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<!DOCTYPE html>
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<h1>Source code for catsim.selection</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="k">import</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">warnings</span> <span class="k">import</span> <span class="n">warn</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="k">import</span> <span class="n">quad</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">irt</span>
<span class="kn">from</span> <span class="nn">.simulation</span> <span class="k">import</span> <span class="n">Selector</span><span class="p">,</span> <span class="n">FiniteSelector</span>
<span class="k">def</span> <span class="nf">_nearest</span><span class="p">(</span><span class="n">array</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="sd">"""Returns the indexes of values in `array` that are closest to `value`</span>
<span class="sd"> :param array: an array of numeric values</span>
<span class="sd"> :param value: a numerical value</span>
<span class="sd"> :return: an array containing the indexes of numbers in `array`,</span>
<span class="sd"> according to how close their are to `value`</span>
<span class="sd"> """</span>
<span class="n">array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">array</span> <span class="o">-</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
<div class="viewcode-block" id="MaxInfoSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.MaxInfoSelector">[docs]</a><span class="k">class</span> <span class="nc">MaxInfoSelector</span><span class="p">(</span><span class="n">Selector</span><span class="p">):</span>
<span class="sd">"""Selector that returns the first non-administered item with maximum information, given an estimated theta</span>
<span class="sd"> </span>
<span class="sd"> :param r_max: maximum exposure rate for items</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">r_max</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">_r_max</span> <span class="o">=</span> <span class="n">r_max</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">'Maximum Information Selector'</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">r_max</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">_r_max</span>
<div class="viewcode-block" id="MaxInfoSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.MaxInfoSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">est_theta</span><span class="p">:</span> <span class="nb">float</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :param est_theta: a float containing the current estimated proficiency</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more items in the item bank.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">est_theta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">latest_estimations</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="c1"># first, we'll order items by their information value</span>
<span class="k">if</span> <span class="n">irt</span><span class="o">.</span><span class="n">detect_model</span><span class="p">(</span><span class="n">items</span><span class="p">)</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">:</span>
<span class="c1"># when the logistic model has the number of parameters <= 2,</span>
<span class="c1"># all items have highest information where theta = b</span>
<span class="n">ordered_items</span> <span class="o">=</span> <span class="n">_nearest</span><span class="p">(</span><span class="n">items</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">est_theta</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># else, we'll have to calculate the theta value where information is maximum</span>
<span class="n">inf_values</span> <span class="o">=</span> <span class="n">irt</span><span class="o">.</span><span class="n">max_info_hpc</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
<span class="n">ordered_items</span> <span class="o">=</span> <span class="n">_nearest</span><span class="p">(</span><span class="n">inf_values</span><span class="p">,</span> <span class="n">est_theta</span><span class="p">)</span>
<span class="n">valid_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ordered_items</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_indexes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span><span class="s1">'There are no more items to be applied.'</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="c1"># gets the indexes and information values from the items with r < rmax</span>
<span class="n">valid_indexes_low_r</span> <span class="o">=</span> <span class="p">[</span><span class="n">index</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">valid_indexes</span> <span class="k">if</span> <span class="n">items</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_r_max</span><span class="p">]</span>
<span class="c1"># return the item with maximum information from the ones available</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_indexes_low_r</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="n">valid_indexes_low_r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="n">valid_indexes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">selected_item</span></div></div>
<div class="viewcode-block" id="UrrySelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.UrrySelector">[docs]</a><span class="k">class</span> <span class="nc">UrrySelector</span><span class="p">(</span><span class="n">Selector</span><span class="p">):</span>
<span class="sd">"""Selector that returns the item whose difficulty parameter is closest to the examinee's proficiency"""</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="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</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">'Urry Selector'</span>
<div class="viewcode-block" id="UrrySelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.UrrySelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">est_theta</span><span class="p">:</span> <span class="nb">float</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :param est_theta: a float containing the current estimated proficiency</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more items in the item bank.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">est_theta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">latest_estimations</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">ordered_items</span> <span class="o">=</span> <span class="n">_nearest</span><span class="p">(</span><span class="n">items</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">est_theta</span><span class="p">)</span>
