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<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">TruncatedSVD</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<div class="viewcode-block" id="LowRankLogisticRegression">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a>
<span class="k">class</span> <span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)</span>
<span class="sd"> that also generates embedded inputs (i.e., that implements `transform`), as those required for</span>
<span class="sd"> :class:`quapy.method.neural.QuaNet`. This is a mock method to allow for easily instantiating</span>
<span class="sd"> :class:`quapy.method.neural.QuaNet` on array-like real-valued instances.</span>
<span class="sd"> The transformation consists of applying :class:`sklearn.decomposition.TruncatedSVD`</span>
<span class="sd"> while classification is performed using :class:`sklearn.linear_model.LogisticRegression` on the low-rank space.</span>
<span class="sd"> :param n_components: the number of principal components to retain</span>
<span class="sd"> :param kwargs: parameters for the</span>
<span class="sd"> `Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ classifier</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">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="LowRankLogisticRegression.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="w"> </span><span class="sd">"""</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>
<span class="sd"> """</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_components'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">}</span>
<span class="n">params</span><span class="o">.</span><span class="n">update</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="k">return</span> <span class="n">params</span></div>
<div class="viewcode-block" id="LowRankLogisticRegression.set_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="w"> </span><span class="sd">"""</span>
<span class="sd"> Set the parameters of this estimator.</span>
<span class="sd"> :param parameters: a `**kwargs` dictionary with the estimator parameters for</span>
<span class="sd"> `Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__</span>
<span class="sd"> and eventually also `n_components` for `TruncatedSVD`</span>
<span class="sd"> """</span>
<span class="n">params_</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'n_components'</span> <span class="ow">in</span> <span class="n">params_</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">params_</span><span class="p">[</span><span class="s1">'n_components'</span><span class="p">]</span>
<span class="k">del</span> <span class="n">params_</span><span class="p">[</span><span class="s1">'n_components'</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">params_</span><span class="p">)</span></div>
<div class="viewcode-block" id="LowRankLogisticRegression.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Fit the model according to the given training data. The fit consists of</span>
<span class="sd"> fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples, n_classes)` with the class labels</span>
<span class="sd"> :return: `self`</span>
<span class="sd"> """</span>
<span class="n">nF</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">pca</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">nF</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="o">=</span> <span class="n">TruncatedSVD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</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="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="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="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="LowRankLogisticRegression.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Predicts labels for the instances `X` embedded into the low-rank space.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
<span class="sd"> :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
<span class="sd"> instances in `X`</span>
<span class="sd"> """</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="w"> </span><span class="sd">"""</span>
<span class="sd"> Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities</span>
<span class="sd"> """</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</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="k">return</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></div>
<div class="viewcode-block" id="LowRankLogisticRegression.transform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if</span>
<span class="sd"> `n_components` >= `X.shape[1]`.</span>
<span class="sd"> </span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to embed</span>
<span class="sd"> :return: array-like of shape `(n_samples, n_components)` with the embedded instances</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">X</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
</div>
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