
||||
from scipy.stats import ttest_ind_from_stats
|
||||
from matplotlib.ticker import ScalarFormatter
|
||||
import math
|
||||
|
||||
from matplotlib import cm
|
||||
import matplotlib.colors as mcolors
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
|
||||
from matplotlib.pyplot import get_cmap
|
||||
from matplotlib.ticker import ScalarFormatter
|
||||
import numpy as np
|
||||
from scipy.stats import ttest_ind_from_stats
|
||||
|
||||
import quapy as qp
|
||||
|
||||
plt.rcParams['figure.figsize'] = [10, 6]
|
||||
|
|
@ -560,6 +563,123 @@ def brokenbar_supremacy_by_drift(method_names, true_prevs, estim_prevs, tr_prevs
|
|||
return fig, ax
|
||||
|
||||
|
||||
def plot_simplex(
|
||||
point_layers=None,
|
||||
region_layers=None,
|
||||
density_function=None,
|
||||
density_color='#1f77b4',
|
||||
density_alpha=1.0,
|
||||
resolution=400,
|
||||
class_names=None,
|
||||
title=None,
|
||||
show_legend=True,
|
||||
legend_loc='lower center',
|
||||
legend_bbox_to_anchor=(0.5, -0.08),
|
||||
legend_ncol=2,
|
||||
figsize=(6.8, 6.2),
|
||||
class_name_fontsize=10,
|
||||
title_fontsize=11,
|
||||
legend_fontsize=9,
|
||||
ax=None,
|
||||
savepath=None):
|
||||
"""
|
||||
Plots data on the ternary simplex for three-class quantification problems.
|
||||
|
||||
This utility is convenient for visualising prevalence vectors, posterior triplets,
|
||||
confidence regions, or any other points that lie on the 2-dimensional probability
|
||||
simplex. The plot can combine three optional layer types:
|
||||
|
||||
* `point_layers`: scatter layers for one or more prevalence clouds or reference points
|
||||
* `region_layers`: shaded regions defined by callables on prevalence vectors
|
||||
* `density_function`: a scalar function evaluated on the simplex and rendered as a heatmap
|
||||
|
||||
Each entry in `point_layers` is a dictionary with a mandatory `points` field
|
||||
containing an array-like of shape `(n_points, 3)` or `(3,)`. Optional fields are
|
||||
`label` for the legend and `style` for matplotlib scatter keyword arguments.
|
||||
|
||||
Each entry in `region_layers` is a dictionary with a mandatory `fn` field containing
|
||||
a callable that receives prevalence vectors and returns region-membership scores.
|
||||
Optional fields are `label`, `color`, and `alpha`.
|
||||
|
||||
:param point_layers: optional list of point-layer dictionaries
|
||||
:param region_layers: optional list of region-layer dictionaries
|
||||
:param density_function: optional callable receiving prevalence vectors and returning
|
||||
scalar values
|
||||
:param density_color: color used for the density heatmap
|
||||
:param density_alpha: opacity for the density heatmap
|
||||
:param resolution: number of grid steps per axis used for rendering regions and densities
|
||||
:param class_names: optional list or tuple with the three class names
|
||||
:param title: optional plot title
|
||||
:param show_legend: whether to display the legend
|
||||
:param legend_loc: location string passed to matplotlib for the legend
|
||||
:param legend_bbox_to_anchor: optional legend anchor box
|
||||
:param legend_ncol: number of legend columns
|
||||
:param figsize: figure size used when `ax` is not provided
|
||||
:param class_name_fontsize: fontsize used for simplex vertex labels
|
||||
:param title_fontsize: fontsize used for the optional title
|
||||
:param legend_fontsize: fontsize used for the legend
|
||||
:param ax: optional matplotlib axes object; if not provided, a new figure is created
|
||||
:param savepath: path where to save the plot; if not indicated, the plot is shown when
|
||||
`ax` is not provided
|
||||