<span class="n">valid_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ordered_items</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_indexes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span><span class="s1">'There are no more items to be applied.'</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">valid_indexes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div></div>
<div class="viewcode-block" id="LinearSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.LinearSelector">[docs]</a><span class="k">class</span> <span class="nc">LinearSelector</span><span class="p">(</span><span class="n">FiniteSelector</span><span class="p">):</span>
<span class="sd">"""Selector that returns item indexes in a linear order, simulating a standard</span>
<span class="sd"> (non-adaptive) test.</span>
<span class="sd"> :param indexes: the indexes of the items that will be returned in order"""</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">'Linear Selector'</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">indexes</span><span class="p">:</span> <span class="nb">list</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="nb">len</span><span class="p">(</span><span class="n">indexes</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_indexes</span> <span class="o">=</span> <span class="n">indexes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_current</span> <span class="o">=</span> <span class="mi">0</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">indexes</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">_indexes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">current</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">_current</span>
<div class="viewcode-block" id="LinearSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.LinearSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more items in the item bank.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_indexes</span><span class="p">)</span> <span class="o"><=</span> <span class="nb">set</span><span class="p">(</span><span class="n">administered_items</span><span class="p">):</span>
<span class="n">warn</span><span class="p">(</span>
<span class="s1">'A new index was asked for, but there are no more item indexes to present.</span><span class="se">\n</span><span class="s1">Current item:</span><span class="se">\t\t\t</span><span class="si">{0}</span><span class="se">\n</span><span class="s1">Items to be administered:</span><span class="se">\t</span><span class="si">{1}</span><span class="s1"> (size: </span><span class="si">{2}</span><span class="s1">)</span><span class="se">\n</span><span class="s1">Administered items:</span><span class="se">\t\t</span><span class="si">{3}</span><span class="s1"> (size: </span><span class="si">{4}</span><span class="s1">)'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_current</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_indexes</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">_indexes</span><span class="p">),</span>
<span class="nb">sorted</span><span class="p">(</span><span class="n">administered_items</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_indexes</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">selected_item</span></div></div>
<div class="viewcode-block" id="RandomSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.RandomSelector">[docs]</a><span class="k">class</span> <span class="nc">RandomSelector</span><span class="p">(</span><span class="n">Selector</span><span class="p">):</span>
<span class="sd">"""Selector that randomly selects items for application.</span>
<span class="sd"> :param replace: whether to select an item that has already been selected before for this examinee."""</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">'Random Selector'</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</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="nb">super</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">_replace</span> <span class="o">=</span> <span class="n">replace</span>
<div class="viewcode-block" id="RandomSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.RandomSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more items in the item bank.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span>
<span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)</span> <span class="o">>=</span> <span class="n">items</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="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_replace</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span>
<span class="s1">'A new item was asked for, but there are no more items to present.</span><span class="se">\n</span><span class="s1">Administered items:</span><span class="se">\t</span><span class="si">{0}</span><span class="se">\n</span><span class="s1">Item bank size:</span><span class="se">\t</span><span class="si">{1}</span><span class="s1">'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">),</span> <span class="n">items</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="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_replace</span><span class="p">:</span>
<span class="k">return</span> <span class="n">numpy</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">items</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">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">numpy</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="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">items</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="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)))</span></div></div>
<div class="viewcode-block" id="ClusterSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector">[docs]</a><span class="k">class</span> <span class="nc">ClusterSelector</span><span class="p">(</span><span class="n">Selector</span><span class="p">):</span>
<span class="sd">"""Cluster-based Item Selection Method.</span>
<span class="sd"> .. [Men15] Meneghetti, D. R. (2015). Metolodogia de seleção de itens em testes</span>
<span class="sd"> adaptativos informatizados baseada em agrupamento por similaridade (Mestrado).</span>
<span class="sd"> Centro Universitário da FEI. Retrieved from</span>
<span class="sd"> https://www.researchgate.net/publication/283944553_Metodologia_de_selecao_de_itens_em_Testes_Adaptativos_Informatizados_baseada_em_Agrupamento_por_Similaridade</span>
<span class="sd"> :param clusters: a list containing item cluster memberships</span>
<span class="sd"> :param r_max: maximum exposure rate for items</span>
<span class="sd"> :param method: one of the available methods for cluster selection. Given</span>
<span class="sd"> the estimated theta value at each step:</span>
<span class="sd"> ``item_info``: selects the cluster which has the item</span>
