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<p class="caption"><span class="caption-text">Attack Documentation:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/attack.html">Attack Documentation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/greedy_word_swap.html">Greedy Word Swap</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/genetic_algorithm.html">Genetic Algorithm</a></li>
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<p class="caption"><span class="caption-text">Models Documentation:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../models/bert.html">BERT</a></li>
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<p class="caption"><span class="caption-text">Transformations Documentation:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../transformations/transformation.html">Transformation</a></li>
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<p class="caption"><span class="caption-text">Constraints Documentation:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../constraints/constraint.html">Constraints</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../constraints/semantics/google_language_model.html">Google Language Model</a></li>
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<h1>Source code for attacks.attack</h1><div class="highlight"><pre>
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<span></span><span class="kn">import</span> <span class="nn">difflib</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">import</span> <span class="nn">os</span>
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<span class="kn">import</span> <span class="nn">torch</span>
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<span class="kn">import</span> <span class="nn">random</span>
|
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||||
<span class="kn">from</span> <span class="nn">textattack</span> <span class="k">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">utils</span>
|
||||
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||||
<span class="kn">from</span> <span class="nn">textattack.constraints</span> <span class="k">import</span> <span class="n">Constraint</span>
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||||
<span class="kn">from</span> <span class="nn">textattack.tokenized_text</span> <span class="k">import</span> <span class="n">TokenizedText</span>
|
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|
||||
<div class="viewcode-block" id="Attack"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack">[docs]</a><span class="k">class</span> <span class="nc">Attack</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> An attack generates adversarial examples on text. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> model: A PyTorch or TensorFlow model to attack</span>
|
||||
<span class="sd"> constraints: A list of constraints to add to the attack</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="p">[]):</span>
|
||||
<span class="sd">""" Initialize an attack object.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Attacks can be run multiple times</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> @TODO should `tokenizer` be an additional parameter or should</span>
|
||||
<span class="sd"> we assume every model has a .tokenizer ?</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
|
||||
<span class="c1"># Transformation and corresponding constraints.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">constraints</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">if</span> <span class="n">constraints</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">add_constraints</span><span class="p">(</span><span class="n">constraints</span><span class="p">)</span>
|
||||
<span class="c1"># List of files to output to.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_files</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_to_terminal</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_to_visdom</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<div class="viewcode-block" id="Attack.add_output_file"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack.add_output_file">[docs]</a> <span class="k">def</span> <span class="nf">add_output_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> When attack runs, it will output to this file. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> file (str): The path to the output file</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">directory</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">directory</span><span class="p">):</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">directory</span><span class="p">)</span>
|
||||
<span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Attack.add_constraint"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack.add_constraint">[docs]</a> <span class="k">def</span> <span class="nf">add_constraint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constraint</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Adds a constraint to the attack. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> constraint: A constraint to add, see constraints</span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> ValueError: If the constraint is not of type :obj:`Constraint`</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">constraint</span><span class="p">,</span> <span class="n">Constraint</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Cannot add constraint of type'</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">constraint</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">constraint</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Attack.add_constraints"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack.add_constraints">[docs]</a> <span class="k">def</span> <span class="nf">add_constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">constraints</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Adds multiple constraints to the attack. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> constraints: An iterable of constraints to add, see constraints. </span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> TypeError: If the constraints are not iterable</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Make sure constraints are iterable.</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="nb">iter</span><span class="p">(</span><span class="n">constraints</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">te</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="n">f</span><span class="s1">'Constraint list type {type(constraints)} is not iterable.'</span><span class="p">)</span>
|
||||
<span class="c1"># Store each constraint after validating its type.</span>
|
||||
<span class="k">for</span> <span class="n">constraint</span> <span class="ow">in</span> <span class="n">constraints</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">add_constraint</span><span class="p">(</span><span class="n">constraint</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Attack.get_transformations"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack.get_transformations">[docs]</a> <span class="k">def</span> <span class="nf">get_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">original_text</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Filters a list of transformations by self.constraints. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> transformation:</span>
|
||||
<span class="sd"> text:</span>
|
||||
<span class="sd"> original text (:obj:`type`, optional): Defaults to None. </span>
|
||||
<span class="sd"> **kwargs:</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> A filtered list of transformations where each transformation matches the constraints</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">transformations</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="n">transformation</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">C</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">constraints</span><span class="p">:</span>
|
||||
<span class="n">transformations</span> <span class="o">=</span> <span class="n">C</span><span class="o">.</span><span class="n">call_many</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">transformations</span><span class="p">,</span> <span class="n">original_text</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">transformations</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_attack_one</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Perturbs `text` to until `self.model` gives a different label</span>
|
||||
<span class="sd"> than `label`. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_call_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenized_text_list</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns model predictions for a list of TokenizedText objects. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">t</span><span class="o">.</span><span class="n">ids</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tokenized_text_list</span><span class="p">])</span>
|
||||
<span class="n">ids</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">utils</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
|
||||
|
||||
<div class="viewcode-block" id="Attack.attack"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.Attack.attack">[docs]</a> <span class="k">def</span> <span class="nf">attack</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Runs an attack on the given dataset and outputs the results to the console and the output file.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> dataset: An iterable of (label, text) pairs</span>
|
||||
<span class="sd"> shuffle (:obj:`bool`, optional): Whether to shuffle the data. Defaults to False.</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The results of the attack on the dataset</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">shuffle</span><span class="p">:</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
|
||||
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">:</span>
|
||||
<span class="n">tokenized_text</span> <span class="o">=</span> <span class="n">TokenizedText</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">text</span><span class="p">)</span>
|
||||
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_attack_one</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">)</span>
|
||||
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_terminal</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">result</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span><span class="o">*</span><span class="mi">35</span><span class="p">,</span> <span class="s1">'Result'</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="s1">'-'</span><span class="o">*</span><span class="mi">35</span><span class="p">)</span>
|
||||
<span class="n">result</span><span class="o">.</span><span class="n">print_</span><span class="p">()</span>
|
||||
<span class="nb">print</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_files</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">output_file</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_files</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
|
||||
<span class="n">output_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_visdom</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">results</span></div></div>
|
||||
|
||||
<div class="viewcode-block" id="AttackResult"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.AttackResult">[docs]</a><span class="k">class</span> <span class="nc">AttackResult</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Result of an Attack run on a single (label, text_input) pair. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> original_text (str): The original text</span>
|
||||
<span class="sd"> perturbed_text (str): The perturbed text resulting from the attack</span>
|
||||
<span class="sd"> original_label (int): he classification label of the original text</span>
|
||||
<span class="sd"> perturbed_label (int): The classification label of the perturbed text</span>
|
||||
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> @TODO support attacks that fail (no perturbed label/text)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">original_text</span><span class="p">,</span> <span class="n">perturbed_text</span><span class="p">,</span> <span class="n">original_label</span><span class="p">,</span>
|
||||
<span class="n">perturbed_label</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_text</span> <span class="o">=</span> <span class="n">original_text</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">perturbed_text</span> <span class="o">=</span> <span class="n">perturbed_text</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_label</span> <span class="o">=</span> <span class="n">original_label</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">perturbed_label</span> <span class="o">=</span> <span class="n">perturbed_label</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__data__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_text</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbed_text</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">perturbed_label</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">data</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'</span><span class="se">\n</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">__data__</span><span class="p">())</span>
|
||||
|
||||
<div class="viewcode-block" id="AttackResult.diff"><a class="viewcode-back" href="../../attacks/attack.html#attacks.attack.AttackResult.diff">[docs]</a> <span class="k">def</span> <span class="nf">diff</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Highlights the difference between two texts using color.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">_color</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">color_text_terminal</span>
|
||||
<span class="n">t1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_text</span>
|
||||
<span class="n">t2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbed_text</span>
|
||||
|
||||
<span class="n">words1</span> <span class="o">=</span> <span class="n">t1</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
<span class="n">words2</span> <span class="o">=</span> <span class="n">t2</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
|
||||
<span class="n">c1</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">color_from_label</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_label</span><span class="p">)</span>
|
||||
<span class="n">c2</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">color_from_label</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">perturbed_label</span><span class="p">)</span>
|
||||
<span class="n">new_is</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">new_w1s</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">new_w2s</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">words1</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">words2</span><span class="p">))):</span>
|
||||
<span class="n">w1</span> <span class="o">=</span> <span class="n">words1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="n">w2</span> <span class="o">=</span> <span class="n">words2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">w1</span> <span class="o">!=</span> <span class="n">w2</span><span class="p">:</span>
|
||||
<span class="n">new_is</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
|
||||
<span class="n">new_w1s</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_color</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">c1</span><span class="p">))</span>
|
||||
<span class="n">new_w2s</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_color</span><span class="p">(</span><span class="n">w2</span><span class="p">,</span> <span class="n">c2</span><span class="p">))</span>
|
||||
|
||||
<span class="n">t1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_text</span><span class="o">.</span><span class="n">replace_words_at_indices</span><span class="p">(</span><span class="n">new_is</span><span class="p">,</span> <span class="n">new_w1s</span><span class="p">)</span>
|
||||
<span class="n">t2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_text</span><span class="o">.</span><span class="n">replace_words_at_indices</span><span class="p">(</span><span class="n">new_is</span><span class="p">,</span> <span class="n">new_w2s</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">t1</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">t2</span><span class="p">))</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">print_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_label</span><span class="p">),</span> <span class="s1">'-->'</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">perturbed_label</span><span class="p">))</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</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">diff</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="kn">import</span> <span class="nn">textattack.attacks</span> <span class="k">as</span> <span class="nn">attacks</span>
|
||||
<span class="kn">import</span> <span class="nn">textattack.constraints</span> <span class="k">as</span> <span class="nn">constraints</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.datasets</span> <span class="k">import</span> <span class="n">YelpSentiment</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.models</span> <span class="k">import</span> <span class="n">BertForSentimentClassification</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.transformations</span> <span class="k">import</span> <span class="n">WordSwapCounterfit</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="c1"># Only use one GPU, if we have one.</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'CUDA_VISIBLE_DEVICES'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'0'</span>
|
||||
<span class="c1"># Disable tensorflow logs, except in the case of an error.</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'TF_CPP_MIN_LOG_LEVEL'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'3'</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">BertForSentimentClassification</span><span class="p">()</span>
|
||||
|
||||
<span class="n">transformation</span> <span class="o">=</span> <span class="n">WordSwapCounterfit</span><span class="p">(</span><span class="n">max_candidates</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<span class="n">attack</span> <span class="o">=</span> <span class="n">attacks</span><span class="o">.</span><span class="n">GreedyWordSwapWIR</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">transformation</span><span class="p">)</span>
|
||||
|
||||
<span class="n">attack</span><span class="o">.</span><span class="n">add_constraints</span><span class="p">(</span>
|
||||
<span class="p">(</span>
|
||||
<span class="n">constraints</span><span class="o">.</span><span class="n">semantics</span><span class="o">.</span><span class="n">GoogleLanguageModel</span><span class="p">(</span><span class="n">top_n</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
||||
<span class="c1"># constraints.syntax.LanguageTool(1),</span>
|
||||
<span class="c1"># constraints.semantics.UniversalSentenceEncoder(0.9, metric='cosine'),</span>
|
||||
<span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">yelp_data</span> <span class="o">=</span> <span class="n">YelpSentiment</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="c1"># yelp_data = [</span>
|
||||
<span class="c1"># (1, 'I hate this Restaurant!'), </span>
|
||||
<span class="c1"># (0, "Texas Jack's has amazing food.")</span>
|
||||
<span class="c1"># ]</span>
|
||||
|
||||
<span class="c1"># attack.enable_visdom()</span>
|
||||
<span class="n">attack</span><span class="o">.</span><span class="n">add_output_file</span><span class="p">(</span><span class="s1">'outputs/test.txt'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">attack</span><span class="o">.</span><span class="n">attack</span><span class="p">(</span><span class="n">yelp_data</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2019, UVA QData Lab
|
||||
|
||||
</p>
|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
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||||
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||||
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||||
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/genetic_algorithm.html">Genetic Algorithm</a></li>
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<h1>Source code for attacks.genetic_algorithm</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">'''</span>
|
||||
<span class="sd">Algorithm from Generating Natural Language Adversarial Examples by Alzantot et. al</span>
|
||||
|
||||
<span class="sd">`<arxiv.org/abs/1804.07998>`_</span>
|
||||
|
||||
<span class="sd">`<github.com/nesl/nlp_adversarial_examples>`_</span>
|
||||
