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<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
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<span></span><span class="kn">import</span> <span class="nn">random</span>
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<span class="kn">import</span> <span class="nn">shutil</span>
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<span class="kn">import</span> <span class="nn">subprocess</span>
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<span class="kn">import</span> <span class="nn">tempfile</span>
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<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
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<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
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<span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
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<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">dump_svmlight_file</span>
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<div class="viewcode-block" id="SVMperf">
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<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
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<span class="k">class</span> <span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">"""A wrapper for the `SVM-perf package <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`__ by Thorsten Joachims.</span>
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<span class="sd"> When using losses for quantification, the source code has to be patched. See</span>
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<span class="sd"> the `installation documentation <https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses>`__</span>
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<span class="sd"> for further details.</span>
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<span class="sd"> References:</span>
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<span class="sd"> * `Esuli et al.2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`__</span>
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<span class="sd"> * `Barranquero et al.2015 <https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X>`__</span>
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<span class="sd"> :param svmperf_base: path to directory containing the binary files `svm_perf_learn` and `svm_perf_classify`</span>
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<span class="sd"> :param C: trade-off between training error and margin (default 0.01)</span>
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<span class="sd"> :param verbose: set to True to print svm-perf std outputs</span>
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<span class="sd"> :param loss: the loss to optimize for. Available losses are "01", "f1", "kld", "nkld", "q", "qacc", "qf1", "qgm", "mae", "mrae".</span>
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<span class="sd"> :param host_folder: directory where to store the trained model; set to None (default) for using a tmp directory</span>
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<span class="sd"> (temporal directories are automatically deleted)</span>
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<span class="sd"> """</span>
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<span class="c1"># losses with their respective codes in svm_perf implementation</span>
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<span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'01'</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'kld'</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">'nkld'</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">'q'</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">'qacc'</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">'qf1'</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">'qgm'</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">'mae'</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">'mrae'</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'01'</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path'</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span>
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<span class="c1"># def set_params(self, **parameters):</span>
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<span class="c1"># """</span>
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<span class="c1"># Set the hyper-parameters for svm-perf. Currently, only the `C` and `loss` parameters are supported</span>
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<span class="c1">#</span>
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<span class="c1"># :param parameters: a `**kwargs` dictionary `{'C': <float>}`</span>
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<span class="c1"># """</span>
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<span class="c1"># assert sorted(list(parameters.keys())) == ['C', 'loss'], \</span>
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<span class="c1"># 'currently, only the C and loss parameters are supported'</span>
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<span class="c1"># self.C = parameters.get('C', self.C)</span>
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<span class="c1"># self.loss = parameters.get('loss', self.loss)</span>
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<span class="c1">#</span>
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<span class="c1"># def get_params(self, deep=True):</span>
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<span class="c1"># return {'C': self.C, 'loss': self.loss}</span>
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<div class="viewcode-block" id="SVMperf.fit">
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<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a>
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<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>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Trains the SVM for the multivariate performance loss</span>
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<span class="sd"> :param X: training instances</span>
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<span class="sd"> :param y: a binary vector of labels</span>
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<span class="sd"> :return: `self`</span>
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<span class="sd"> """</span>
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<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="ow">in</span> <span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">,</span> \
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<span class="sa">f</span><span class="s1">'unsupported loss </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="si">}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s1">'</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">'svm_perf_learn'</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">'svm_perf_classify'</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span> <span class="o">=</span> <span class="s1">'-w 3 -l '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">])</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span> <span class="o">=</span> <span class="s1">'-c '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">n_classes_</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
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<span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
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<span class="c1"># this would allow to run parallel instances of predict</span>
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<span class="n">random_code</span> <span class="o">=</span> <span class="s1">'svmperfprocess'</span><span class="o">+</span><span class="s1">'-'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
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<span class="c1"># tmp dir are removed after the fit terminates in multiprocessing...</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">(</span><span class="n">suffix</span><span class="o">=</span><span class="n">random_code</span><span class="p">)</span><span class="o">.</span><span class="n">name</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span><span class="p">,</span> <span class="s1">'.'</span> <span class="o">+</span> <span class="n">random_code</span><span class="p">)</span>
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<span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'model-'</span><span class="o">+</span><span class="n">random_code</span><span class="p">)</span>
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<span class="n">traindat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'train-</span><span class="si">{</span><span class="n">random_code</span><span class="si">}</span><span class="s1">.dat'</span><span class="p">)</span>
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<span class="n">dump_svmlight_file</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">traindat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="n">cmd</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span><span class="p">,</span> <span class="n">traindat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">])</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'[Running]'</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
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<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">):</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
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<span class="n">remove</span><span class="p">(</span><span class="n">traindat</span><span class="p">)</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
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<span class="k">return</span> <span class="bp">self</span></div>
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<div class="viewcode-block" id="SVMperf.predict">
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<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a>
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<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>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Predicts labels for the instances `X`</span>
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<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
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<span class="sd"> :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
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<span class="sd"> instances in `X`</span>
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<span class="sd"> """</span>
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<span class="n">confidence_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">confidence_scores</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1</span>
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<span class="k">return</span> <span class="n">predictions</span></div>
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<div class="viewcode-block" id="SVMperf.decision_function">
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<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
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<span class="k">def</span> <span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Evaluate the decision function for the samples in `X`.</span>
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<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` containing the instances to classify</span>
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<span class="sd"> :param y: unused</span>
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<span class="sd"> :return: array-like of shape `(n_samples,)` containing the decision scores of the instances</span>
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<span class="sd"> """</span>
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<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'tmpdir'</span><span class="p">),</span> <span class="s1">'predict called before fit'</span>
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<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">'model directory corrupted'</span>
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<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">),</span> <span class="s1">'model not found'</span>
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<span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
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<span class="n">y</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">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<span class="c1"># in order to allow for parallel runs of predict, a random code is assigned</span>
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<span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
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<span class="n">random_code</span> <span class="o">=</span> <span class="s1">'-'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
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<span class="n">predictions_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'predictions'</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">'.dat'</span><span class="p">)</span>
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<span class="n">testdat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">'test'</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">'.dat'</span><span class="p">)</span>
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<span class="n">dump_svmlight_file</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">testdat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="n">cmd</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span><span class="p">,</span> <span class="n">testdat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions_path</span><span class="p">])</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'[Running]'</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
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<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf-8'</span><span class="p">))</span>
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|
<span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
|
|
<span class="n">remove</span><span class="p">(</span><span class="n">testdat</span><span class="p">)</span>
|
|
<span class="n">remove</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
|
|
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|
<span class="k">return</span> <span class="n">scores</span></div>
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<span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
|
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'tmpdir'</span><span class="p">):</span>
|
|
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
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</pre></div>
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