<span class="sd"> with maximum information;</span>
<span class="sd"> ``cluster_info``: selects the cluster whose items sum of</span>
<span class="sd"> information is maximum;</span>
<span class="sd"> ``weighted_info``: selects the cluster whose weighted</span>
<span class="sd"> sum of information is maximum. The weighted equals the</span>
<span class="sd"> number of items in the cluster;</span>
<span class="sd"> :param r_control: if `passive` and all items :math:`i` in the selected</span>
<span class="sd"> cluster have :math:`r_i > r^{max}`, applies the item with</span>
<span class="sd"> maximum information; if `aggressive`, applies the item</span>
<span class="sd"> with smallest :math:`r` value.</span>
<span class="sd"> """</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">'Cluster Selector'</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">r_max</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">_r_max</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">clusters</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">_clusters</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">method</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">_method</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">r_control</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">_r_control</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">clusters</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span>
<span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">'item_info'</span><span class="p">,</span>
<span class="n">r_max</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">r_control</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">'passive'</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">available_methods</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'item_info'</span><span class="p">,</span> <span class="s1">'cluster_info'</span><span class="p">,</span> <span class="s1">'weighted_info'</span><span class="p">]</span>
<span class="k">if</span> <span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">available_methods</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'</span><span class="si">{0}</span><span class="s1"> is not a valid cluster selection method; choose one from </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">method</span><span class="p">,</span> <span class="n">available_methods</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">available_rcontrol</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'passive'</span><span class="p">,</span> <span class="s1">'aggressive'</span><span class="p">]</span>
<span class="k">if</span> <span class="n">r_control</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">available_rcontrol</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'</span><span class="si">{0}</span><span class="s1"> is not a valid item exposure control method; choose one from </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">r_control</span><span class="p">,</span> <span class="n">available_rcontrol</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span> <span class="o">=</span> <span class="n">clusters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_method</span> <span class="o">=</span> <span class="n">method</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_r_max</span> <span class="o">=</span> <span class="n">r_max</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_r_control</span> <span class="o">=</span> <span class="n">r_control</span>
<div class="viewcode-block" id="ClusterSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">est_theta</span><span class="p">:</span> <span class="nb">float</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :param est_theta: a float containing the current estimated proficiency</span>
<span class="sd"> :returns: index of the next item to be applied.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">est_theta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">latest_estimations</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">selected_cluster</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">existent_clusters</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">)</span>
<span class="c1"># this part of the code selects the cluster from which the item at</span>
<span class="c1"># the current point of the test will be chosen</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_method</span> <span class="o">==</span> <span class="s1">'item_info'</span><span class="p">:</span>
<span class="c1"># get the item indexes sorted by their information value</span>
<span class="n">infos</span> <span class="o">=</span> <span class="n">_nearest</span><span class="p">(</span><span class="n">irt</span><span class="o">.</span><span class="n">max_info_hpc</span><span class="p">(</span><span class="n">items</span><span class="p">),</span> <span class="n">est_theta</span><span class="p">)</span>
<span class="n">evaluated_clusters</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="c1"># iterate over every item in order of information value</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">items</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="c1"># get the current non-examined item</span>
<span class="n">max_info_item</span> <span class="o">=</span> <span class="n">infos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="c1"># if the cluster of the current item has already been fully examined, go to the next item</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">[</span><span class="n">max_info_item</span><span class="p">]</span> <span class="ow">in</span> <span class="n">evaluated_clusters</span><span class="p">:</span>
<span class="k">continue</span>
<span class="c1"># get the indexes of all items in the same cluster as the current item</span>
<span class="n">items_in_cluster</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span>
<span class="p">[</span><span class="n">x</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">[</span><span class="n">max_info_item</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">]</span>
<span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># if all the items in the current cluster have already been administered (it happens, theoretically),</span>
<span class="c1"># add this cluster to the list of fully evaluated clusters</span>
<span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">items_in_cluster</span><span class="p">)</span> <span class="o"><=</span> <span class="nb">set</span><span class="p">(</span><span class="n">administered_items</span><span class="p">):</span>
<span class="n">evaluated_clusters</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">[</span><span class="n">max_info_item</span><span class="p">])</span>