<span class="sd">'''</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">textattack.attacks</span> <span class="k">import</span> <span class="n">Attack</span><span class="p">,</span> <span class="n">AttackResult</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.transformations</span> <span class="k">import</span> <span class="n">WordSwap</span>
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm"><a class="viewcode-back" href="../../attacks/genetic_algorithm.html#attacks.genetic_algorithm.GeneticAlgorithm">[docs]</a><span class="k">class</span> <span class="nc">GeneticAlgorithm</span><span class="p">(</span><span class="n">Attack</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> Attacks a model using a genetic algorithm. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> model: A PyTorch or TensorFlow model to attack.</span>
|
||||
<span class="sd"> transformation: The type of transformation to use. Should be a subclass of WordSwap. </span>
|
||||
<span class="sd"> pop_size (:obj:`int`, optional): The population size. Defauls to 20. </span>
|
||||
<span class="sd"> max_iters (:obj:`int`, optional): The maximum number of iterations to use. Defaults to 100. </span>
|
||||
<span class="sd"> n1 (:obj:`int`, optinal): The number of similar words. Defaults to 20. </span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> ValueError: If the transformation is not a subclass of WordSwap. </span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">pop_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n1</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transformation</span><span class="p">,</span> <span class="n">WordSwap</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">f</span><span class="s1">'Transformation is of type {type(transformation)}, should be a subclass of WordSwap'</span><span class="p">)</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">transformation</span>
|
||||
<span class="c1"># self.batch_model = batch_model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_iters</span> <span class="o">=</span> <span class="n">max_iters</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span> <span class="o">=</span> <span class="n">pop_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">top_n</span> <span class="o">=</span> <span class="n">n1</span> <span class="c1"># similar words</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">temp</span> <span class="o">=</span> <span class="mf">0.3</span>
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm.select_best_replacement"><a class="viewcode-back" href="../../attacks/genetic_algorithm.html#attacks.genetic_algorithm.GeneticAlgorithm.select_best_replacement">[docs]</a> <span class="k">def</span> <span class="nf">select_best_replacement</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">x_cur</span><span class="p">,</span> <span class="n">x_orig</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">replace_list</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Select the most effective replacement for word at position (pos)</span>
|
||||
<span class="sd"> in (x_cur) between the words in replace_list.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> pos:</span>
|
||||
<span class="sd"> x_cur:</span>
|
||||
<span class="sd"> x_orig:</span>
|
||||
<span class="sd"> target:</span>
|
||||
<span class="sd"> replace_list:</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">orig_words</span> <span class="o">=</span> <span class="n">x_orig</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
<span class="n">new_x_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">x_cur</span><span class="o">.</span><span class="n">replace_word_at_index</span><span class="p">(</span>
|
||||
<span class="n">pos</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> <span class="k">if</span> <span class="n">orig_words</span><span class="p">[</span><span class="n">pos</span><span class="p">]</span> <span class="o">!=</span> <span class="n">w</span> <span class="ow">and</span> <span class="n">w</span> <span class="o">!=</span> <span class="s1">'0.0'</span> <span class="k">else</span> <span class="n">x_cur</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">replace_list</span><span class="p">]</span>
|
||||
<span class="n">new_x_preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">(</span><span class="n">new_x_list</span><span class="p">)</span>
|
||||
|
||||
<span class="n">new_x_scores</span> <span class="o">=</span> <span class="n">new_x_preds</span><span class="p">[:,</span> <span class="n">target</span><span class="p">]</span>
|
||||
<span class="n">orig_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">([</span><span class="n">x_cur</span><span class="p">])[</span><span class="n">target</span><span class="p">]</span>
|
||||
<span class="n">new_x_scores</span> <span class="o">=</span> <span class="n">new_x_scores</span> <span class="o">-</span> <span class="n">orig_score</span>
|
||||
|
||||
<span class="c1"># Eliminate words that are not that close</span>
|
||||
<span class="n">new_x_scores</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">top_n</span><span class="p">:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">10000000</span>
|
||||
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> if self.use_lm:</span>
|
||||
<span class="sd"> prefix = ""</span>
|
||||
<span class="sd"> suffix = None</span>
|
||||
<span class="sd"> if pos > 0:</span>
|
||||
<span class="sd"> prefix = self.dataset.inv_dict[x_cur[pos-1]]</span>
|
||||
<span class="sd"> #</span>
|
||||
<span class="sd"> orig_word = self.dataset.inv_dict[x_orig[pos]]</span>
|
||||
<span class="sd"> if self.use_suffix and pos < x_cur.shape[0]-1:</span>
|
||||
<span class="sd"> if (x_cur[pos+1] != 0):</span>
|
||||
<span class="sd"> suffix = self.dataset.inv_dict[x_cur[pos+1]]</span>
|
||||
<span class="sd"> # print('** ', orig_word)</span>
|
||||
<span class="sd"> replace_words_and_orig = [</span>
|
||||
<span class="sd"> self.dataset.inv_dict[w] if w in self.dataset.inv_dict else 'UNK' for w in replace_list[:self.top_n]] + [orig_word]</span>
|
||||
<span class="sd"> # print(replace_words_and_orig)</span>
|
||||
<span class="sd"> replace_words_lm_scores = self.lm.get_words_probs(</span>
|
||||
<span class="sd"> prefix, replace_words_and_orig, suffix)</span>
|
||||
<span class="sd"> # print(replace_words_lm_scores)</span>
|
||||
<span class="sd"> # for i in range(len(replace_words_and_orig)):</span>
|
||||
<span class="sd"> # print(replace_words_and_orig[i], ' -- ', replace_words_lm_scores[i])</span>
|
||||
|
||||
<span class="sd"> # select words</span>
|
||||
<span class="sd"> new_words_lm_scores = np.array(replace_words_lm_scores[:-1])</span>
|
||||
<span class="sd"> # abs_diff_lm_scores = np.abs(new_words_lm_scores - replace_words_lm_scores[-1])</span>
|
||||
<span class="sd"> # rank_replaces_by_lm = np.argsort(abs_diff_lm_scores)</span>
|
||||
<span class="sd"> rank_replaces_by_lm = np.argsort(-new_words_lm_scores)</span>
|
||||
|
||||
<span class="sd"> filtered_words_idx = rank_replaces_by_lm[self.top_n2:]</span>
|
||||
<span class="sd"> # print(filtered_words_idx)</span>
|
||||
<span class="sd"> new_x_scores[filtered_words_idx] = -10000000</span>
|
||||
<span class="sd"> '''</span>
|
||||
|
||||
<span class="k">if</span> <span class="p">(</span><span class="n">new_x_scores</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">new_x_list</span><span class="p">[</span><span class="n">new_x_scores</span><span class="o">.</span><span class="n">argmax</span><span class="p">()]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">x_cur</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm.perturb"><a class="viewcode-back" href="../../attacks/genetic_algorithm.html#attacks.genetic_algorithm.GeneticAlgorithm.perturb">[docs]</a> <span class="k">def</span> <span class="nf">perturb</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x_cur</span><span class="p">,</span> <span class="n">x_orig</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">w_select_probs</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
|
||||
<span class="c1"># Pick a word that is not modified and is not UNK</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> Replaces a word that has not been modifired. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x_cur:</span>
|
||||
<span class="sd"> x_orig:</span>
|
||||
<span class="sd"> neighbors:</span>
|
||||
<span class="sd"> w_select_probs:</span>
|
||||
<span class="sd"> target:</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
<span class="n">x_len</span> <span class="o">=</span> <span class="n">w_select_probs</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="n">rand_idx</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">choice</span><span class="p">(</span><span class="n">x_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">w_select_probs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">diff_set</span> <span class="o">=</span> <span class="n">x_cur</span><span class="o">.</span><span class="n">all_words_diff</span><span class="p">(</span><span class="n">x_orig</span><span class="p">)</span>
|
||||
<span class="n">num_replaceable_words</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">w_select_probs</span><span class="p">))</span>
|
||||
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">diff_set</span><span class="p">)</span> <span class="o"><</span> <span class="n">num_replaceable_words</span> <span class="ow">and</span> <span class="n">x_cur</span><span class="o">.</span><span class="n">ith_word_diff</span><span class="p">(</span><span class="n">x_orig</span><span class="p">,</span> <span class="n">rand_idx</span><span class="p">):</span>
|
||||
<span class="sd">''' The condition above has a quick hack to prevent getting stuck in infinite loop while processing too short examples and all words (excluding articles) have been already replaced and still no successful attack is found. A more elegant way to handle this could be implemented in Attack to abort early based on the status of all population members or to improve select_best_replacement by making it stochastic.</span>
|
||||
<span class="sd"> '''</span>
|
||||
<span class="n">rand_idx</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">choice</span><span class="p">(</span><span class="n">x_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">w_select_probs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">replace_list</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">rand_idx</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">replace_list</span><span class="p">)</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_n</span><span class="p">:</span>
|
||||
<span class="n">replace_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
|
||||
<span class="p">(</span><span class="n">replace_list</span><span class="p">,</span> <span class="n">np</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">top_n</span> <span class="o">-</span> <span class="n">replace_list</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="bp">self</span><span class="o">.</span><span class="n">select_best_replacement</span><span class="p">(</span><span class="n">rand_idx</span><span class="p">,</span> <span class="n">x_cur</span><span class="p">,</span> <span class="n">x_orig</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">replace_list</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm.generate_population"><a class="viewcode-back" href="../../attacks/genetic_algorithm.html#attacks.genetic_algorithm.GeneticAlgorithm.generate_population">[docs]</a> <span class="k">def</span> <span class="nf">generate_population</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x_orig</span><span class="p">,</span> <span class="n">neigbhors_list</span><span class="p">,</span> <span class="n">w_select_probs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">pop_size</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> Generates the population of replacement words. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x_orig:</span>
|
||||
<span class="sd"> neigbhors_list:</span>
|
||||
<span class="sd"> w_select_probs:</span>
|
||||
<span class="sd"> target:</span>
|
||||
<span class="sd"> pop_size:</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The population</span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">perturb</span><span class="p">(</span><span class="n">x_orig</span><span class="p">,</span> <span class="n">x_orig</span><span class="p">,</span> <span class="n">neigbhors_list</span><span class="p">,</span> <span class="n">w_select_probs</span><span class="p">,</span> <span class="n">target</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="n">pop_size</span><span class="p">)]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm.crossover"><a class="viewcode-back" href="../../attacks/genetic_algorithm.html#attacks.genetic_algorithm.GeneticAlgorithm.crossover">[docs]</a> <span class="k">def</span> <span class="nf">crossover</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x1:</span>
|
||||
<span class="sd"> x2:</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
|
||||
<span class="n">indices_to_replace</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">words_to_replace</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">x2_words</span> <span class="o">=</span> <span class="n">x2</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x1</span><span class="o">.</span><span class="n">words</span><span class="p">())):</span>
|
||||
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">()</span> <span class="o"><</span> <span class="mf">0.5</span><span class="p">:</span>
|
||||
<span class="n">indices_to_replace</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
|
||||
<span class="n">words_to_replace</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x2_words</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">x1</span><span class="o">.</span><span class="n">replace_words_at_indices</span><span class="p">(</span><span class="n">indices_to_replace</span><span class="p">,</span> <span class="n">words_to_replace</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">,</span> <span class="n">original_tokenized_text</span><span class="p">):</span>
|
||||
<span class="n">words</span> <span class="o">=</span> <span class="n">tokenized_text</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
<span class="n">neighbors_list</span> <span class="o">=</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="nb">len</span><span class="p">(</span><span class="n">words</span><span class="p">))]</span>
|
||||
<span class="n">transformations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_transformations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span>
|
||||
<span class="n">tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">original_text</span><span class="o">=</span><span class="n">original_tokenized_text</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">transformed_text</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
|
||||
<span class="n">diff_idx</span> <span class="o">=</span> <span class="n">tokenized_text</span><span class="o">.</span><span class="n">first_word_diff_index</span><span class="p">(</span><span class="n">transformed_text</span><span class="p">)</span>
|
||||
<span class="n">neighbors_list</span><span class="p">[</span><span class="n">diff_idx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformed_text</span><span class="o">.</span><span class="n">words</span><span class="p">()[</span><span class="n">diff_idx</span><span class="p">])</span>
|
||||
<span class="n">neighbors_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">neighbors_list</span><span class="p">]</span>
|
||||
<span class="n">neighbors_len</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">neighbors_list</span><span class="p">]</span>
|
||||
<span class="n">w_select_probs</span> <span class="o">=</span> <span class="n">neighbors_len</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">neighbors_len</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">neighbors_list</span><span class="p">,</span> <span class="n">w_select_probs</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_attack_one</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">original_label</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">):</span>
|
||||
<span class="n">target</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">original_label</span>
|
||||
<span class="n">original_tokenized_text</span> <span class="o">=</span> <span class="n">tokenized_text</span>
|
||||
<span class="n">neighbors_list</span><span class="p">,</span> <span class="n">w_select_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_neighbors</span><span class="p">(</span>
|
||||
<span class="n">tokenized_text</span><span class="p">,</span> <span class="n">original_tokenized_text</span><span class="p">)</span>
|
||||
<span class="n">pop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_population</span><span class="p">(</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span> <span class="n">neighbors_list</span><span class="p">,</span> <span class="n">w_select_probs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_iters</span><span class="p">):</span>
|
||||
<span class="n">pop_preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">(</span><span class="n">pop</span><span class="p">)</span>
|
||||
<span class="n">pop_scores</span> <span class="o">=</span> <span class="n">pop_preds</span><span class="p">[:,</span> <span class="n">target</span><span class="p">]</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t\t</span><span class="s1">'</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s1">' -- '</span><span class="p">,</span> <span class="n">pop_scores</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
|
||||
<span class="n">top_attack</span> <span class="o">=</span> <span class="n">pop_scores</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
|
||||
|
||||
<span class="n">logits</span> <span class="o">=</span> <span class="p">(</span><span class="n">pop_scores</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp</span><span class="p">)</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span>
|
||||
<span class="n">select_probs</span> <span class="o">=</span> <span class="p">(</span><span class="n">logits</span> <span class="o">/</span> <span class="n">logits</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
||||
|
||||
<span class="n">top_attack_probs</span> <span class="o">=</span> <span class="n">pop_preds</span><span class="p">[</span><span class="n">top_attack</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">top_attack_probs</span><span class="p">)</span> <span class="o">==</span> <span class="n">target</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">AttackResult</span><span class="p">(</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">pop</span><span class="p">[</span><span class="n">top_attack</span><span class="p">],</span>
|
||||
<span class="n">original_label</span><span class="p">,</span>
|
||||
<span class="n">target</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">elite</span> <span class="o">=</span> <span class="p">[</span><span class="n">pop</span><span class="p">[</span><span class="n">top_attack</span><span class="p">]]</span> <span class="c1"># elite</span>
|
||||
<span class="c1"># print(select_probs.shape)</span>
|
||||
<span class="n">parent1_idx</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">choice</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">select_probs</span><span class="p">)</span>
|
||||
<span class="n">parent2_idx</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">choice</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">select_probs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">initial_children</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">crossover</span><span class="p">(</span><span class="n">pop</span><span class="p">[</span><span class="n">parent1_idx</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
|
||||
<span class="n">pop</span><span class="p">[</span><span class="n">parent2_idx</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pop_size</span><span class="o">-</span><span class="mi">1</span><span class="p">)]</span>
|
||||
<span class="n">children</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">initial_children</span><span class="p">:</span>
|
||||
<span class="n">neighbors_list</span><span class="p">,</span> <span class="n">w_select_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_neighbors</span><span class="p">(</span>
|
||||
<span class="n">child</span><span class="p">,</span> <span class="n">original_tokenized_text</span><span class="p">)</span>
|
||||
<span class="n">children</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">perturb</span><span class="p">(</span>
|
||||
<span class="n">child</span><span class="p">,</span> <span class="n">original_tokenized_text</span><span class="p">,</span> <span class="n">neighbors_list</span><span class="p">,</span> <span class="n">w_select_probs</span><span class="p">,</span> <span class="n">target</span><span class="p">))</span>
|
||||
|
||||