:return: returns `(fig, ax)` matplotlib objects for eventual customisation
|
||||
"""
|
||||
if class_names is None:
|
||||
class_names = ('Y=1', 'Y=2', 'Y=3')
|
||||
if len(class_names) != 3:
|
||||
raise ValueError(f'expected exactly 3 class names, got {len(class_names)}')
|
||||
|
||||
own_figure = ax is None
|
||||
if own_figure:
|
||||
fig, ax = plt.subplots(figsize=figsize)
|
||||
else:
|
||||
fig = ax.figure
|
||||
|
||||
if density_function is not None:
|
||||
_plot_simplex_density(ax, density_function, resolution, density_color, density_alpha)
|
||||
|
||||
if region_layers:
|
||||
_plot_simplex_regions(ax, region_layers, resolution)
|
||||
|
||||
if point_layers:
|
||||
_plot_simplex_points(ax, point_layers)
|
||||
|
||||
simplex_ymax = np.sqrt(3) / 2
|
||||
triangle = np.array([
|
||||
[0.0, 0.0],
|
||||
[1.0, 0.0],
|
||||
[0.5, simplex_ymax],
|
||||
[0.0, 0.0],
|
||||
])
|
||||
ax.plot(triangle[:, 0], triangle[:, 1], color='black')
|
||||
|
||||
ax.text(-0.05, -0.05, class_names[0], ha='right', va='top', fontsize=class_name_fontsize)
|
||||
ax.text(1.05, -0.05, class_names[1], ha='left', va='top', fontsize=class_name_fontsize)
|
||||
ax.text(0.5, simplex_ymax + 0.05, class_names[2], ha='center', va='bottom', fontsize=class_name_fontsize)
|
||||
|
||||
if title is not None:
|
||||
ax.set_title(title, fontsize=title_fontsize)
|
||||
|
||||
ax.set_aspect('equal')
|
||||
ax.set_xlim(-0.1, 1.1)
|
||||
ax.set_ylim(-0.1, simplex_ymax + 0.1)
|
||||
ax.axis('off')
|
||||
|
||||
if show_legend:
|
||||
_, labels = ax.get_legend_handles_labels()
|
||||
if labels:
|
||||
ax.legend(loc=legend_loc, bbox_to_anchor=legend_bbox_to_anchor, ncol=legend_ncol, fontsize=legend_fontsize, frameon=False)
|
||||
|
||||
fig.tight_layout(pad=0.6)
|
||||
|
||||
if savepath is not None:
|
||||
qp.util.create_parent_dir(savepath)
|
||||
fig.savefig(savepath, bbox_inches='tight')
|
||||
elif own_figure:
|
||||
plt.show()
|
||||
|
||||
return fig, ax
|
||||
|
||||
|
||||
def _merge(method_names, true_prevs, estim_prevs):
|
||||
ndims = true_prevs[0].shape[1]
|
||||
data = defaultdict(lambda: {'true': np.empty(shape=(0, ndims)), 'estim': np.empty(shape=(0, ndims))})
|
||||
|
|
@ -612,6 +732,94 @@ def _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error
|
|||
return data
|
||||
|
||||
|
||||
def _simplex_to_cartesian(prevalences):
|
||||
prevalences = np.asarray(prevalences, dtype=float)
|
||||
prevalences = np.atleast_2d(prevalences)
|
||||
if prevalences.shape[1] != 3:
|
||||
raise ValueError(f'plot_simplex expects prevalence vectors of shape (_, 3); found {prevalences.shape}')
|
||||
x = prevalences[:, 1] + 0.5 * prevalences[:, 2]
|
||||
y = prevalences[:, 2] * (np.sqrt(3) / 2)
|
||||
return x, y
|
||||
|
||||
|
||||
def _barycentric_from_xy(x, y):
|
||||
p3 = 2 * y / np.sqrt(3)
|
||||
p2 = x - 0.5 * p3
|
||||
p1 = 1 - p2 - p3
|
||||
return np.stack([p1, p2, p3], axis=-1)
|
||||
|
||||
|
||||
def _simplex_mesh(resolution):
|
||||
simplex_ymax = np.sqrt(3) / 2
|
||||
xs = np.linspace(0, 1, resolution)
|
||||
ys = np.linspace(0, simplex_ymax, resolution)
|
||||
grid_x, grid_y = np.meshgrid(xs, ys)
|
||||
pts_bary = _barycentric_from_xy(grid_x, grid_y)
|
||||
mask = np.all(pts_bary >= 0, axis=-1)
|
||||
return xs, ys, pts_bary, mask
|
||||
|
||||
|
||||
def _evaluate_simplex_function(function, points):
|
||||
points = np.asarray(points, dtype=float)
|
||||
try:
|
||||
values = np.asarray(function(points), dtype=float)
|
||||
if values.shape == (points.shape[0],):
|
||||
return values
|
||||
if values.shape == points.shape[:-1]:
|
||||
return values.reshape(-1)
|
||||
except Exception:
|
||||
pass
|
||||
return np.asarray([function(point) for point in points], dtype=float)
|
||||