<span class="c1"># if all clusters have been evaluated, the loop ends and the method returns None somewhere below</span>
<span class="k">if</span> <span class="n">evaluated_clusters</span> <span class="o">==</span> <span class="n">existent_clusters</span><span class="p">:</span>
<span class="k">break</span>
<span class="c1"># else, if there are still items and clusters to be explored, keep going</span>
<span class="k">continue</span>
<span class="c1"># if the algorithm gets here, it means this cluster can be used</span>
<span class="n">selected_cluster</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">[</span><span class="n">max_info_item</span><span class="p">]</span>
<span class="k">break</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'cluster_info'</span><span class="p">,</span> <span class="s1">'weighted_info'</span><span class="p">]:</span>
<span class="c1"># calculates the cluster information, depending on the method</span>
<span class="c1"># selected</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_method</span> <span class="o">==</span> <span class="s1">'cluster_info'</span><span class="p">:</span>
<span class="n">cluster_infos</span> <span class="o">=</span> <span class="n">ClusterSelector</span><span class="o">.</span><span class="n">sum_cluster_infos</span><span class="p">(</span><span class="n">est_theta</span><span class="p">,</span> <span class="n">items</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cluster_infos</span> <span class="o">=</span> <span class="n">ClusterSelector</span><span class="o">.</span><span class="n">weighted_cluster_infos</span><span class="p">(</span>
<span class="n">est_theta</span><span class="p">,</span> <span class="n">items</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span>
<span class="p">)</span>
<span class="c1"># sorts clusters descending by their information values</span>
<span class="c1"># this type of sorting was seem on</span>
<span class="c1"># http://stackoverflow.com/a/6618543</span>
<span class="n">sorted_clusters</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">cluster</span> <span class="k">for</span> <span class="p">(</span><span class="n">inf_value</span><span class="p">,</span> <span class="n">cluster</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span>
<span class="nb">zip</span><span class="p">(</span><span class="n">cluster_infos</span><span class="p">,</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">)),</span>
<span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">pair</span><span class="p">:</span> <span class="n">pair</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">reverse</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
<span class="p">],</span>
<span class="n">dtype</span><span class="o">=</span><span class="nb">float</span>
<span class="p">)</span>
<span class="c1"># walks through the sorted clusters in order</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">sorted_clusters</span><span class="p">)):</span>
<span class="n">valid_indexes</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">([</span><span class="n">r</span> <span class="o">==</span> <span class="n">sorted_clusters</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">items</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]])[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># checks if at least one item from this cluster has not</span>
<span class="c1"># been administered to this examinee yet</span>
<span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">valid_indexes</span><span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">valid_indexes</span><span class="p">):</span>
<span class="n">selected_cluster</span> <span class="o">=</span> <span class="n">sorted_clusters</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">break</span>
<span class="c1"># the for loop ends with the cluster that has a) the maximum</span>
<span class="c1"># information possible and b) at least one item that has not</span>
<span class="c1"># yet been administered</span>
<span class="c1"># if the test size gets larger than the item bank size, end the test</span>
<span class="k">if</span> <span class="n">selected_cluster</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span><span class="s2">"There are no more items to be applied."</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="c1"># in this part, an item is chosen from the cluster that was</span>
<span class="c1"># selected above</span>
<span class="c1"># gets the indexes and information values from the items in the</span>
<span class="c1"># selected cluster that have not been administered</span>
<span class="n">valid_indexes</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">index</span>
<span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">([</span><span class="n">cluster</span> <span class="o">==</span> <span class="n">selected_cluster</span>
<span class="k">for</span> <span class="n">cluster</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clusters</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span>
<span class="p">]</span>
<span class="c1"># gets the indexes and information values from the items in the</span>
<span class="c1"># selected cluster with r < rmax that have not been</span>
<span class="c1"># administered</span>
<span class="n">valid_indexes_low_r</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">index</span> <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">valid_indexes</span>
<span class="k">if</span> <span class="n">items</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_r_max</span> <span class="ow">and</span> <span class="n">index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span>
<span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_indexes_low_r</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># return the item with maximum information from the ones available</span>
<span class="n">inf_values</span> <span class="o">=</span> <span class="n">irt</span><span class="o">.</span><span class="n">inf_hpc</span><span class="p">(</span><span class="n">est_theta</span><span class="p">,</span> <span class="n">items</span><span class="p">[</span><span class="n">valid_indexes_low_r</span><span class="p">])</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="n">valid_indexes_low_r</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">inf_values</span> <span class="o">==</span> <span class="nb">max</span><span class="p">(</span><span class="n">inf_values</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="c1"># if all items in the selected cluster have exceed their r values,</span>