<span class="n">pop</span> <span class="o">=</span> <span class="n">elite</span> <span class="o">+</span> <span class="n">children</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">AttackResult</span><span class="p">(</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">pop</span><span class="p">[</span><span class="n">top_attack</span><span class="p">],</span>
|
||||
<span class="n">original_label</span><span class="p">,</span>
|
||||
<span class="n">target</span>
|
||||
<span class="p">)</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
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|
||||
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|
||||
|
||||
|
||||
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|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2019, UVA QData Lab
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||||
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||||
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||||
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||||
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||||
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||||
|
||||
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|
||||
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|
||||
@@ -1,270 +0,0 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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<h1>Source code for attacks.greedy_word_swap</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">textattack.attacks</span> <span class="k">import</span> <span class="n">Attack</span><span class="p">,</span> <span class="n">AttackResult</span>
|
||||
|
||||
<div class="viewcode-block" id="GreedyWordSwap"><a class="viewcode-back" href="../../attacks/greedy_word_swap.html#attacks.greedy_word_swap.GreedyWordSwap">[docs]</a><span class="k">class</span> <span class="nc">GreedyWordSwap</span><span class="p">(</span><span class="n">Attack</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> An attack that greedily chooses from a list of possible </span>
|
||||
<span class="sd"> perturbations.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> model: The PyTorch NLP model to attack.</span>
|
||||
<span class="sd"> transformation: The type of transformation.</span>
|
||||
<span class="sd"> max_depth (:obj:`int`, optional): The maximum number of words to change. Defaults to 32. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">transformation</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span> <span class="o">=</span> <span class="n">max_depth</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_attack_one</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">original_label</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">):</span>
|
||||
<span class="n">original_tokenized_text</span> <span class="o">=</span> <span class="n">tokenized_text</span>
|
||||
<span class="n">num_words_changed</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">unswapped_word_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tokenized_text</span><span class="o">.</span><span class="n">words</span><span class="p">())))</span>
|
||||
<span class="n">new_tokenized_text</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">new_text_label</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">while</span> <span class="n">num_words_changed</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">unswapped_word_indices</span><span class="p">):</span>
|
||||
<span class="n">num_words_changed</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="n">transformed_text_candidates</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_transformations</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span>
|
||||
<span class="n">tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">indices_to_replace</span><span class="o">=</span><span class="n">unswapped_word_indices</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transformed_text_candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="c1"># If we did not find any possible perturbations, give up.</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">(</span><span class="n">transformed_text_candidates</span><span class="p">)</span>
|
||||
<span class="c1"># The best choice is the one that minimizes the original class label.</span>
|
||||
<span class="n">best_index</span> <span class="o">=</span> <span class="n">scores</span><span class="p">[:,</span> <span class="n">original_label</span><span class="p">]</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
|
||||
<span class="n">new_tokenized_text</span> <span class="o">=</span> <span class="n">transformed_text_candidates</span><span class="p">[</span><span class="n">best_index</span><span class="p">]</span>
|
||||
<span class="c1"># If we changed the label, break.</span>
|
||||
<span class="n">new_text_label</span> <span class="o">=</span> <span class="n">scores</span><span class="p">[</span><span class="n">best_index</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">new_text_label</span> <span class="o">!=</span> <span class="n">original_label</span><span class="p">:</span>
|
||||
<span class="k">break</span>
|
||||
<span class="c1"># Otherwise, remove this word from list of words to change and</span>
|
||||
<span class="c1"># iterate.</span>
|
||||
<span class="n">word_swap_loc</span> <span class="o">=</span> <span class="n">tokenized_text</span><span class="o">.</span><span class="n">first_word_diff_index</span><span class="p">(</span><span class="n">new_tokenized_text</span><span class="p">)</span>
|
||||
<span class="n">tokenized_text</span> <span class="o">=</span> <span class="n">new_tokenized_text</span>
|
||||
<span class="n">unswapped_word_indices</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">word_swap_loc</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">AttackResult</span><span class="p">(</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">new_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">original_label</span><span class="p">,</span>
|
||||
<span class="n">new_text_label</span>
|
||||
<span class="p">)</span></div>
|
||||
</pre></div>
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<h1>Source code for attacks.greedy_word_swap_wir</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">from</span> <span class="nn">textattack.attacks</span> <span class="k">import</span> <span class="n">Attack</span><span class="p">,</span> <span class="n">AttackResult</span>
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
|
||||
<div class="viewcode-block" id="GreedyWordSwapWIR"><a class="viewcode-back" href="../../attacks/greedy_word_swap.html#attacks.greedy_word_swap_wir.GreedyWordSwapWIR">[docs]</a><span class="k">class</span> <span class="nc">GreedyWordSwapWIR</span><span class="p">(</span><span class="n">Attack</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> An attack that greedily chooses from a list of possible </span>
|
||||
<span class="sd"> perturbations for each index, after ranking indices by importance.</span>
|
||||
<span class="sd"> Reimplementation of paper:</span>
|
||||
<span class="sd"> Is BERT Really Robust? A Strong Baseline for Natural Language Attack on </span>
|
||||
<span class="sd"> Text Classification and Entailment by Jin et. al, 2019</span>
|
||||
<span class="sd"> https://github.com/jind11/TextFooler </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> model: The PyTorch NLP model to attack.</span>
|
||||
<span class="sd"> transformation: The type of transformation.</span>
|
||||
<span class="sd"> max_depth (:obj:`int`, optional): The maximum number of words to change. Defaults to 32. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">transformation</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span> <span class="o">=</span> <span class="n">max_depth</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_attack_one</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">original_label</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">):</span>
|
||||
<span class="n">original_tokenized_text</span> <span class="o">=</span> <span class="n">tokenized_text</span>
|
||||
<span class="n">num_words_changed</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="c1"># Sort words by order of importance</span>
|
||||
<span class="n">orig_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">([</span><span class="n">tokenized_text</span><span class="p">])</span>
|
||||
<span class="n">orig_prob</span> <span class="o">=</span> <span class="n">orig_probs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
|
||||
<span class="n">len_text</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokenized_text</span><span class="o">.</span><span class="n">words</span><span class="p">())</span>
|
||||
<span class="n">leave_one_texts</span> <span class="o">=</span> \
|
||||
<span class="p">[</span><span class="n">tokenized_text</span><span class="o">.</span><span class="n">replace_word_at_index</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="s1">'[UNKNOWN]'</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">len_text</span><span class="p">)]</span>
|
||||
<span class="n">leave_one_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">(</span><span class="n">leave_one_texts</span><span class="p">)</span>
|
||||
<span class="n">leave_one_probs_argmax</span> <span class="o">=</span> <span class="n">leave_one_probs</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">importance_scores</span> <span class="o">=</span> <span class="p">(</span><span class="n">orig_prob</span> <span class="o">-</span> <span class="n">leave_one_probs</span><span class="p">[:,</span> <span class="n">original_label</span><span class="p">]</span>
|
||||
<span class="o">+</span> <span class="p">(</span><span class="n">leave_one_probs_argmax</span> <span class="o">!=</span> <span class="n">original_label</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">*</span>
|
||||
<span class="p">(</span><span class="n">leave_one_probs</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span><span class="n">orig_probs</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">leave_one_probs_argmax</span><span class="p">)))</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
||||
<span class="n">index_order</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">importance_scores</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
|
||||
|
||||
<span class="n">new_tokenized_text</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">new_text_label</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">while</span> <span class="n">num_words_changed</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_depth</span> <span class="ow">and</span> <span class="n">i</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">index_order</span><span class="p">):</span>
|
||||
<span class="n">transformed_text_candidates</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_transformations</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span>
|
||||
<span class="n">tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">indices_to_replace</span><span class="o">=</span><span class="p">[</span><span class="n">index_order</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
|
||||
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transformed_text_candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">continue</span>
|
||||
<span class="n">num_words_changed</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_model</span><span class="p">(</span><span class="n">transformed_text_candidates</span><span class="p">)</span>
|
||||
<span class="c1"># The best choice is the one that minimizes the original class label.</span>
|
||||
<span class="n">best_index</span> <span class="o">=</span> <span class="n">scores</span><span class="p">[:,</span> <span class="n">original_label</span><span class="p">]</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
|
||||
<span class="n">new_tokenized_text</span> <span class="o">=</span> <span class="n">transformed_text_candidates</span><span class="p">[</span><span class="n">best_index</span><span class="p">]</span>
|
||||
<span class="c1"># If we changed the label, break.</span>
|
||||
<span class="n">new_text_label</span> <span class="o">=</span> <span class="n">scores</span><span class="p">[</span><span class="n">best_index</span><span class="p">]</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">new_text_label</span> <span class="o">!=</span> <span class="n">original_label</span><span class="p">:</span>
|
||||
<span class="k">break</span>
|
||||
<span class="n">tokenized_text</span> <span class="o">=</span> <span class="n">new_tokenized_text</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">AttackResult</span><span class="p">(</span>
|
||||
<span class="n">original_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">new_tokenized_text</span><span class="p">,</span>
|
||||
<span class="n">original_label</span><span class="p">,</span>
|
||||
<span class="n">new_text_label</span>
|
||||
<span class="p">)</span></div>
|
||||
</pre></div>
|
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|
||||
<h1>Source code for constraints.constraint</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">""" Abstract classes represent constraints on text adversarial examples. </span>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Constraint"><a class="viewcode-back" href="../../constraints/constraint.html#constraints.constraint.Constraint">[docs]</a><span class="k">class</span> <span class="nc">Constraint</span><span class="p">:</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> An abstract class that represents constraints on adversial text examples. </span>
|
||||
<span class="sd"> A constraint evaluates if (x,x_adv) meets a certain constraint. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="Constraint.call_many"><a class="viewcode-back" href="../../constraints/constraint.html#constraints.constraint.Constraint.call_many">[docs]</a> <span class="k">def</span> <span class="nf">call_many</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">,</span> <span class="n">original_text</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Filters x_adv_list to x_adv where C(x,x_adv) is true.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x:</span>
|
||||
<span class="sd"> x_adv_list:</span>
|
||||
<span class="sd"> original_text(:obj:`type`, optional): Defaults to None. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">):</span>
|
||||
<span class="sd">""" Returns True if C(x,x_adv) is true. """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
</pre></div>
|
||||
|
||||
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||||
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<h1>Source code for constraints.semantics.google_language_model.alzantot_goog_lm</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd"> Author: Moustafa Alzantot (malzantot@ucla.edu)</span>
|
||||
<span class="sd"> All rights reserved.</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
|
||||
<span class="kn">import</span> <span class="nn">sys</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">google.protobuf</span> <span class="k">import</span> <span class="n">text_format</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">textattack.constraints.semantics.google_language_model.lm_utils</span> <span class="k">as</span> <span class="nn">lm_utils</span>
|
||||
<span class="kn">import</span> <span class="nn">textattack.constraints.semantics.google_language_model.lm_data_utils</span> <span class="k">as</span> <span class="nn">lm_data_utils</span>
|
||||
|
||||
<span class="n">tf</span><span class="o">.</span><span class="n">get_logger</span><span class="p">()</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="s1">'INFO'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># @TODO automatically choose between GPU and CPU.</span>
|
||||
|
||||
<span class="n">ROOT_FOLDER</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research_OLD/nlp_adversarial_examples_new/goog_lm'</span>
|
||||
|
||||
<div class="viewcode-block" id="GoogLMHelper"><a class="viewcode-back" href="../../../../constraints/semantics/google_language_model.html#constraints.semantics.google_language_model.alzantot_goog_lm.GoogLMHelper">[docs]</a><span class="k">class</span> <span class="nc">GoogLMHelper</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> An implementation of `<https://arxiv.org/abs/1804.07998>`_</span>
|
||||
<span class="sd"> adapted from `<https://github.com/nesl/nlp_adversarial_examples>`_. </span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">PBTXT_PATH</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ROOT_FOLDER</span><span class="p">,</span> <span class="s1">'graph-2016-09-10-gpu.pbtxt'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">CKPT_PATH</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ROOT_FOLDER</span><span class="p">,</span> <span class="s1">'ckpt-*'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">VOCAB_PATH</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ROOT_FOLDER</span><span class="p">,</span> <span class="s1">'vocab-2016-09-10.txt'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">NUM_TIMESTEPS</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">MAX_WORD_LEN</span> <span class="o">=</span> <span class="mi">50</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vocab</span> <span class="o">=</span> <span class="n">lm_data_utils</span><span class="o">.</span><span class="n">CharsVocabulary</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">VOCAB_PATH</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">MAX_WORD_LEN</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"/gpu:1"</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">compat</span><span class="o">.</span><span class="n">v1</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="n">lm_utils</span><span class="o">.</span><span class="n">LoadModel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sess</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">PBTXT_PATH</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">CKPT_PATH</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="GoogLMHelper.get_words_probs"><a class="viewcode-back" href="../../../../constraints/semantics/google_language_model.html#constraints.semantics.google_language_model.alzantot_goog_lm.GoogLMHelper.get_words_probs">[docs]</a> <span class="k">def</span> <span class="nf">get_words_probs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix_words</span><span class="p">,</span> <span class="n">list_words</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> Retrieves the probability of words.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> prefix_words:</span>
|
||||
<span class="sd"> list_words:</span>
|
||||
<span class="sd"> suffix (:obj:`type`, optional): Defaults to None. </span>
|
||||
|
||||
<span class="sd"> '''</span>
|
||||
<span class="n">targets</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="bp">self</span><span class="o">.</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">NUM_TIMESTEPS</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
|
||||
<span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">NUM_TIMESTEPS</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">prefix_words</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'<S>'</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">prefix_words</span> <span class="o">=</span> <span class="s1">'<S> '</span> <span class="o">+</span> <span class="n">prefix_words</span>
|
||||
<span class="n">prefix</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">word_to_id</span><span class="p">(</span><span class="n">w</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">prefix_words</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
|
||||
<span class="n">prefix_char_ids</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">word_to_char_ids</span><span class="p">(</span><span class="n">w</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">prefix_words</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
|
||||
|
||||
<span class="n">inputs</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="bp">self</span><span class="o">.</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">NUM_TIMESTEPS</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
|
||||
<span class="n">char_ids_inputs</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="bp">self</span><span class="o">.</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">NUM_TIMESTEPS</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">max_word_length</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
|
||||
|
||||
<span class="n">samples</span> <span class="o">=</span> <span class="n">prefix</span><span class="p">[:]</span>
|
||||
<span class="n">char_ids_samples</span> <span class="o">=</span> <span class="n">prefix_char_ids</span><span class="p">[:]</span>
|
||||
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span> <span class="p">[</span><span class="n">samples</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]]</span>