|
||||
|
||||
def _region_colormap(color='blue', alpha=0.35):
|
||||
return ListedColormap([
|
||||
(1.0, 1.0, 1.0, 0.0),
|
||||
(*mcolors.to_rgb(color), alpha),
|
||||
])
|
||||
|
||||
|
||||
def _plot_simplex_points(ax, point_layers):
|
||||
for layer in point_layers:
|
||||
points = np.asarray(layer['points'], dtype=float)
|
||||
style = {'s': 25, 'alpha': 0.8}
|
||||
style.update(layer.get('style', {}))
|
||||
ax.scatter(*_simplex_to_cartesian(points), label=layer.get('label'), **style)
|
||||
|
||||
|
||||
def _plot_simplex_regions(ax, region_layers, resolution):
|
||||
xs, ys, pts_bary, simplex_mask = _simplex_mesh(resolution)
|
||||
valid_points = pts_bary[simplex_mask]
|
||||
|
||||
for layer in region_layers:
|
||||
mask = np.zeros(simplex_mask.shape, dtype=float)
|
||||
values = _evaluate_simplex_function(layer['fn'], valid_points)
|
||||
mask[simplex_mask] = values
|
||||
ax.pcolormesh(
|
||||
xs,
|
||||
ys,
|
||||
mask,
|
||||
shading='auto',
|
||||
cmap=_region_colormap(layer.get('color', 'blue'), layer.get('alpha', 0.35)),
|
||||
)
|
||||
if layer.get('label') is not None:
|
||||
ax.scatter([], [], color=layer.get('color', 'blue'), alpha=layer.get('alpha', 0.35), label=layer['label'])
|
||||
|
||||
|
||||
def _plot_simplex_density(ax, density_function, resolution, color, alpha):
|
||||
xs, ys, pts_bary, simplex_mask = _simplex_mesh(resolution)
|
||||
valid_points = pts_bary[simplex_mask]
|
||||
density = np.full(simplex_mask.shape, np.nan, dtype=float)
|
||||
values = _evaluate_simplex_function(density_function, valid_points)
|
||||
min_v, max_v = np.min(values), np.max(values)
|
||||
if max_v > min_v:
|
||||
values = (values - min_v) / (max_v - min_v)
|
||||
density[simplex_mask] = values
|
||||
|
||||
cmap = LinearSegmentedColormap.from_list('simplex_density', ['white', color])
|
||||
ax.pcolormesh(xs, ys, density, shading='auto', cmap=cmap, alpha=alpha)
|
||||
|
||||
|
||||
def calibration_plot(prob_classifier, X, y, nbins=10, savepath=None):
|
||||
posteriors = prob_classifier.predict_proba(X)
|
||||
assert posteriors.ndim==2, 'calibration plot only works for binary problems'
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ import numpy as np
|
|||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS, NON_AGGREGATIVE_METHODS
|
||||
from quapy.method.non_aggregative import DMx
|
||||
from quapy.method.non_aggregative import DMx, HDx
|
||||
from quapy.method.aggregative import ACC, DMy, KDEyCS, RLLS
|
||||
from quapy.method.meta import Ensemble
|
||||
from quapy.functional import check_prevalence_vector
|
||||
|
|
@ -175,5 +175,19 @@ class TestMethods(unittest.TestCase):
|
|||
self.assertEqual(len(estim_prevalences), len(np.unique(y_train)))
|
||||
|
||||
|
||||
def test_historical_distribution_matching_presets(self):
|
||||
dataset = TestMethods.tiny_dataset_binary
|
||||
|
||||
hdy = DMy.HDy(LogisticRegression(max_iter=2000), val_split=3)
|
||||
hdy.fit(*dataset.training.Xy)
|
||||
prev_hdy = hdy.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(prev_hdy))
|
||||
|
||||
hdx = HDx()
|
||||
hdx.fit(*dataset.training.Xy)
|
||||
prev_hdx = hdx.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(prev_hdx))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
|
|||
|
|
@ -152,7 +152,7 @@ def download_file(url, archive_filename):
|
|||
|
||||
def download_file_if_not_exists(url, archive_filename):
|
||||
"""
|
||||
Dowloads a function (using :meth:`download_file`) if the file does not exist.
|
||||
Downloads a file (using :meth:`download_file`) if the file does not exist.
|
||||
|
||||
:param url: the url
|
||||
:param archive_filename: destination filename
|
||||
|
|
|
|||
Loading…
Reference in New Issue