<span class="c1"># select the one with smallest r, regardless of information</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_r_control</span> <span class="o">==</span> <span class="s1">'passive'</span><span class="p">:</span>
<span class="n">inf_values</span> <span class="o">=</span> <span class="n">irt</span><span class="o">.</span><span class="n">inf_hpc</span><span class="p">(</span><span class="n">est_theta</span><span class="p">,</span> <span class="n">items</span><span class="p">[</span><span class="n">valid_indexes</span><span class="p">])</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="n">valid_indexes</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">inf_values</span> <span class="o">==</span> <span class="nb">max</span><span class="p">(</span><span class="n">inf_values</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">else</span><span class="p">:</span>
<span class="n">selected_item</span> <span class="o">=</span> <span class="n">valid_indexes</span><span class="p">[</span><span class="n">items</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">items</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]))]</span>
<span class="k">return</span> <span class="n">selected_item</span></div>
<div class="viewcode-block" id="ClusterSelector.sum_cluster_infos"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector.sum_cluster_infos">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sum_cluster_infos</span><span class="p">(</span><span class="n">theta</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">clusters</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="sd">"""Returns the sum of item information values, separated by cluster</span>
<span class="sd"> :param theta: an examinee's :math:`\\theta` value</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param clusters: a list containing item cluster memberships, represented by integers</span>
<span class="sd"> :returns: array containing the sum of item information values for each cluster"""</span>
<span class="n">cluster_infos</span> <span class="o">=</span> <span class="n">numpy</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="nb">set</span><span class="p">(</span><span class="n">clusters</span><span class="p">))))</span>
<span class="k">for</span> <span class="n">cluster</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">(</span><span class="n">clusters</span><span class="p">):</span>
<span class="n">cluster_indexes</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">nonzero</span><span class="p">([</span><span class="n">c</span> <span class="o">==</span> <span class="n">cluster</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">items</span><span class="p">[</span><span class="n">cluster_indexes</span><span class="p">]:</span>
<span class="n">cluster_infos</span><span class="p">[</span><span class="n">cluster</span><span class="p">]</span> <span class="o">+=</span> <span class="n">irt</span><span class="o">.</span><span class="n">inf</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">item</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">item</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="k">return</span> <span class="n">cluster_infos</span></div>
<div class="viewcode-block" id="ClusterSelector.weighted_cluster_infos"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector.weighted_cluster_infos">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">weighted_cluster_infos</span><span class="p">(</span><span class="n">theta</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">clusters</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="sd">"""Returns the weighted sum of item information values, separated by cluster.</span>
<span class="sd"> The weight is the number of items in each cluster.</span>
<span class="sd"> :param theta: an examinee's :math:`\\theta` value</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param clusters: a list containing item cluster memberships, represented by integers</span>
<span class="sd"> :returns: array containing the sum of item information values for each cluster,</span>
<span class="sd"> divided by the number of items in each cluster"""</span>
<span class="n">cluster_infos</span> <span class="o">=</span> <span class="n">ClusterSelector</span><span class="o">.</span><span class="n">sum_cluster_infos</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">items</span><span class="p">,</span> <span class="n">clusters</span><span class="p">)</span>
<span class="n">count</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">clusters</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">cluster_infos</span><span class="p">)):</span>
<span class="n">cluster_infos</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/=</span> <span class="n">count</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">cluster_infos</span></div>
<div class="viewcode-block" id="ClusterSelector.sum_cluster_params"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector.sum_cluster_params">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sum_cluster_params</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
<span class="sd">"""Returns the sum of item parameter values for each cluster</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param c: a list containing clustering memeberships.</span>
<span class="sd"> :returns: a matrix containing the sum of each parameter by cluster. Lines are clusters, columns are parameters.</span>
<span class="sd"> """</span>
<span class="n">averages</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">c</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">averages</span><span class="p">[</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">averages</span><span class="p">[</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">averages</span><span class="p">[</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">averages</span><span class="p">[</span><span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="k">return</span> <span class="n">averages</span></div>
<div class="viewcode-block" id="ClusterSelector.avg_cluster_params"><a class="viewcode-back" href="../../selection.html#catsim.selection.ClusterSelector.avg_cluster_params">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">avg_cluster_params</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
<span class="sd">"""Returns the average values of item parameters by cluster</span>
<span class="sd"> :param items:</span>
<span class="sd"> :param c: a list containing clustering memeberships.</span>
<span class="sd"> :returns: a matrix containing the average values of each parameter by cluster.</span>
<span class="sd"> Lines are clusters, columns are parameters."""</span>
<span class="n">averages</span> <span class="o">=</span> <span class="n">ClusterSelector</span><span class="o">.</span><span class="n">sum_cluster_params</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