|
||||
<span class="n">char_ids_inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">char_ids_samples</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">softmax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'softmax_out'</span><span class="p">],</span>
|
||||
<span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'char_inputs_in'</span><span class="p">]:</span> <span class="n">char_ids_inputs</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'inputs_in'</span><span class="p">]:</span> <span class="n">inputs</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'targets_in'</span><span class="p">]:</span> <span class="n">targets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'target_weights_in'</span><span class="p">]:</span> <span class="n">weights</span>
|
||||
<span class="p">}</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">words_ids</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">word_to_id</span><span class="p">(</span><span class="n">w</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">list_words</span><span class="p">]</span>
|
||||
<span class="n">word_probs</span> <span class="o">=</span><span class="p">[</span><span class="n">softmax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">w_id</span><span class="p">]</span> <span class="k">for</span> <span class="n">w_id</span> <span class="ow">in</span> <span class="n">words_ids</span><span class="p">]</span>
|
||||
<span class="n">word_probs</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="n">word_probs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">suffix</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">suffix_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">word_probs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">suffix_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">word_to_id</span><span class="p">(</span><span class="n">suffix</span><span class="p">)</span>
|
||||
<span class="n">suffix_probs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">w_id</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">words_ids</span><span class="p">):</span>
|
||||
<span class="n">inputs</span> <span class="o">=</span> <span class="p">[[</span><span class="n">w_id</span><span class="p">]]</span>
|
||||
<span class="n">w_char_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">word_to_char_ids</span><span class="p">(</span><span class="n">list_words</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||||
<span class="n">char_ids_inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">w_char_ids</span>
|
||||
<span class="n">softmax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'softmax_out'</span><span class="p">],</span>
|
||||
<span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'char_inputs_in'</span><span class="p">]:</span> <span class="n">char_ids_inputs</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'inputs_in'</span><span class="p">]:</span> <span class="n">inputs</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'targets_in'</span><span class="p">]:</span> <span class="n">targets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">[</span><span class="s1">'target_weights_in'</span><span class="p">]:</span> <span class="n">weights</span>
|
||||
<span class="p">})</span>
|
||||
<span class="n">suffix_probs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">softmax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">suffix_id</span><span class="p">])</span>
|
||||
<span class="n">suffix_probs</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="n">suffix_probs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">suffix_probs</span> <span class="o">*</span> <span class="n">word_probs</span></div></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="n">my_lm</span> <span class="o">=</span> <span class="n">LM</span><span class="p">()</span>
|
||||
<span class="n">list_words</span> <span class="o">=</span> <span class="s1">'play will playing played afternoon'</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="n">prefix</span> <span class="o">=</span> <span class="s1">'i'</span>
|
||||
<span class="n">suffix</span> <span class="o">=</span> <span class="s1">'yesterday'</span>
|
||||
<span class="n">probs</span> <span class="o">=</span> <span class="p">(</span><span class="n">my_lm</span><span class="o">.</span><span class="n">get_words_probs</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">list_words</span><span class="p">,</span> <span class="n">suffix</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">list_words</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="s1">' - '</span><span class="p">,</span> <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
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<h1>Source code for constraints.semantics.google_language_model.google_language_model</h1><div class="highlight"><pre>
|
||||
<span></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">time</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">textattack.constraints</span> <span class="k">import</span> <span class="n">Constraint</span>
|
||||
<span class="kn">from</span> <span class="nn">.alzantot_goog_lm</span> <span class="k">import</span> <span class="n">GoogLMHelper</span>
|
||||
|
||||
<div class="viewcode-block" id="GoogleLanguageModel"><a class="viewcode-back" href="../../../../constraints/semantics/google_language_model.html#constraints.semantics.google_language_model.google_language_model.GoogleLanguageModel">[docs]</a><span class="k">class</span> <span class="nc">GoogleLanguageModel</span><span class="p">(</span><span class="n">Constraint</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Constraint that uses the Google 1 Billion Words Language Model to </span>
|
||||
<span class="sd"> determine the difference in perplexity between x and x_adv. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> top_n (int):</span>
|
||||
<span class="sd"> top_n_per_index (int):</span>
|
||||
<span class="sd"> print_step (:obj:`bool`, optional): Whether to print each step. Defaults to False. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The :obj:`top_n` sentences.</span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> ValueError: If :obj:`top_n` or :obj:`top_n_per_index` are not provided. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> @TODO allow user to set perplexity threshold; implement __call__.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> @TODO this use of the language model only really makes sense for </span>
|
||||
<span class="sd"> adversarial examples based on word swaps</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">top_n</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">top_n_per_index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">print_step</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">top_n</span> <span class="ow">or</span> <span class="n">top_n_per_index</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Cannot instantiate GoogleLanguageModel without top_n or top_n_per_index'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lm</span> <span class="o">=</span> <span class="n">GoogLMHelper</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">top_n</span> <span class="o">=</span> <span class="n">top_n</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">top_n_per_index</span> <span class="o">=</span> <span class="n">top_n_per_index</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">print_step</span> <span class="o">=</span> <span class="n">print_step</span>
|
||||
|
||||
<div class="viewcode-block" id="GoogleLanguageModel.call_many"><a class="viewcode-back" href="../../../../constraints/semantics/google_language_model.html#constraints.semantics.google_language_model.google_language_model.GoogleLanguageModel.call_many">[docs]</a> <span class="k">def</span> <span class="nf">call_many</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">,</span> <span class="n">original_text</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the `top_n` of x_adv_list, as evaluated by the language </span>
|
||||
<span class="sd"> model. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x:</span>
|
||||
<span class="sd"> X_adv_list:</span>
|
||||
<span class="sd"> original_text (:obj:`type`, optional): Defaults to None. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_adv_list</span><span class="p">):</span> <span class="k">return</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_probs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">):</span>
|
||||
<span class="n">word_swap_index</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">first_word_diff_index</span><span class="p">(</span><span class="n">x_adv_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">prefix</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">text_until_word_index</span><span class="p">(</span><span class="n">word_swap_index</span><span class="p">)</span>
|
||||
<span class="n">swapped_words</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="n">t</span><span class="o">.</span><span class="n">words</span><span class="p">()[</span><span class="n">word_swap_index</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">x_adv_list</span><span class="p">])</span>
|
||||
<span class="n">suffix</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">text_after_word_index</span><span class="p">(</span><span class="n">word_swap_index</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">print_step</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">swapped_words</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span>
|
||||
<span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lm</span><span class="o">.</span><span class="n">get_words_probs</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">swapped_words</span><span class="p">,</span> <span class="n">suffix</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">probs</span>
|
||||
|
||||
<span class="c1"># This creates a dictionary where each new key is initialized to [].</span>
|
||||
<span class="n">word_swap_index_map</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">x_adv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">x_adv_list</span><span class="p">):</span>
|
||||
<span class="n">word_swap_index</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">first_word_diff_index</span><span class="p">(</span><span class="n">x_adv</span><span class="p">)</span>
|
||||
<span class="n">word_swap_index_map</span><span class="p">[</span><span class="n">word_swap_index</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">idx</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">))</span>
|
||||
|
||||
<span class="n">probs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">word_swap_index</span><span class="p">,</span> <span class="n">item_list</span> <span class="ow">in</span> <span class="n">word_swap_index_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="c1"># zip(*some_list) is the inverse operator of zip!</span>
|
||||
<span class="n">item_indices</span><span class="p">,</span> <span class="n">this_x_adv_list</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">item_list</span><span class="p">)</span>
|
||||
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">probs_of_swaps_at_index</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">item_indices</span><span class="p">,</span> <span class="n">get_probs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">this_x_adv_list</span><span class="p">)))</span>
|
||||
<span class="c1"># Sort by probability in descending order and take the top n for this index.</span>
|
||||
<span class="n">probs_of_swaps_at_index</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_n_per_index</span><span class="p">:</span>
|
||||
<span class="n">probs_of_swaps_at_index</span> <span class="o">=</span> <span class="n">probs_of_swaps_at_index</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">top_n_per_index</span><span class="p">]</span>
|
||||
<span class="n">probs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">probs_of_swaps_at_index</span><span class="p">)</span>
|
||||
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">print_step</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">'LM {len(item_list)} items in {t2-t1}s'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Probs is a list of (index, prob) where index is the corresponding </span>
|
||||
<span class="c1"># position in x_adv_list.</span>
|
||||
<span class="n">probs</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="c1"># Now that they're in order, reduce to just a list of probabilities.</span>
|
||||
<span class="n">probs</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="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">probs</span><span class="p">)))</span>
|
||||
|
||||
<span class="c1"># Get the indices of the maximum elements.</span>
|
||||
<span class="n">max_el_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="o">-</span><span class="n">probs</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_n</span><span class="p">:</span>
|
||||
<span class="n">max_el_indices</span> <span class="o">=</span> <span class="n">max_el_indices</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">top_n</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># Put indices in order, now, so that the examples are returned in the</span>
|
||||
<span class="c1"># same order they were passed in.</span>
|
||||
<span class="n">max_el_indices</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_adv_list</span><span class="p">)[</span><span class="n">max_el_indices</span><span class="p">]</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
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© Copyright 2019, UVA QData Lab
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<li class="toctree-l1"><a class="reference internal" href="../../../attacks/greedy_word_swap.html">Greedy Word Swap</a></li>
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||||
<li><a href="../../index.html">Module code</a> »</li>
|
||||
|
||||
<li>constraints.semantics.universal_sentence_encoder</li>
|
||||
|
||||
|
||||
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|
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|
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|
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|
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|
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<h1>Source code for constraints.semantics.universal_sentence_encoder</h1><div class="highlight"><pre>
|
||||
<span></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">os</span>
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">textattack.constraints</span> <span class="k">import</span> <span class="n">Constraint</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.models</span> <span class="k">import</span> <span class="n">InferSent</span>
|
||||
<span class="kn">from</span> <span class="nn">textattack.utils</span> <span class="k">import</span> <span class="n">download_if_needed</span><span class="p">,</span> <span class="n">get_device</span>
|
||||
|
||||
<div class="viewcode-block" id="UniversalSentenceEncoder"><a class="viewcode-back" href="../../../constraints/semantics/semantics.html#constraints.semantics.universal_sentence_encoder.UniversalSentenceEncoder">[docs]</a><span class="k">class</span> <span class="nc">UniversalSentenceEncoder</span><span class="p">(</span><span class="n">Constraint</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Constraint using cosine similarity between Universal Sentence Encodings</span>
|
||||
<span class="sd"> of x and x_adv where the text embeddings are created using InferSent.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> threshold (:obj:`float`, optional): The threshold for the constraint to bet met.</span>
|
||||
<span class="sd"> Defaults to 0.8</span>
|
||||
<span class="sd"> metric (:obj:`str`, optional): The metric function to use. Must be one of TODO. </span>
|
||||
<span class="sd"> Defaults to cosine. </span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> ValueError: If :obj:`metric` is not supported</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">MODEL_PATH</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research/text_attacks/RobustNLP/AttackGeneration/infersent-encoder'</span>
|
||||
<span class="n">WORD_EMBEDDING_PATH</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research/text_attacks/RobustNLP/AttackGeneration/word_embeddings'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'cosine'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_infersent_model</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">metric</span><span class="o">==</span><span class="s1">'cosine'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dist</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">CosineSimilarity</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">f</span><span class="s1">'Unsupported metric </span><span class="si">{metric}</span><span class="s1">.'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="UniversalSentenceEncoder.get_infersent_model"><a class="viewcode-back" href="../../../constraints/semantics/semantics.html#constraints.semantics.universal_sentence_encoder.UniversalSentenceEncoder.get_infersent_model">[docs]</a> <span class="k">def</span> <span class="nf">get_infersent_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Retrieves the InferSent model. </span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The pretrained InferSent model. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">infersent_version</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
<span class="n">model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">UniversalSentenceEncoder</span><span class="o">.</span><span class="n">MODEL_PATH</span><span class="p">,</span> <span class="n">f</span><span class="s1">'infersent</span><span class="si">{infersent_version}</span><span class="s1">.pkl'</span><span class="p">)</span>
|
||||
<span class="n">download_if_needed</span><span class="p">(</span><span class="n">model_path</span><span class="p">)</span>
|
||||
<span class="n">params_model</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'bsize'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span> <span class="s1">'word_emb_dim'</span><span class="p">:</span> <span class="mi">300</span><span class="p">,</span> <span class="s1">'enc_lstm_dim'</span><span class="p">:</span> <span class="mi">2048</span><span class="p">,</span>
|
||||
<span class="s1">'pool_type'</span><span class="p">:</span> <span class="s1">'max'</span><span class="p">,</span> <span class="s1">'dpout_model'</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s1">'version'</span><span class="p">:</span> <span class="n">infersent_version</span><span class="p">}</span>
|
||||
<span class="n">infersent</span> <span class="o">=</span> <span class="n">InferSent</span><span class="p">(</span><span class="n">params_model</span><span class="p">)</span>
|
||||
<span class="n">infersent</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_path</span><span class="p">))</span>
|
||||
<span class="n">W2V_PATH</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">UniversalSentenceEncoder</span><span class="o">.</span><span class="n">WORD_EMBEDDING_PATH</span><span class="p">,</span>
|
||||
<span class="s1">'fastText'</span><span class="p">,</span> <span class="s1">'crawl-300d-2M.vec'</span><span class="p">)</span>
|
||||
<span class="n">download_if_needed</span><span class="p">(</span><span class="n">W2V_PATH</span><span class="p">)</span>
|
||||
<span class="n">infersent</span><span class="o">.</span><span class="n">set_w2v_path</span><span class="p">(</span><span class="n">W2V_PATH</span><span class="p">)</span>
|
||||
<span class="n">infersent</span><span class="o">.</span><span class="n">build_vocab_k_words</span><span class="p">(</span><span class="n">K</span><span class="o">=</span><span class="mi">100000</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">infersent</span></div>
|
||||
|
||||
<div class="viewcode-block" id="UniversalSentenceEncoder.score"><a class="viewcode-back" href="../../../constraints/semantics/semantics.html#constraints.semantics.universal_sentence_encoder.UniversalSentenceEncoder.score">[docs]</a> <span class="k">def</span> <span class="nf">score</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Returns the metric similarity between embeddings of the text and </span>
|
||||
<span class="sd"> the perturbed text. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x (str): The original text</span>
|
||||
<span class="sd"> x_adv (str): The perturbed text</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The similarity between the original and perturbed text using the metric. </span>
|
||||
|
||||
