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docs-source/source/validation/approval.rst

@@ -8,7 +8,9 @@ Impartial
 
 :py:func:`prefsampling.approval.impartial`
 
-The probability distribution generated by the approval impartial culture model.
+In the impartial model every candidate has the same probability :code:`p` of being approved. Thus,
+the probability of generating an approval ballot of length :code:`k` follows a binomial distribution
+of parameters :code:`p` and :code:`k`.
 
 .. image:: validation_plots/approval/impartial.png
   :width: 800
@@ -19,29 +21,27 @@ Resampling
 
 :py:func:`prefsampling.approval.resampling`
 
-The probability distribution generated by the resampling model.
 See the work of `Szufa et al. (2022) <https://www.ijcai.org/proceedings/2022/0071.pdf>`_
-for more details.
+for more details on this model.
 
 .. image:: validation_plots/approval/resampling.png
   :width: 800
   :alt: Observed versus theoretical frequencies for the resampling model
 
 Disjoint Resampling
-----------
+-------------------
 
 :py:func:`prefsampling.approval.disjoint_resampling`
 
-The probability distribution generated by the disjoint resampling model.
 See  the work of `Szufa et al. (2022) <https://www.ijcai.org/proceedings/2022/0071.pdf>`_
-for more details.
+for more details on this model.
 
 .. image:: validation_plots/approval/disjoint_resampling.png
   :width: 800
   :alt: Observed versus theoretical frequencies for the disjoint resampling model
 
 Noise
-----------
+-----
 
 :py:func:`prefsampling.approval.noise`
 
@@ -52,11 +52,11 @@ The probability distribution generated by the noise model based on the Hamming d
   :alt: Observed versus theoretical frequencies for the noise model
 
 Identity
-----------
+--------
 
 :py:func:`prefsampling.approval.identity`
 
-The probability distribution generated by the identity model.
+The identity sampler only samples a single ballot. This is what we observe here.
 
 .. image:: validation_plots/approval/identity.png
   :width: 800

docs/_modules/index.html

@@ -265,6 +265,7 @@ <h1>All modules for which code is available</h1>
 <ul><li><a href="prefsampling/approval/euclidean.html">prefsampling.approval.euclidean</a></li>
 <li><a href="prefsampling/approval/identity.html">prefsampling.approval.identity</a></li>
 <li><a href="prefsampling/approval/impartial.html">prefsampling.approval.impartial</a></li>
+<li><a href="prefsampling/approval/mixture.html">prefsampling.approval.mixture</a></li>
 <li><a href="prefsampling/approval/noise.html">prefsampling.approval.noise</a></li>
 <li><a href="prefsampling/approval/partylist.html">prefsampling.approval.partylist</a></li>
 <li><a href="prefsampling/approval/resampling.html">prefsampling.approval.resampling</a></li>
@@ -280,6 +281,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="prefsampling/ordinal/singlecrossing.html">prefsampling.ordinal.singlecrossing</a></li>
 <li><a href="prefsampling/ordinal/singlepeaked.html">prefsampling.ordinal.singlepeaked</a></li>
 <li><a href="prefsampling/ordinal/urn.html">prefsampling.ordinal.urn</a></li>
+<li><a href="prefsampling/tree/caterpillar.html">prefsampling.tree.caterpillar</a></li>
 <li><a href="prefsampling/tree/schroeder.html">prefsampling.tree.schroeder</a></li>
 </ul>
 

docs/_modules/prefsampling/approval/noise.html

@@ -343,7 +343,7 @@ <h1>Source code for prefsampling.approval.noise</h1><div class="highlight"><pre>
 
     <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
 
-    <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
+    <span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
 
     <span class="n">A</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</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">k</span><span class="p">)}</span>
     <span class="n">B</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_candidates</span><span class="p">))</span> <span class="o">-</span> <span class="n">A</span>

docs/_modules/prefsampling/approval/resampling.html

@@ -264,8 +264,10 @@ <h1></h1>
   <h1>Source code for prefsampling.approval.resampling</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">copy</span>
 
+<span class="kn">import</span> <span class="nn">math</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
+
 <span class="kn">from</span> <span class="nn">prefsampling.inputvalidators</span> <span class="kn">import</span> <span class="n">validate_num_voters_candidates</span>
 
 
@@ -316,7 +318,7 @@ <h1>Source code for prefsampling.approval.resampling</h1><div class="highlight">
 
     <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
 
-    <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
+    <span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">central_vote</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">central_vote</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
 
@@ -386,7 +388,7 @@ <h1>Source code for prefsampling.approval.resampling</h1><div class="highlight">
     <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
 
     <span class="n">num_groups</span> <span class="o">=</span> <span class="n">g</span>
-    <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
+    <span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
 
     <span class="n">votes</span> <span class="o">=</span> <span class="p">[</span><span class="nb">set</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_voters</span><span class="p">)]</span>
 
@@ -453,7 +455,7 @@ <h1>Source code for prefsampling.approval.resampling</h1><div class="highlight">
 
     <span class="n">breaks</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">num_voters</span> <span class="o">/</span> <span class="n">num_legs</span><span class="p">)</span> <span class="o">*</span> <span class="n">i</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">num_legs</span><span class="p">)]</span>
 
-    <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
+    <span class="n">k</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">num_candidates</span><span class="p">)</span>
     <span class="n">central_vote</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</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">k</span><span class="p">)}</span>
     <span class="n">ccc</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">central_vote</span><span class="p">)</span>
 

docs/_modules/prefsampling/ordinal/singlecrossing.html

@@ -349,9 +349,9 @@ <h1>Source code for prefsampling.ordinal.singlecrossing</h1><div class="highligh
         <span class="n">votes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">domain</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
         <span class="n">last_sampled_index</span> <span class="o">=</span> <span class="n">index</span>
 
-    <span class="c1"># vote_indices = np.sort(rng.choice(np.arange(domain_size), size=num_voters))</span>
-    <span class="c1"># for i, index in enumerate(vote_indices):</span>
-    <span class="c1">#     votes[i, :] = domain[index]</span>
+    <span class="n">vote_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">domain_size</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">num_voters</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vote_indices</span><span class="p">):</span>
+        <span class="n">votes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">domain</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">votes</span></div>