<span class="n">occurrences</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">c</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">int64</span><span class="p">))</span>
<span class="k">for</span> <span class="n">counter</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">occurrences</span><span class="p">):</span>
<span class="n">averages</span><span class="p">[</span><span class="n">counter</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/=</span> <span class="n">i</span>
<span class="n">averages</span><span class="p">[</span><span class="n">counter</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/=</span> <span class="n">i</span>
<span class="n">averages</span><span class="p">[</span><span class="n">counter</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/=</span> <span class="n">i</span>
<span class="n">averages</span><span class="p">[</span><span class="n">counter</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">/=</span> <span class="n">i</span>
<span class="k">return</span> <span class="n">averages</span></div></div>
<div class="viewcode-block" id="StratifiedSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.StratifiedSelector">[docs]</a><span class="k">class</span> <span class="nc">StratifiedSelector</span><span class="p">(</span><span class="n">FiniteSelector</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">'General Stratified Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_organized_items</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nd">@staticmethod</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">sort_items</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">pass</span>
<div class="viewcode-block" id="StratifiedSelector.preprocess"><a class="viewcode-back" href="../../selection.html#catsim.selection.StratifiedSelector.preprocess">[docs]</a> <span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># sort item indexes by their discrimination value</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_organized_items</span> <span class="o">=</span> <span class="vm">__class__</span><span class="o">.</span><span class="n">sort_items</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span><span class="p">)</span></div>
<div class="viewcode-block" id="StratifiedSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.StratifiedSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more strata to get items from.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span>
<span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="c1"># select the item in the correct layer, according to the point in the test the examinee is</span>
<span class="n">slices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">items</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="bp">self</span><span class="o">.</span><span class="n">_test_size</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'i'</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">pointer</span> <span class="o">=</span> <span class="n">slices</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)]</span>
<span class="n">max_pointer</span> <span class="o">=</span> <span class="n">items</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">if</span> <span class="nb">len</span><span class="p">(</span>
<span class="n">administered_items</span>
<span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">slices</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span>
<span class="s2">"</span><span class="si">{0}</span><span class="s2">: test size is larger than was informed to the selector</span><span class="se">\n</span><span class="s2">Length of administered items:</span><span class="se">\t</span><span class="si">{0}</span><span class="se">\n</span><span class="s2">Total length of the test:</span><span class="se">\t</span><span class="si">{1}</span><span class="se">\n</span><span class="s2">Number of slices:</span><span class="se">\t</span><span class="si">{2}</span><span class="s2">"</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">slices</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">organized_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_organized_items</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_organized_items</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">sort_items</span><span class="p">(</span>
<span class="n">items</span>
<span class="p">)</span>
<span class="c1"># if the selected item has already been administered, select the next one</span>
<span class="k">while</span> <span class="n">organized_items</span><span class="p">[</span><span class="n">pointer</span><span class="p">]</span> <span class="ow">in</span> <span class="n">administered_items</span><span class="p">:</span>
<span class="n">pointer</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">pointer</span> <span class="o">==</span> <span class="n">max_pointer</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'There are no more items to be selected from stratum </span><span class="si">{0}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">slices</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)]</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">organized_items</span><span class="p">[</span><span class="n">pointer</span><span class="p">]</span></div></div>
<div class="viewcode-block" id="AStratSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.AStratSelector">[docs]</a><span class="k">class</span> <span class="nc">AStratSelector</span><span class="p">(</span><span class="n">StratifiedSelector</span><span class="p">):</span>
<span class="sd">"""Implementation of the :math:`\\alpha`-stratified selector proposed by</span>
<span class="sd"> [Chang99]_, in which the item bank is sorted in ascending order according to the</span>
<span class="sd"> items discrimination parameter and then separated into :math:`K` strata</span>
<span class="sd"> (:math:`K` being the test size), each stratum containing gradually higher</span>
<span class="sd"> average discrimination. The :math:`\\alpha`-stratified selector then selects the</span>
<span class="sd"> first non-administered item from stratum :math:`k`, in which :math:`k`</span>
<span class="sd"> represents the position in the test of the current item the examinee is being</span>
<span class="sd"> presented.</span>
<span class="sd"> .. image:: ../sphinx/alpha-strat.*</span>
<span class="sd"> :param test_size: the number of items the test contains. The selector uses this parameter</span>
<span class="sd"> to create the correct number of strata.</span>