<span class="sd"> @TODO should this support multiple sentences for x_adv?</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">original_embedding</span><span class="p">,</span> <span class="n">perturbed_embedding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">],</span> <span class="n">tokenize</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">original_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">original_embedding</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">get_device</span><span class="p">())</span>
|
||||
<span class="n">perturbed_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">perturbed_embedding</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">get_device</span><span class="p">())</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)(</span><span class="n">original_embedding</span><span class="p">,</span> <span class="n">perturbed_embedding</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="UniversalSentenceEncoder.score_list"><a class="viewcode-back" href="../../../constraints/semantics/semantics.html#constraints.semantics.universal_sentence_encoder.UniversalSentenceEncoder.score_list">[docs]</a> <span class="k">def</span> <span class="nf">score_list</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the metric similarity between the embedding of the text and a list</span>
|
||||
<span class="sd"> of perturbed text. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x (str): The original text</span>
|
||||
<span class="sd"> x_adv_list (list(str)): A list of perturbed texts</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> A list with the similarity between the original text and each perturbed text in :obj:`x_adv_list`. </span>
|
||||
<span class="sd"> If x_adv_list is empty, an empty tensor is returned</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Return an empty tensor if x_adv_list is empty.</span>
|
||||
<span class="c1"># This prevents us from calling .repeat(x, 0), which throws an</span>
|
||||
<span class="c1"># error on machines with multiple GPUs (pytorch 1.2).</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_adv_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([])</span>
|
||||
|
||||
<span class="n">x_text</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">text</span>
|
||||
<span class="n">x_adv_list_text</span> <span class="o">=</span> <span class="p">[</span><span class="n">x_adv</span><span class="o">.</span><span class="n">text</span> <span class="k">for</span> <span class="n">x_adv</span> <span class="ow">in</span> <span class="n">x_adv_list</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">model</span><span class="o">.</span><span class="n">encode</span><span class="p">([</span><span class="n">x_text</span><span class="p">]</span> <span class="o">+</span> <span class="n">x_adv_list_text</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">original_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">embeddings</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">get_device</span><span class="p">())</span>
|
||||
<span class="n">perturbed_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">embeddings</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">get_device</span><span class="p">())</span>
|
||||
|
||||
<span class="c1"># Repeat original embedding to size of perturbed embedding.</span>
|
||||
<span class="n">original_embedding</span> <span class="o">=</span> <span class="n">original_embedding</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">perturbed_embedding</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)(</span><span class="n">original_embedding</span><span class="p">,</span> <span class="n">perturbed_embedding</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="UniversalSentenceEncoder.call_many"><a class="viewcode-back" href="../../../constraints/semantics/semantics.html#constraints.semantics.universal_sentence_encoder.UniversalSentenceEncoder.call_many">[docs]</a> <span class="k">def</span> <span class="nf">call_many</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">,</span> <span class="n">original_text</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Filters the list of perturbed texts so that the similarity between the original text</span>
|
||||
<span class="sd"> and the perturbed text is greater than the :obj:`threshold`. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> x (str): The original text</span>
|
||||
<span class="sd"> x_adv_list (list(str)): A list of perturbed texts</span>
|
||||
<span class="sd"> original_text(:obj:`type`, optional): Defaults to None. </span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> A filtered list of perturbed texts where each perturbed text meets the similarity threshold. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># @TODO can we rename this function `filter`? (It's a reserved keyword in python)</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">score_list</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x_adv_list</span><span class="p">)</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">scores</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span>
|
||||
<span class="n">mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">x_adv_list</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_adv</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">text</span><span class="p">,</span> <span class="n">x_adv</span><span class="o">.</span><span class="n">text</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> </div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for datasets.dataset</h1><div class="highlight"><pre>
|
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<div class="viewcode-block" id="TextAttackDataset"><a class="viewcode-back" href="../../datasets/user_defined_datasets.html#datasets.dataset.TextAttackDataset">[docs]</a><span></span><span class="k">class</span> <span class="nc">TextAttackDataset</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> A dataset for text attacks.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Any iterable of (label, text_input) pairs qualifies as </span>
|
||||
<span class="sd"> a TextAttackDataset.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">""" Loads a full dataset from disk. Typically stores tuples in</span>
|
||||
<span class="sd"> `self.examples`.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__iter__</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">examples</span><span class="o">.</span><span class="fm">__iter__</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__next__</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">examples</span><span class="o">.</span><span class="fm">__next__</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_load_text_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text_file_name</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">""" Loads (label, text) pairs from a text file. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Format must look like:</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> 1 this is a great little ...</span>
|
||||
<span class="sd"> 0 "i love hot n juicy . ...</span>
|
||||
<span class="sd"> 0 "\""this world needs a ...</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">examples</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">raw_line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="n">text_file_name</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">():</span>
|
||||
<span class="n">tokens</span> <span class="o">=</span> <span class="n">raw_line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="n">label</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">tokens</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">text</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tokens</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
|
||||
<span class="n">examples</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="n">text</span><span class="p">))</span>
|
||||
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">n</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">>=</span> <span class="n">n</span><span class="p">:</span> <span class="k">break</span>
|
||||
<span class="k">return</span> <span class="n">examples</span></div>
|
||||
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|
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<h1>Source code for datasets.yelp_sentiment</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">textattack</span> <span class="k">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">utils</span>
|
||||
<span class="kn">from</span> <span class="nn">.dataset</span> <span class="k">import</span> <span class="n">TextAttackDataset</span>
|
||||
|
||||
<div class="viewcode-block" id="YelpSentiment"><a class="viewcode-back" href="../../datasets/built-in_datasets.html#datasets.yelp_sentiment.YelpSentiment">[docs]</a><span class="k">class</span> <span class="nc">YelpSentiment</span><span class="p">(</span><span class="n">TextAttackDataset</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Loads the Yelp Sentiment dataset</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> n (int): The number of examples to load</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">DATA_PATH</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research_OLD/TextFooler/data/yelp'</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">""" Loads a full dataset from disk. """</span>
|
||||
<span class="n">utils</span><span class="o">.</span><span class="n">download_if_needed</span><span class="p">(</span><span class="n">YelpSentiment</span><span class="o">.</span><span class="n">DATA_PATH</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">examples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_text_file</span><span class="p">(</span><span class="n">YelpSentiment</span><span class="o">.</span><span class="n">DATA_PATH</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'YelpSentiment loaded'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">examples</span><span class="p">),</span> <span class="s1">'examples...'</span><span class="p">)</span></div>
|
||||
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|
||||
|
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|
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|
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230
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|
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|
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<p class="caption"><span class="caption-text">Models Documentation:</span></p>
|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../models/bert.html">BERT</a></li>
|
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|
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|
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|
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<li class="toctree-l1"><a class="reference internal" href="../constraints/constraint.html">Constraints</a></li>
|
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|
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<h1>All modules for which code is available</h1>
|
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|
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<li><a href="attacks/genetic_algorithm.html">attacks.genetic_algorithm</a></li>
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|
||||
<li><a href="constraints/semantics/google_language_model/google_language_model.html">constraints.semantics.google_language_model.google_language_model</a></li>
|
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<li><a href="constraints/semantics/universal_sentence_encoder.html">constraints.semantics.universal_sentence_encoder</a></li>
|
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<li><a href="datasets/dataset.html">datasets.dataset</a></li>
|
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<li><a href="datasets/yelp_sentiment.html">datasets.yelp_sentiment</a></li>
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<li><a href="transformations/transformation.html">transformations.transformation</a></li>
|
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<li><a href="transformations/word_swap.html">transformations.word_swap</a></li>
|
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<li><a href="transformations/word_swap_counterfit.html">transformations.word_swap_counterfit</a></li>
|
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|
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/genetic_algorithm.html">Genetic Algorithm</a></li>
|
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</ul>
|
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<p class="caption"><span class="caption-text">Models Documentation:</span></p>
|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../models/bert.html">BERT</a></li>
|
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|
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|
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|
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<p class="caption"><span class="caption-text">Constraints Documentation:</span></p>
|
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|
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|
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|
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|
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|
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<h1>Source code for models.bert_for_sentiment_classification</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">transformers.modeling_bert</span> <span class="k">import</span> <span class="n">BertForSequenceClassification</span>
|
||||
<span class="kn">from</span> <span class="nn">transformers.tokenization_bert</span> <span class="k">import</span> <span class="n">BertTokenizer</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">textattack.utils</span> <span class="k">as</span> <span class="nn">utils</span>
|
||||
<span class="kn">import</span> <span class="nn">torch</span>
|
||||
|
||||
<div class="viewcode-block" id="BertForSentimentClassification"><a class="viewcode-back" href="../../models/bert.html#models.bert_for_sentiment_classification.BertForSentimentClassification">[docs]</a><span class="k">class</span> <span class="nc">BertForSentimentClassification</span><span class="p">:</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> BERT fine-tuned on the Yelp Sentiment dataset for sentiment classification. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> max_seq_length(:obj:`int`, optional): Maximum length of a sequence after tokenizing.</span>
|
||||
<span class="sd"> Defaults to 32.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">MODEL_PATH</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research/text_attacks/RobustNLP/AttackGeneration/models/bert/models/bert-vanilla'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_seq_length</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
|
||||
<span class="n">utils</span><span class="o">.</span><span class="n">download_if_needed</span><span class="p">(</span><span class="n">BertForSentimentClassification</span><span class="o">.</span><span class="n">MODEL_PATH</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">BertForSequenceClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
|
||||
<span class="n">BertForSentimentClassification</span><span class="o">.</span><span class="n">MODEL_PATH</span><span class="p">,</span>
|
||||
<span class="n">num_labels</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">BertTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
|
||||
<span class="n">BertForSentimentClassification</span><span class="o">.</span><span class="n">MODEL_PATH</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">utils</span><span class="o">.</span><span class="n">get_device</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_seq_length</span> <span class="o">=</span> <span class="n">max_seq_length</span>
|
||||
|
||||
<div class="viewcode-block" id="BertForSentimentClassification.convert_text_to_ids"><a class="viewcode-back" href="../../models/bert.html#models.bert_for_sentiment_classification.BertForSentimentClassification.convert_text_to_ids">[docs]</a> <span class="k">def</span> <span class="nf">convert_text_to_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_text</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Takes a string input, tokenizes, formats,</span>
|
||||
<span class="sd"> and returns a tensor with text IDs. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> input_text (str): The text to tokenize</span>
|
||||
|
||||
<span class="sd"> Returns:</span>
|
||||
<span class="sd"> The ID of the tokenized text</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">input_text</span><span class="p">)</span>
|
||||
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_seq_length</span><span class="p">:</span>
|
||||
<span class="n">tokens</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
|
||||
<span class="n">tokens</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"[CLS]"</span><span class="p">]</span> <span class="o">+</span> <span class="n">tokens</span> <span class="o">+</span> <span class="p">[</span><span class="s2">"[SEP]"</span><span class="p">]</span>
|
||||
<span class="n">ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span>
|
||||
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_seq_length</span> <span class="o">+</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">ids</span> <span class="o">=</span> <span class="n">ids</span> <span class="o">+</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># @TODO Is it correct to just pad with zeros?</span>
|
||||
<span class="k">return</span> <span class="n">ids</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text_ids</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">text_ids</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">f</span><span class="s1">'Object of type {type(text_ids)} must be of type torch.tensor'</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>
|
||||
<span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">text_ids</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">pred</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<footer>
|
||||
|
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|
||||
<hr/>
|
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|
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<div role="contentinfo">
|
||||
<p>
|
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© Copyright 2019, UVA QData Lab
|
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|
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|
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481
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|
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|
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|
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|
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|
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<li class="toctree-l1"><a class="reference internal" href="../../users/introduction.html">What is TextAttack?</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../users/installation.html">Installation</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../users/examples.html">Examples</a></li>
|
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|
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<p class="caption"><span class="caption-text">Attack Documentation:</span></p>
|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../attacks/attack.html">Attack Documentation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../attacks/greedy_word_swap.html">Greedy Word Swap</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../attacks/genetic_algorithm.html">Genetic Algorithm</a></li>
|
||||
</ul>
|
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<p class="caption"><span class="caption-text">Models Documentation:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../models/bert.html">BERT</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../models/infer_sent.html">InferSent</a></li>
|
||||
</ul>
|
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<p class="caption"><span class="caption-text">Transformations Documentation:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../transformations/transformation.html">Transformation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../transformations/word_swap.html">Word Swap</a></li>
|
||||
</ul>
|
||||
<p class="caption"><span class="caption-text">Constraints Documentation:</span></p>
|
||||
<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../constraints/constraint.html">Constraints</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../constraints/semantics/semantics.html">Semantics</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../constraints/semantics/google_language_model.html">Google Language Model</a></li>
|
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</ul>
|
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<p class="caption"><span class="caption-text">Datasets:</span></p>
|
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<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../datasets/built-in_datasets.html">Built-in Datasets</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../datasets/user_defined_datasets.html">User-Defined Datasets</a></li>
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<h1>Source code for models.infer_sent</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1"># Copyright (c) 2017-present, Facebook, Inc.</span>
|
||||
<span class="c1"># All rights reserved.</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># This source code is licensed under the license found in the</span>
|
||||
<span class="c1"># LICENSE file in the root directory of this source tree.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd">This file contains the definition of encoders used in https://arxiv.org/pdf/1705.02364.pdf</span>
|
||||
<span class="sd">"""</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">time</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>
|
||||
|
||||
<div class="viewcode-block" id="InferSent"><a class="viewcode-back" href="../../models/infer_sent.html#models.infer_sent.InferSent">[docs]</a><span class="k">class</span> <span class="nc">InferSent</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">InferSent</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bsize</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'bsize'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_emb_dim</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'word_emb_dim'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm_dim</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'enc_lstm_dim'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pool_type</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'pool_type'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dpout_model</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'dpout_model'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="s1">'version'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">config</span> <span class="k">else</span> <span class="n">config</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">word_emb_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span>
|
||||
<span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dpout_model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bos</span> <span class="o">=</span> <span class="s1">'<s>'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">eos</span> <span class="o">=</span> <span class="s1">'</s>'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_pad</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">moses_tok</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bos</span> <span class="o">=</span> <span class="s1">'<p>'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">eos</span> <span class="o">=</span> <span class="s1">'</p>'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_pad</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">moses_tok</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">is_cuda</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="c1"># either all weights are on cpu or they are on gpu</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm</span><span class="o">.</span><span class="n">bias_hh_l0</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">is_cuda</span>
|
||||
|
||||
<div class="viewcode-block" id="InferSent.forward"><a class="viewcode-back" href="../../models/infer_sent.html#models.infer_sent.InferSent.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">sent_tuple</span><span class="p">):</span>
|
||||
<span class="c1"># sent_len: [max_len, ..., min_len] (bsize)</span>
|
||||
<span class="c1"># sent: (seqlen x bsize x worddim)</span>
|
||||
<span class="n">sent</span><span class="p">,</span> <span class="n">sent_len</span> <span class="o">=</span> <span class="n">sent_tuple</span>
|
||||
|
||||
<span class="c1"># Sort by length (keep idx)</span>
|
||||
<span class="n">sent_len_sorted</span><span class="p">,</span> <span class="n">idx_sort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">sent_len</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="o">-</span><span class="n">sent_len</span><span class="p">)</span>
|
||||
<span class="n">sent_len_sorted</span> <span class="o">=</span> <span class="n">sent_len_sorted</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="n">idx_unsort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">idx_sort</span><span class="p">)</span>
|
||||
|
||||
<span class="n">idx_sort</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">idx_sort</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">()</span> \
|
||||
<span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">idx_sort</span><span class="p">)</span>
|
||||
<span class="n">sent</span> <span class="o">=</span> <span class="n">sent</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">idx_sort</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Handling padding in Recurrent Networks</span>
|
||||
<span class="n">sent_packed</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">pack_padded_sequence</span><span class="p">(</span><span class="n">sent</span><span class="p">,</span> <span class="n">sent_len_sorted</span><span class="p">)</span>
|
||||
<span class="n">sent_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm</span><span class="p">(</span><span class="n">sent_packed</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># seqlen x batch x 2*nhid</span>
|
||||
<span class="n">sent_output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">pad_packed_sequence</span><span class="p">(</span><span class="n">sent_output</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># Un-sort by length</span>
|
||||
<span class="n">idx_unsort</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">idx_unsort</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">()</span> \
|
||||
<span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">idx_unsort</span><span class="p">)</span>
|
||||
<span class="n">sent_output</span> <span class="o">=</span> <span class="n">sent_output</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">idx_unsort</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Pooling</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool_type</span> <span class="o">==</span> <span class="s2">"mean"</span><span class="p">:</span>
|
||||
<span class="n">sent_len</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">sent_len</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="n">emb</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">sent_output</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">emb</span> <span class="o">=</span> <span class="n">emb</span> <span class="o">/</span> <span class="n">sent_len</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">emb</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool_type</span> <span class="o">==</span> <span class="s2">"max"</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_pad</span><span class="p">:</span>
|
||||
<span class="n">sent_output</span><span class="p">[</span><span class="n">sent_output</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1e9</span>
|
||||
<span class="n">emb</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">sent_output</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">emb</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
|
||||
<span class="n">emb</span> <span class="o">=</span> <span class="n">emb</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">emb</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">emb</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">set_w2v_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">w2v_path</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w2v_path</span> <span class="o">=</span> <span class="n">w2v_path</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_word_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="c1"># create vocab of words</span>
|
||||
<span class="n">word_dict</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">sentences</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">tokenize</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">sent</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sent</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">word</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">word_dict</span><span class="p">:</span>
|
||||
<span class="n">word_dict</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="n">word_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="n">word_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="k">return</span> <span class="n">word_dict</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_w2v</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word_dict</span><span class="p">):</span>
|
||||
<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">'w2v_path'</span><span class="p">),</span> <span class="s1">'w2v path not set'</span>
|
||||
<span class="c1"># create word_vec with w2v vectors</span>
|
||||
<span class="n">word_vec</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w2v_path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
|
||||
<span class="n">word</span><span class="p">,</span> <span class="n">vec</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">word_dict</span><span class="p">:</span>
|
||||
<span class="n">word_vec</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">' '</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Found </span><span class="si">%s</span><span class="s1">(/</span><span class="si">%s</span><span class="s1">) words with w2v vectors'</span> <span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">word_vec</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">word_dict</span><span class="p">)))</span>
|
||||
<span class="k">return</span> <span class="n">word_vec</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_w2v_k</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">K</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'w2v_path'</span><span class="p">),</span> <span class="s1">'w2v path not set'</span>
|
||||
<span class="c1"># create word_vec with k first w2v vectors</span>
|
||||
<span class="n">k</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">word_vec</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w2v_path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
|
||||
<span class="n">word</span><span class="p">,</span> <span class="n">vec</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o"><=</span> <span class="n">K</span><span class="p">:</span>
|
||||
<span class="n">word_vec</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">' '</span><span class="p">)</span>
|
||||
<span class="n">k</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o">></span> <span class="n">K</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]:</span>
|
||||
<span class="n">word_vec</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fromstring</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">' '</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o">></span> <span class="n">K</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">([</span><span class="n">w</span> <span class="ow">in</span> <span class="n">word_vec</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]]):</span>
|
||||
<span class="k">break</span>
|
||||
<span class="k">return</span> <span class="n">word_vec</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">build_vocab</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<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">'w2v_path'</span><span class="p">),</span> <span class="s1">'w2v path not set'</span>
|
||||
<span class="n">word_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_word_dict</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">tokenize</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_w2v</span><span class="p">(</span><span class="n">word_dict</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Vocab size : </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">)))</span>
|
||||
|
||||
<span class="c1"># build w2v vocab with k most frequent words</span>
|
||||
<span class="k">def</span> <span class="nf">build_vocab_k_words</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">K</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'w2v_path'</span><span class="p">),</span> <span class="s1">'w2v path not set'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_w2v_k</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Vocab size : </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">K</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">update_vocab</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<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">'w2v_path'</span><span class="p">),</span> <span class="s1">'warning : w2v path not set'</span>
|
||||
<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">'word_vec'</span><span class="p">),</span> <span class="s1">'build_vocab before updating it'</span>
|
||||
<span class="n">word_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_word_dict</span><span class="p">(</span><span class="n">sentences</span><span class="p">,</span> <span class="n">tokenize</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># keep only new words</span>
|
||||
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">word_dict</span><span class="p">:</span>
|
||||
<span class="k">del</span> <span class="n">word_dict</span><span class="p">[</span><span class="n">word</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># udpate vocabulary</span>
|
||||
<span class="k">if</span> <span class="n">word_dict</span><span class="p">:</span>
|
||||
<span class="n">new_word_vec</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_w2v</span><span class="p">(</span><span class="n">word_dict</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_word_vec</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">new_word_vec</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'New vocab size : </span><span class="si">%s</span><span class="s1"> (added </span><span class="si">%s</span><span class="s1"> words)'</span><span class="o">%</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_word_vec</span><span class="p">)))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
|
||||
<span class="c1"># sent in batch in decreasing order of lengths</span>
|
||||
<span class="c1"># batch: (bsize, max_len, word_dim)</span>
|
||||
<span class="n">embed</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="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_emb_dim</span><span class="p">))</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)):</span>
|
||||
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="n">i</span><span class="p">])):</span>
|
||||
<span class="n">embed</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">[</span><span class="n">batch</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">embed</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">nltk.tokenize</span> <span class="k">import</span> <span class="n">word_tokenize</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">moses_tok</span><span class="p">:</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">word_tokenize</span><span class="p">(</span><span class="n">s</span><span class="p">))</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" n't "</span><span class="p">,</span> <span class="s2">"n 't "</span><span class="p">)</span> <span class="c1"># HACK to get ~MOSES tokenization</span>
|
||||
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">word_tokenize</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">prepare_samples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">bsize</span><span class="p">,</span> <span class="n">tokenize</span><span class="p">,</span> <span class="n">verbose</span><span class="p">):</span>
|
||||
<span class="n">sentences</span> <span class="o">=</span> <span class="p">[[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">]</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">tokenize</span> <span class="k">else</span>
|
||||
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">]</span>
|
||||
<span class="n">n_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">])</span>
|
||||
|
||||
<span class="c1"># filters words without w2v vectors</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">)):</span>
|
||||
<span class="n">s_f</span> <span class="o">=</span> <span class="p">[</span><span class="n">word</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">s_f</span><span class="p">:</span>
|
||||
<span class="kn">import</span> <span class="nn">warnings</span>
|
||||
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'No words in "</span><span class="si">%s</span><span class="s1">" (idx=</span><span class="si">%s</span><span class="s1">) have w2v vectors. </span><span class="se">\</span>
|
||||
<span class="s1"> Replacing by "</s>"..'</span> <span class="o">%</span> <span class="p">(</span><span class="n">sentences</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">i</span><span class="p">))</span>
|
||||
<span class="n">s_f</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]</span>
|
||||
<span class="n">sentences</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">s_f</span>
|
||||
|
||||
<span class="n">lengths</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="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sentences</span><span class="p">])</span>
|
||||
<span class="n">n_wk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Nb words kept : </span><span class="si">%s</span><span class="s1">/</span><span class="si">%s</span><span class="s1"> (</span><span class="si">%.1f%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="p">(</span>
|
||||
<span class="n">n_wk</span><span class="p">,</span> <span class="n">n_w</span><span class="p">,</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="n">n_wk</span> <span class="o">/</span> <span class="n">n_w</span><span class="p">,</span> <span class="s1">'%'</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># sort by decreasing length</span>
|
||||
<span class="n">lengths</span><span class="p">,</span> <span class="n">idx_sort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">lengths</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="o">-</span><span class="n">lengths</span><span class="p">)</span>
|
||||
<span class="n">sentences</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="n">sentences</span><span class="p">)[</span><span class="n">idx_sort</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">lengths</span><span class="p">,</span> <span class="n">idx_sort</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentences</span><span class="p">,</span> <span class="n">bsize</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
|
||||
<span class="n">sentences</span><span class="p">,</span> <span class="n">lengths</span><span class="p">,</span> <span class="n">idx_sort</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prepare_samples</span><span class="p">(</span>
|
||||
<span class="n">sentences</span><span class="p">,</span> <span class="n">bsize</span><span class="p">,</span> <span class="n">tokenize</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
|
||||
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">stidx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">sentences</span><span class="p">),</span> <span class="n">bsize</span><span class="p">):</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">sentences</span><span class="p">[</span><span class="n">stidx</span><span class="p">:</span><span class="n">stidx</span> <span class="o">+</span> <span class="n">bsize</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">():</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">cuda</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>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">((</span><span class="n">batch</span><span class="p">,</span> <span class="n">lengths</span><span class="p">[</span><span class="n">stidx</span><span class="p">:</span><span class="n">stidx</span> <span class="o">+</span> <span class="n">bsize</span><span class="p">]))</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
||||
<span class="n">embeddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># unsort</span>
|
||||
<span class="n">idx_unsort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">idx_sort</span><span class="p">)</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">idx_unsort</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'Speed : </span><span class="si">%.1f</span><span class="s1"> sentences/s (</span><span class="si">%s</span><span class="s1"> mode, bsize=</span><span class="si">%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="p">(</span>
|
||||
<span class="nb">len</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tic</span><span class="p">),</span>
|
||||