<span class="sd"> """</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">'a-Stratified Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sort_items</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">return</span> <span class="n">items</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span></div>
<div class="viewcode-block" id="AStratBBlockSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.AStratBBlockSelector">[docs]</a><span class="k">class</span> <span class="nc">AStratBBlockSelector</span><span class="p">(</span><span class="n">StratifiedSelector</span><span class="p">):</span>
<span class="sd">"""Implementation of the :math:`\\alpha`-stratified selector with :math:`b`</span>
<span class="sd"> blocking proposed by [Chang2001]_, in which the item bank is sorted in ascending</span>
<span class="sd"> order according to the items difficulty parameter and then separated into</span>
<span class="sd"> :math:`M` strata, each stratum containing gradually higher average difficulty.</span>
<span class="sd"> Each of the :math:`M` strata is then again separated into :math:`K`</span>
<span class="sd"> sub-strata (:math:`k` being the test size), according to their</span>
<span class="sd"> discrimination. The final item bank is then ordered such that the first</span>
<span class="sd"> sub-strata of each strata forms the first strata of the new ordered item</span>
<span class="sd"> bank, and so on. This method tries to balance the distribution of both</span>
<span class="sd"> parameters between all strata, after perceiving that they are correlated.</span>
<span class="sd"> .. image:: ../sphinx/b-blocking.*</span>
<span class="sd"> :param test_size: the number of items the test contains. The selector uses this parameter to</span>
<span class="sd"> create the correct number of strata.</span>
<span class="sd"> """</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">'a-Stratified b-Blocking Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sort_items</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">lexsort</span><span class="p">((</span><span class="n">items</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">items</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]))</span></div>
<div class="viewcode-block" id="MaxInfoStratSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.MaxInfoStratSelector">[docs]</a><span class="k">class</span> <span class="nc">MaxInfoStratSelector</span><span class="p">(</span><span class="n">StratifiedSelector</span><span class="p">):</span>
<span class="sd">"""Implementation of the maximum information stratification (MIS) selector</span>
<span class="sd"> proposed by [Bar06]_, in which the item bank is sorted in ascending order</span>
<span class="sd"> according to the items maximum information and then separated into :math:`K`</span>
<span class="sd"> strata (:math:`K` being the test size), each stratum containing items with</span>
<span class="sd"> gradually higher maximum information. The MIS selector then selects the first</span>
<span class="sd"> non-administered item from stratum :math:`k`, in which :math:`k` represents the</span>
<span class="sd"> position in the test of the current item the examinee is being presented.</span>
<span class="sd"> .. image:: ../sphinx/mis.*</span>
<span class="sd"> This method claims to work better than the :math:`a`-stratified method by</span>
<span class="sd"> [Chang99]_ for the three-parameter logistic model of IRT, since item difficulty</span>
<span class="sd"> and maximum information are not positioned in the same place in the proficiency</span>
<span class="sd"> scale in 3PL.</span>
<span class="sd"> :param test_size: the number of items the test contains. The selector uses this parameter to</span>
<span class="sd"> create the correct number of strata.</span>
<span class="sd"> """</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">'Maximum Information Stratification Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sort_items</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="n">maxinfo</span> <span class="o">=</span> <span class="n">irt</span><span class="o">.</span><span class="n">max_info_hpc</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
<span class="k">return</span> <span class="n">irt</span><span class="o">.</span><span class="n">inf_hpc</span><span class="p">(</span><span class="n">maxinfo</span><span class="p">,</span> <span class="n">items</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span></div>
<div class="viewcode-block" id="MaxInfoBBlockSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.MaxInfoBBlockSelector">[docs]</a><span class="k">class</span> <span class="nc">MaxInfoBBlockSelector</span><span class="p">(</span><span class="n">StratifiedSelector</span><span class="p">):</span>
<span class="sd">"""Implementation of the maximum information stratification with :math:`b`</span>
<span class="sd"> blocking (MIS-B) selector proposed by [Bar06]_, in which the item bank is sorted</span>
<span class="sd"> in ascending order according to the items difficulty parameter and then</span>
<span class="sd"> separated into :math:`M` strata, each stratum containing gradually higher</span>
<span class="sd"> average difficulty.</span>
<span class="sd"> Each of the :math:`M` strata is then again separated into :math:`K`</span>
<span class="sd"> sub-strata (:math:`k` being the test size), according to the items maximum</span>
<span class="sd"> information. The final item bank is then ordered such that the first</span>
<span class="sd"> sub-strata of each strata forms the first strata of the new ordered item</span>
<span class="sd"> bank, and so on. This method tries to balance the distribution of both</span>
<span class="sd"> parameters between all strata and works better than the :math:`a`-stratified</span>
<span class="sd"> with :math:`b` blocking method by [Chang2001]_ for the three-parameter</span>
<span class="sd"> logistic model of IRT, since item difficulty and maximum information are not</span>
<span class="sd"> positioned in the same place in the proficiency scale in 3PL. This may also</span>
<span class="sd"> apply, although not mentioned by the authors, for the 4PL.</span>
<span class="sd"> .. image:: ../sphinx/mis-b.*</span>
<span class="sd"> :param test_size: the number of items the test contains. The selector uses this parameter to</span>
<span class="sd"> create the correct number of strata.</span>
<span class="sd"> """</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">'Maximum Information Stratification with b-Blocking Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">sort_items</span><span class="p">(</span><span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-></span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="n">maxinfo</span> <span class="o">=</span> <span class="n">irt</span><span class="o">.</span><span class="n">max_info_hpc</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">lexsort</span><span class="p">((</span><span class="n">irt</span><span class="o">.</span><span class="n">inf_hpc</span><span class="p">(</span><span class="n">maxinfo</span><span class="p">,</span> <span class="n">items</span><span class="p">),</span> <span class="n">maxinfo</span><span class="p">))</span></div>