<span class="s1">'gpu'</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">()</span> <span class="k">else</span> <span class="s1">'cpu'</span><span class="p">,</span> <span class="n">bsize</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">embeddings</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">visualize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sent</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sent</span> <span class="o">=</span> <span class="n">sent</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">tokenize</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">sent</span><span class="p">)</span>
|
||||
<span class="n">sent</span> <span class="o">=</span> <span class="p">[[</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">word</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">sent</span> <span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_vec</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">]]</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sent</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="s1">'</span><span class="si">%s</span><span class="s1"> </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">warnings</span>
|
||||
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'No words in "</span><span class="si">%s</span><span class="s1">" have w2v vectors. Replacing </span><span class="se">\</span>
|
||||
<span class="s1"> by "</span><span class="si">%s</span><span class="s1"> </span><span class="si">%s</span><span class="s1">"..'</span> <span class="o">%</span> <span class="p">(</span><span class="n">sent</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bos</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos</span><span class="p">))</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">sent</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">():</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">enc_lstm</span><span class="p">(</span><span class="n">batch</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">output</span><span class="p">,</span> <span class="n">idxs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="c1"># output, idxs = output.squeeze(), idxs.squeeze()</span>
|
||||
<span class="n">idxs</span> <span class="o">=</span> <span class="n">idxs</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
||||
<span class="n">argmaxs</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">idxs</span> <span class="o">==</span> <span class="n">k</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sent</span><span class="p">[</span><span class="mi">0</span><span class="p">]))]</span>
|
||||
|
||||
<span class="c1"># visualize model</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sent</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mf">100.0</span> <span class="o">*</span> <span class="n">n</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">argmaxs</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">argmaxs</span><span class="p">]</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sent</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">bar</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">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'%'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Visualisation of words importance'</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">output</span><span class="p">,</span> <span class="n">idxs</span></div>
|
||||
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|
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<h1>Source code for transformations.transformation</h1><div class="highlight"><pre>
|
||||
<div class="viewcode-block" id="Transformation"><a class="viewcode-back" href="../../transformations/transformation.html#transformations.transformation.Transformation">[docs]</a><span></span><span class="k">class</span> <span class="nc">Transformation</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> An abstract class for transofrming a string of text to produce</span>
|
||||
<span class="sd"> a potential adversarial example. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">):</span>
|
||||
<span class="sd">""" Returns a list of all possible transformations for `text`."""</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
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</pre></div>
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|
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<h1>Source code for transformations.word_swap</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">.transformation</span> <span class="k">import</span> <span class="n">Transformation</span>
|
||||
<span class="kn">from</span> <span class="nn">nltk.corpus</span> <span class="k">import</span> <span class="n">stopwords</span>
|
||||
|
||||
<div class="viewcode-block" id="WordSwap"><a class="viewcode-back" href="../../transformations/word_swap.html#transformations.word_swap.WordSwap">[docs]</a><span class="k">class</span> <span class="nc">WordSwap</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> An abstract class that takes a sentence and transforms it by replacing</span>
|
||||
<span class="sd"> some of its words.</span>
|
||||
|
||||
<span class="sd"> Other classes can achieve this by inheriting from WordSwap and </span>
|
||||
<span class="sd"> overriding self._get_replacement_words.</span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> replace_stopwords(:obj:`bool`, optional): Whether to replace stopwords. Defaults to False. </span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">replace_stopwords</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">replace_stopwords</span> <span class="o">=</span> <span class="n">replace_stopwords</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">stopwords</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">stopwords</span><span class="o">.</span><span class="n">words</span><span class="p">(</span><span class="s1">'english'</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_replacement_words</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">):</span>
|
||||
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenized_text</span><span class="p">,</span> <span class="n">indices_to_replace</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns a list of all possible transformations for `text`.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> If indices_to_replace is set, only replaces words at those indices.</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">words</span> <span class="o">=</span> <span class="n">tokenized_text</span><span class="o">.</span><span class="n">words</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">indices_to_replace</span><span class="p">:</span>
|
||||
<span class="n">indices_to_replace</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">words</span><span class="p">)))</span>
|
||||
|
||||
<span class="n">transformations</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">word_swaps</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices_to_replace</span><span class="p">:</span>
|
||||
<span class="n">word_to_replace</span> <span class="o">=</span> <span class="n">words</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">replace_stopwords</span> <span class="ow">and</span> <span class="n">word_to_replace</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">stopwords</span><span class="p">:</span>
|
||||
<span class="k">continue</span>
|
||||
<span class="n">replacement_words</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_replacement_words</span><span class="p">(</span><span class="n">word_to_replace</span><span class="p">)</span>
|
||||
<span class="n">new_tokenized_texts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">replacement_words</span><span class="p">:</span>
|
||||
<span class="n">new_tokenized_texts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tokenized_text</span><span class="o">.</span><span class="n">replace_word_at_index</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
|
||||
<span class="n">transformations</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new_tokenized_texts</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">transformations</span></div>
|
||||
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|
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<h1>Source code for transformations.word_swap_counterfit</h1><div class="highlight"><pre>
|
||||
<span></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">os</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">textattack</span> <span class="k">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">utils</span>
|
||||
<span class="kn">from</span> <span class="nn">.word_swap</span> <span class="k">import</span> <span class="n">WordSwap</span>
|
||||
|
||||
<div class="viewcode-block" id="WordSwapCounterfit"><a class="viewcode-back" href="../../transformations/word_swap.html#transformations.word_swap_counterfit.WordSwapCounterfit">[docs]</a><span class="k">class</span> <span class="nc">WordSwapCounterfit</span><span class="p">(</span><span class="n">WordSwap</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Transforms an input by replacing its words with synonyms in the counter-fitted</span>
|
||||
<span class="sd"> embedding space. </span>
|
||||
|
||||
<span class="sd"> Args:</span>
|
||||
<span class="sd"> replace_stopwords (:obj:`bool`, optional): Whether to replace the stopwords in the text. Defaults to False. </span>
|
||||
<span class="sd"> max_candidates (:obj:`int`, optional): The default number of words to replace. Defaults to None. </span>
|
||||
<span class="sd"> word_embedding_folder (:obj:`str`, optional): The path to the word_embedding folder. Defaults to paragram_300_sl999</span>
|
||||
|
||||
<span class="sd"> Raises:</span>
|
||||
<span class="sd"> ValueError: If the word_embedding_folder is not found. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">PATH</span> <span class="o">=</span> <span class="s1">'/p/qdata/jm8wx/research/text_attacks/RobustNLP/AttackGeneration/word_embeddings/'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">replace_stopwords</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">max_candidates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">word_embedding_folder</span><span class="o">=</span><span class="s1">'paragram_300_sl999'</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">replace_stopwords</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_candidates</span> <span class="o">=</span> <span class="n">max_candidates</span>
|
||||
<span class="k">if</span> <span class="n">word_embedding_folder</span> <span class="o">==</span> <span class="s1">'paragram_300_sl999'</span><span class="p">:</span>
|
||||
<span class="n">word_embeddings_file</span> <span class="o">=</span> <span class="s1">'paragram_300_sl999.npy'</span>
|
||||
<span class="n">word_list_file</span> <span class="o">=</span> <span class="s1">'wordlist.pickle'</span>
|
||||
<span class="n">word_embedding_matrix_file</span> <span class="o">=</span> <span class="s1">'paragramnn.py'</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">f</span><span class="s1">'Could not find word embedding </span><span class="si">{word_embedding}</span><span class="s1">'</span><span class="p">)</span><span class="c1"># Concatenate folder names to create full path.</span>
|
||||
|
||||
<span class="n">utils</span><span class="o">.</span><span class="n">download_if_needed</span><span class="p">(</span><span class="n">WordSwapCounterfit</span><span class="o">.</span><span class="n">PATH</span><span class="p">)</span>
|
||||
<span class="n">word_embeddings_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">WordSwapCounterfit</span><span class="o">.</span><span class="n">PATH</span><span class="p">,</span> <span class="n">word_embedding_folder</span><span class="p">,</span> <span class="n">word_embeddings_file</span><span class="p">)</span>
|
||||
<span class="n">word_list_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">WordSwapCounterfit</span><span class="o">.</span><span class="n">PATH</span><span class="p">,</span> <span class="n">word_embedding_folder</span><span class="p">,</span> <span class="n">word_list_file</span><span class="p">)</span>
|
||||
<span class="n">word_embedding_matrix_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">WordSwapCounterfit</span><span class="o">.</span><span class="n">PATH</span><span class="p">,</span> <span class="n">word_embedding_folder</span><span class="p">,</span> <span class="n">word_embedding_matrix_file</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Actually load the files from disk.</span>
|
||||
<span class="n">word_embeddings</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">word_embeddings_file</span><span class="p">)</span>
|
||||
<span class="n">word_embedding_word2index</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">word_list_file</span><span class="p">,</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">word_embedding_matrix</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">word_embedding_matrix_file</span><span class="p">)</span>
|
||||
<span class="c1"># Build glove dict and index.</span>
|
||||
<span class="n">word_embedding_index2word</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">word</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">word_embedding_word2index</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="n">word_embedding_index2word</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">word</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_embeddings</span> <span class="o">=</span> <span class="n">word_embeddings</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">word_embedding_matrix</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_embedding_index2word</span> <span class="o">=</span> <span class="n">word_embedding_index2word</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">word_embedding_word2index</span> <span class="o">=</span> <span class="n">word_embedding_word2index</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_replacement_words</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="n">max_candidates</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
|
||||
<span class="sd">""" </span>
|
||||
<span class="sd"> Returns a list of possible 'candidate words' to replace a word in a sentence </span>
|
||||
<span class="sd"> or phrase. </span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> Based on nearest neighbors selected word embeddings.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_candidates</span><span class="p">:</span>
|
||||
<span class="n">max_candidates</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">max_candidates</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_candidates</span><span class="p">)</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">word_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">word_embedding_word2index</span><span class="p">[</span><span class="n">word</span><span class="p">]</span>
|
||||
<span class="n">nnids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">[</span><span class="n">word_id</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="n">max_candidates</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">candidate_words</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">wi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nnids</span><span class="p">):</span>
|
||||
<span class="n">candidate_words</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">word_embedding_index2word</span><span class="p">[</span><span class="n">wi</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">candidate_words</span>
|
||||
<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
|
||||
<span class="c1"># This word is not in our word embedding database, so return an empty list.</span>
|
||||
<span class="k">return</span> <span class="p">[]</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2019, UVA QData Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
||||
|
||||
</footer>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</section>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript">
|
||||
jQuery(function () {
|
||||
SphinxRtdTheme.Navigation.enable(true);
|
||||
});
|
||||
</script>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,6 +0,0 @@
|
||||
=====================
|
||||
Attack Documentation
|
||||
=====================
|
||||
|
||||
.. automodule:: attacks.attack
|
||||
:members:
|
||||
@@ -1,6 +0,0 @@
|
||||
===================
|
||||
Genetic Algorithm
|
||||
===================
|
||||
|
||||
.. automodule:: attacks.genetic_algorithm
|
||||
:members:
|
||||
@@ -1,9 +0,0 @@
|
||||
===================
|
||||
Greedy Word Swap
|
||||
===================
|
||||
|
||||
.. automodule:: attacks.greedy_word_swap
|
||||
:members:
|
||||
|
||||
.. automodule:: attacks.greedy_word_swap_wir
|
||||
:members:
|
||||
@@ -1,6 +0,0 @@
|
||||
============
|
||||
Constraints
|
||||
============
|
||||
|
||||
.. automodule:: constraints.constraint
|
||||
:members:
|
||||
@@ -1,9 +0,0 @@
|
||||
=========================
|
||||
Google Language Model
|
||||
=========================
|
||||
|
||||
.. automodule:: constraints.semantics.google_language_model.google_language_model
|
||||
:members:
|
||||
|
||||
.. automodule:: constraints.semantics.google_language_model.alzantot_goog_lm
|
||||
:members:
|
||||
@@ -1,11 +0,0 @@
|
||||
===========
|
||||
Semantics
|
||||
===========
|
||||
|
||||
Semantic constraints.
|
||||
|
||||
Universal Sentence Encoder
|
||||
##########################
|
||||
|
||||
.. automodule:: constraints.semantics.universal_sentence_encoder
|
||||
:members:
|
||||
@@ -1,9 +0,0 @@
|
||||
===================
|
||||
Built-in Datasets
|
||||
===================
|
||||
|
||||
Yelp Sentiment
|
||||
###############
|
||||
.. automodule:: datasets.yelp_sentiment
|
||||
:members:
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
======================
|
||||
User-Defined Datasets
|
||||
======================
|
||||
|
||||
.. automodule:: datasets.dataset
|
||||
:members:
|
||||
61
docs/_build/html/_sources/index.rst.txt
vendored
61
docs/_build/html/_sources/index.rst.txt
vendored
@@ -1,61 +0,0 @@
|
||||
.. TextAttack documentation master file, created by
|
||||
sphinx-quickstart on Sat Oct 19 20:54:30 2019.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to TextAttack's documentation!
|
||||
======================================
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: User Documentation
|
||||
|
||||
users/introduction
|
||||
users/installation
|
||||
users/examples
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Attack Documentation:
|
||||
|
||||
attacks/attack
|
||||
attacks/greedy_word_swap
|
||||
attacks/genetic_algorithm
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Models Documentation:
|
||||
|
||||
models/bert.rst
|
||||
models/infer_sent.rst
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Transformations Documentation:
|
||||
|
||||
transformations/transformation
|
||||
transformations/word_swap
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Constraints Documentation:
|
||||
|
||||
constraints/constraint
|
||||
constraints/semantics/semantics
|
||||
constraints/semantics/google_language_model
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Datasets:
|
||||
|
||||
datasets/built-in_datasets
|
||||
datasets/user_defined_datasets
|
||||
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
@@ -1,7 +0,0 @@
|
||||
=====
|
||||
BERT
|
||||
=====
|
||||
|
||||
.. automodule:: models.bert_for_sentiment_classification
|
||||
:members:
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
=========
|
||||
InferSent
|
||||
=========
|
||||
|
||||
Adapted from Facebook Research (https://arxiv.org/abs/1705.02364.)
|
||||
|
||||
.. automodule:: models.infer_sent
|
||||
:members:
|
||||
@@ -1,6 +0,0 @@
|
||||
================
|
||||
Transformation
|
||||
================
|
||||
|
||||
.. automodule:: transformations.transformation
|
||||
:members:
|
||||
@@ -1,10 +0,0 @@
|
||||
==========
|
||||
Word Swap
|
||||
==========
|
||||
|
||||
|
||||
.. automodule:: transformations.word_swap
|
||||
:members:
|
||||
|
||||
.. automodule:: transformations.word_swap_counterfit
|
||||
:members:
|
||||
26
docs/_build/html/_sources/users/examples.rst.txt
vendored
26
docs/_build/html/_sources/users/examples.rst.txt
vendored
@@ -1,26 +0,0 @@
|
||||
=========
|
||||
Examples
|
||||
=========
|
||||
|
||||
|
||||
BERT Example
|
||||
############
|
||||
|
||||
.. parsed-literal::
|
||||
model = BertForSentimentClassification()
|
||||
|
||||
transformation = WordSwapCounterfit()
|
||||
|
||||
attack = attacks.GreedyWordSwap(model, transformation)
|
||||
|
||||
attack.add_constraints(
|
||||
(
|
||||
constraints.semantics.UniversalSentenceEncoder(0.9, metric='cosine'),
|
||||
)
|
||||
)
|
||||
|
||||
yelp_data = YelpSentiment(n=2)
|
||||
|
||||
attack.add_output_file(open('outputs/test.txt', 'w'))
|
||||
|
||||
attack.attack(yelp_data, shuffle=False)
|
||||
@@ -1,5 +0,0 @@
|
||||
==============
|
||||
Installation
|
||||
==============
|
||||
|
||||
TextAttack must use Python >=3.6
|
||||
@@ -1,5 +0,0 @@
|
||||
=====================
|
||||
What is TextAttack?
|
||||
=====================
|
||||
|
||||
TextAttack is a Python package...
|
||||
764
docs/_build/html/_static/basic.css
vendored
764
docs/_build/html/_static/basic.css
vendored
@@ -1,764 +0,0 @@
|
||||
/*
|
||||
* basic.css
|
||||
* ~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/* -- main layout ----------------------------------------------------------- */
|
||||
|
||||
div.clearer {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
/* -- relbar ---------------------------------------------------------------- */
|
||||
|
||||
div.related {
|
||||
width: 100%;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
div.related h3 {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
margin: 0;
|
||||
padding: 0 0 0 10px;
|
||||
list-style: none;
|
||||
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|
||||
|
||||
div.related li {
|
||||
display: inline;
|
||||
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|
||||
|
||||
div.related li.right {
|
||||
float: right;
|
||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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div.sphinxsidebar ul ul,
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div.sphinxsidebar ul.want-points {
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margin-left: 20px;
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div.sphinxsidebar #searchbox form.search {
|
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|
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|
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|
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|
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|
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|
||||
img {
|
||||
border: 0;
|
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|
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|
||||
|
||||
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|
||||
|
||||
ul.search {
|
||||
margin: 10px 0 0 20px;
|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
ul.search li a {
|
||||
font-weight: bold;
|
||||
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|
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|
||||
ul.search li div.context {
|
||||
color: #888;
|
||||
margin: 2px 0 0 30px;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
ul.keywordmatches li.goodmatch a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- index page ------------------------------------------------------------ */
|
||||
|
||||
table.contentstable {
|
||||
width: 90%;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
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}
|
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|
||||
table.contentstable p.biglink {
|
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line-height: 150%;
|
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|
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|
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|
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font-size: 1.3em;
|
||||
}
|
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|
||||
span.linkdescr {
|
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font-style: italic;
|
||||
padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
table.indextable td {
|
||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
table.indextable ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
|
||||
|
||||
table.indextable > tbody > tr > td > ul {
|
||||
padding-left: 0em;
|
||||
}
|
||||
|
||||
table.indextable tr.pcap {
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
table.indextable tr.cap {
|
||||
margin-top: 10px;
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
|
||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
/* -- domain module index --------------------------------------------------- */
|
||||
|
||||
table.modindextable td {
|
||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body {
|
||||
min-width: 450px;
|
||||
max-width: 800px;
|
||||
}
|
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
a.brackets:before,
|
||||
span.brackets > a:before{
|
||||
content: "[";
|
||||
}
|
||||
|
||||
a.brackets:after,
|
||||
span.brackets > a:after {
|
||||
content: "]";
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
img.align-default, .figure.align-default {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-default {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px 7px 0 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px 7px 0 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.admonition dl {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table.align-center {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.align-default {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
th > p:first-child,
|
||||
td > p:first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
th > p:last-child,
|
||||
td > p:last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */
|
||||
|
||||
table.hlist td {
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
li > p:first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
li > p:last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
dl.footnote > dt,
|
||||
dl.citation > dt {
|
||||
float: left;
|
||||
}
|
||||
|
||||
dl.footnote > dd,
|
||||
dl.citation > dd {
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl.footnote > dd:after,
|
||||
dl.citation > dd:after {
|
||||
content: "";
|
||||
clear: both;
|
||||
}
|
||||
|
||||
dl.field-list {
|
||||
display: grid;
|
||||
grid-template-columns: fit-content(30%) auto;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
font-weight: bold;
|
||||
word-break: break-word;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
dl.field-list > dt:after {
|
||||
content: ":";
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
padding-left: 0.5em;
|
||||
margin-top: 0em;
|
||||
margin-left: 0em;
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd > p:first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dt:target, span.highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
rect.highlighted {
|
||||
fill: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
.sig-paren {
|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.system-message {
|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
.classifier:before {
|
||||
font-style: normal;
|
||||
margin: 0.5em;
|
||||
content: ":";
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
padding: 5px 0px;
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
margin-left: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
padding: 0 0.5em 0 0.5em;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
div.code-block-caption + div > div.highlight > pre {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
padding: 1em 1em 0;
|
||||
}
|
||||
|
||||
div.literal-block-wrapper div.highlight {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: relative;
|
||||
left: 0px;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
1
docs/_build/html/_static/css/badge_only.css
vendored
1
docs/_build/html/_static/css/badge_only.css
vendored
@@ -1 +0,0 @@
|
||||
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docs/_build/html/_static/doctools.js
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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|
||||
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|
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|
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|
||||
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||||
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||||
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|
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/(msie) ([\w.]+)/.exec(ua) ||
|
||||
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|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
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|
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|
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|
||||
jQuery.browser = {};
|
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jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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||||
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|
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|
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|
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|
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||||
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|
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|
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||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
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|
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/* sphinx_rtd_theme version 0.4.3 | MIT license */
|
||||
/* Built 20190212 16:02 */
|
||||
require=function r(s,a,l){function c(e,n){if(!a[e]){if(!s[e]){var i="function"==typeof require&&require;if(!n&&i)return i(e,!0);if(u)return u(e,!0);var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}var o=a[e]={exports:{}};s[e][0].call(o.exports,function(n){return c(s[e][1][n]||n)},o,o.exports,r,s,a,l)}return a[e].exports}for(var u="function"==typeof require&&require,n=0;n<l.length;n++)c(l[n]);return c}({"sphinx-rtd-theme":[function(n,e,i){var jQuery="undefined"!=typeof window?window.jQuery:n("jquery");e.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(e){var i=this;void 0===e&&(e=!0),i.isRunning||(i.isRunning=!0,jQuery(function(n){i.init(n),i.reset(),i.win.on("hashchange",i.reset),e&&i.win.on("scroll",function(){i.linkScroll||i.winScroll||(i.winScroll=!0,requestAnimationFrame(function(){i.onScroll()}))}),i.win.on("resize",function(){i.winResize||(i.winResize=!0,requestAnimationFrame(function(){i.onResize()}))}),i.onResize()}))},enableSticky:function(){this.enable(!0)},init:function(i){i(document);var t=this;this.navBar=i("div.wy-side-scroll:first"),this.win=i(window),i(document).on("click","[data-toggle='wy-nav-top']",function(){i("[data-toggle='wy-nav-shift']").toggleClass("shift"),i("[data-toggle='rst-versions']").toggleClass("shift")}).on("click",".wy-menu-vertical .current ul li a",function(){var n=i(this);i("[data-toggle='wy-nav-shift']").removeClass("shift"),i("[data-toggle='rst-versions']").toggleClass("shift"),t.toggleCurrent(n),t.hashChange()}).on("click","[data-toggle='rst-current-version']",function(){i("[data-toggle='rst-versions']").toggleClass("shift-up")}),i("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),i("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),i("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),i(".wy-menu-vertical ul").not(".simple").siblings("a").each(function(){var e=i(this);expand=i('<span class="toctree-expand"></span>'),expand.on("click",function(n){return t.toggleCurrent(e),n.stopPropagation(),!1}),e.prepend(expand)})},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),i=e.find('[href="'+n+'"]');if(0===i.length){var t=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(i=e.find('[href="#'+t.attr("id")+'"]')).length&&(i=e.find('[href="#"]'))}0<i.length&&($(".wy-menu-vertical .current").removeClass("current"),i.addClass("current"),i.closest("li.toctree-l1").addClass("current"),i.closest("li.toctree-l1").parent().addClass("current"),i.closest("li.toctree-l1").addClass("current"),i.closest("li.toctree-l2").addClass("current"),i.closest("li.toctree-l3").addClass("current"),i.closest("li.toctree-l4").addClass("current"),i[0].scrollIntoView())}catch(o){console.log("Error expanding nav for anchor",o)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,i=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(i),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",function(){this.linkScroll=!1})},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current"),e.siblings().find("li.current").removeClass("current"),e.find("> ul li.current").removeClass("current"),e.toggleClass("current")}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:e.exports.ThemeNav,StickyNav:e.exports.ThemeNav}),function(){for(var r=0,n=["ms","moz","webkit","o"],e=0;e<n.length&&!window.requestAnimationFrame;++e)window.requestAnimationFrame=window[n[e]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[n[e]+"CancelAnimationFrame"]||window[n[e]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(n,e){var i=(new Date).getTime(),t=Math.max(0,16-(i-r)),o=window.setTimeout(function(){n(i+t)},t);return r=i+t,o}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()},{jquery:"jquery"}]},{},["sphinx-rtd-theme"]);
|
||||
297
docs/_build/html/_static/language_data.js
vendored
297
docs/_build/html/_static/language_data.js
vendored
@@ -1,297 +0,0 @@
|
||||
/*
|
||||
* language_data.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
|
||||
|
||||
|
||||
/* Non-minified version JS is _stemmer.js if file is provided */
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() {
|
||||
var result = {};
|
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
|
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
|
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
|
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
|
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
|
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
|
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
|
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
|
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
|
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
|
||||
var i, j, start, end;
|
||||
for (i = 0; i < singles.length; i++) {
|
||||
result[singles[i]] = true;
|
||||
}
|
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
|
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
|
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
|
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
|
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
|
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
|
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
|
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
|
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
|
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
|
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
|
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
|
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
|
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
|
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
|
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
|
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
|
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
|
||||
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
|
||||
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
|
||||
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
|
||||
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
|
||||
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
|
||||
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
|
||||
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
|
||||
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
|
||||
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
|
||||
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
|
||||
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
|
||||
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
|
||||
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
|
||||
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
|
||||
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
|
||||
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
|
||||
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
|
||||
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
|
||||
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
|
||||
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
|
||||
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
|
||||
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
|
||||
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
|
||||
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
|
||||
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
|
||||
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
|
||||
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
|
||||
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
|
||||
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
|
||||
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
|
||||
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
|
||||
for (i = 0; i < ranges.length; i++) {
|
||||
start = ranges[i][0];
|
||||
end = ranges[i][1];
|
||||
for (j = start; j <= end; j++) {
|
||||
result[j] = true;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
})();
|
||||
|
||||
function splitQuery(query) {
|
||||
var result = [];
|
||||
var start = -1;
|
||||
for (var i = 0; i < query.length; i++) {
|
||||
if (splitChars[query.charCodeAt(i)]) {
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start, i));
|
||||
start = -1;
|
||||
}
|
||||
} else if (start === -1) {
|
||||
start = i;
|
||||
}
|
||||
}
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
BIN
docs/_build/html/_static/minus.png
vendored
BIN
docs/_build/html/_static/minus.png
vendored
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|
Before Width: | Height: | Size: 90 B |
BIN
docs/_build/html/_static/plus.png
vendored
BIN
docs/_build/html/_static/plus.png
vendored
Binary file not shown.
|
Before Width: | Height: | Size: 90 B |
69
docs/_build/html/_static/pygments.css
vendored
69
docs/_build/html/_static/pygments.css
vendored
@@ -1,69 +0,0 @@
|
||||
.highlight .hll { background-color: #ffffcc }
|
||||
.highlight { background: #f8f8f8; }
|
||||
.highlight .c { color: #408080; font-style: italic } /* Comment */
|
||||
.highlight .err { border: 1px solid #FF0000 } /* Error */
|
||||
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
|
||||
.highlight .o { color: #666666 } /* Operator */
|
||||
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
|
||||
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
|
||||
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
|
||||
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
|
||||
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
|
||||
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
|
||||
.highlight .gd { color: #A00000 } /* Generic.Deleted */
|
||||
.highlight .ge { font-style: italic } /* Generic.Emph */
|
||||
.highlight .gr { color: #FF0000 } /* Generic.Error */
|
||||
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
|
||||
.highlight .gi { color: #00A000 } /* Generic.Inserted */
|
||||
.highlight .go { color: #888888 } /* Generic.Output */
|
||||
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
|
||||
.highlight .gs { font-weight: bold } /* Generic.Strong */
|
||||
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
|
||||
.highlight .gt { color: #0044DD } /* Generic.Traceback */
|
||||
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
|
||||
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
|
||||
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
|
||||
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
|
||||
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
|
||||
.highlight .kt { color: #B00040 } /* Keyword.Type */
|
||||
.highlight .m { color: #666666 } /* Literal.Number */
|
||||
.highlight .s { color: #BA2121 } /* Literal.String */
|
||||
.highlight .na { color: #7D9029 } /* Name.Attribute */
|
||||
.highlight .nb { color: #008000 } /* Name.Builtin */
|
||||
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
|
||||
.highlight .no { color: #880000 } /* Name.Constant */
|
||||
.highlight .nd { color: #AA22FF } /* Name.Decorator */
|
||||
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
|
||||
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
|
||||
.highlight .nf { color: #0000FF } /* Name.Function */
|
||||
.highlight .nl { color: #A0A000 } /* Name.Label */
|
||||
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
|
||||
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
|
||||
.highlight .nv { color: #19177C } /* Name.Variable */
|
||||
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
|
||||
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
|
||||
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
|
||||
.highlight .mf { color: #666666 } /* Literal.Number.Float */
|
||||
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
|
||||
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
|
||||
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
|
||||
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
|
||||
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
|
||||
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
|
||||
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
|
||||
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
|
||||
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
|
||||
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
|
||||
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
|
||||
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
|
||||
.highlight .sx { color: #008000 } /* Literal.String.Other */
|
||||
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
|
||||
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
|
||||
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
|
||||
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
|
||||
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
|
||||
.highlight .vc { color: #19177C } /* Name.Variable.Class */
|
||||
.highlight .vg { color: #19177C } /* Name.Variable.Global */
|
||||
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
|
||||
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
|
||||
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user