<div class="viewcode-block" id="The54321Selector"><a class="viewcode-back" href="../../selection.html#catsim.selection.The54321Selector">[docs]</a><span class="k">class</span> <span class="nc">The54321Selector</span><span class="p">(</span><span class="n">FiniteSelector</span><span class="p">):</span>
<span class="sd">"""Implementation of the 5-4-3-2-1 selector proposed by [McBride83]_, in which,</span>
<span class="sd"> at each step :math:`k` of a test of size :math:`K`, an item is chosen from a bin</span>
<span class="sd"> containing the :math:`K-k` most informative items in the bank, given the current</span>
<span class="sd"> :math:`\\hat\\theta`. As the test progresses, the bin gets smaller and more</span>
<span class="sd"> informative items have a higher probability of being chosen by the end of the</span>
<span class="sd"> test, when the estimation of ':math:`\\hat\\theta` is more precise. The</span>
<span class="sd"> 5-4-3-2-1 selector can be viewed as a specialization of the</span>
<span class="sd"> :py:class:`catsim.selection.RandomesqueSelector`, in which the bin size of most</span>
<span class="sd"> informative items gets smaller as the test progresses.</span>
<span class="sd"> :param test_size: the number of items the test contains. The selector uses</span>
<span class="sd"> this parameter to set the bin size"""</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">'5-4-3-2-1 Selector'</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">test_size</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">test_size</span><span class="p">)</span>
<div class="viewcode-block" id="The54321Selector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.The54321Selector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">est_theta</span><span class="p">:</span> <span class="nb">float</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>
<span class="sd"> :param index: the index of the current examinee in the simulator.</span>
<span class="sd"> :param items: a matrix containing item parameters in the format that `catsim` understands</span>
<span class="sd"> (see: :py:func:`catsim.cat.generate_item_bank`)</span>
<span class="sd"> :param administered_items: a list containing the indexes of items that were already administered</span>
<span class="sd"> :param est_theta: a float containing the current estimated proficiency</span>
<span class="sd"> :returns: index of the next item to be applied or `None` if there are no more items in the item bank.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Either pass an index for the simulator or all of the other optional parameters to use this component independently.'</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">administered_items</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">est_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">items</span>
<span class="n">administered_items</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">administered_items</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="n">est_theta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">simulator</span><span class="o">.</span><span class="n">latest_estimations</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="c1"># sort item indexes by their information value descending and remove indexes of administered items</span>
<span class="n">organized_items</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="o">-</span><span class="n">irt</span><span class="o">.</span><span class="n">inf_hpc</span><span class="p">(</span><span class="n">est_theta</span><span class="p">,</span> <span class="n">items</span><span class="p">))</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">administered_items</span>
<span class="p">]</span>
<span class="n">bin_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">administered_items</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">organized_items</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">warn</span><span class="p">(</span><span class="s1">'There are no more items to apply.'</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">numpy</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">organized_items</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">bin_size</span><span class="p">])</span></div></div>
<div class="viewcode-block" id="RandomesqueSelector"><a class="viewcode-back" href="../../selection.html#catsim.selection.RandomesqueSelector">[docs]</a><span class="k">class</span> <span class="nc">RandomesqueSelector</span><span class="p">(</span><span class="n">Selector</span><span class="p">):</span>
<span class="sd">"""Implementation of the randomesque selector proposed by [Kingsbury89]_, in which,</span>
<span class="sd"> at every step of the test, an item is randomly chosen from the :math:`n` most informative</span>
<span class="sd"> items in the item bank, :math:`n` being a predefined value (originally 5, but user-defined</span>
<span class="sd"> in this implementation)</span>
<span class="sd"> :param bin_size: the number of most informative items to be taken into consideration when</span>
<span class="sd"> randomly selecting one of them.</span>
<span class="sd"> """</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">'Randomesque Selector'</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">bin_size</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="bp">self</span><span class="o">.</span><span class="n">_bin_size</span> <span class="o">=</span> <span class="n">bin_size</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">bin_size</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">_bin_size</span>
<div class="viewcode-block" id="RandomesqueSelector.select"><a class="viewcode-back" href="../../selection.html#catsim.selection.RandomesqueSelector.select">[docs]</a> <span class="k">def</span> <span class="nf">select</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">index</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">items</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">administered_items</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">est_theta</span><span class="p">:</span> <span class="nb">float</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="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sd">"""Returns the index of the next item to be administered.</span>