Minor changes in Skm doc

cornac/models/skm/recom_skmeans.py

@@ -23,7 +23,7 @@ class Skmeans(Recommender):
 
     trainable: boolean, optional, default: True
         When False, the model is not trained and Cornac assumes that the model is already \
-        pre-trained (Theta and Beta are not None). 
+        trained. 
         
     tol : float, optional, default: 1e-6
         Relative tolerance with regards to skmeans' criterion to declare convergence.
@@ -65,18 +65,17 @@ def fit(self,X):
             the user-item preference matrix (traning data), in a scipy sparse format\
             (e.g., csc_matrix).
         """
+        X1 = X.copy()
+        X1 = X1.multiply(sp.csc_matrix(1./(np.sqrt(X1.multiply(X1).sum(1).A1)+1e-20)).T)
         if self.trainable:
             #Skmeans requires rows of X to have a unit L2 norm. We therefore need to make a copy of X as we should not modify the latter.
-            X1 = X.copy()
-            X1 = X1.multiply(sp.csc_matrix(1./(np.sqrt(X1.multiply(X1).sum(1).A1)+1e-20)).T)
             res = skmeans(X1,k = self.k, max_iter = self.max_iter,tol = self.tol,verbose = self.verbose,init_par = self.par)
             self.centroids = res['centroids']
             self.par = res['partition']
-            self.user_center_sim = X1*self.centroids.T   #user-centroid cosine similarity matrix
-            del(X1)
         else:
             print('%s is trained already (trainable = False)' % (self.name))
-
+        self.user_center_sim = X1*self.centroids.T   #user-centroid cosine similarity matrix
+        del(X1)
 
 
 

docs/build/html/_modules/cornac/models/skm/recom_skmeans.html

@@ -179,7 +179,7 @@ <h1>Source code for cornac.models.skm.recom_skmeans</h1><div class="highlight"><
 
 <span class="sd">    trainable: boolean, optional, default: True</span>
 <span class="sd">        When False, the model is not trained and Cornac assumes that the model is already \</span>
-<span class="sd">        pre-trained (Theta and Beta are not None). </span>
+<span class="sd">        trained. </span>
 <span class="sd">        </span>
 <span class="sd">    tol : float, optional, default: 1e-6</span>
 <span class="sd">        Relative tolerance with regards to skmeans&#39; criterion to declare convergence.</span>
@@ -221,18 +221,17 @@ <h1>Source code for cornac.models.skm.recom_skmeans</h1><div class="highlight"><
 <span class="sd">            the user-item preference matrix (traning data), in a scipy sparse format\</span>
 <span class="sd">            (e.g., csc_matrix).</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">X1</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">X1</span> <span class="o">=</span> <span class="n">X1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">sp</span><span class="o">.</span><span class="n">csc_matrix</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">X1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">A1</span><span class="p">)</span><span class="o">+</span><span class="mf">1e-20</span><span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainable</span><span class="p">:</span>
             <span class="c1">#Skmeans requires rows of X to have a unit L2 norm. We therefore need to make a copy of X as we should not modify the latter.</span>
-            <span class="n">X1</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="n">X1</span> <span class="o">=</span> <span class="n">X1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">sp</span><span class="o">.</span><span class="n">csc_matrix</span><span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">X1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">A1</span><span class="p">)</span><span class="o">+</span><span class="mf">1e-20</span><span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
             <span class="n">res</span> <span class="o">=</span> <span class="n">skmeans</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span><span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="n">max_iter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_iter</span><span class="p">,</span><span class="n">tol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tol</span><span class="p">,</span><span class="n">verbose</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span><span class="n">init_par</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">par</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">centroids</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="s1">&#39;centroids&#39;</span><span class="p">]</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">par</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="s1">&#39;partition&#39;</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">user_center_sim</span> <span class="o">=</span> <span class="n">X1</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">centroids</span><span class="o">.</span><span class="n">T</span>   <span class="c1">#user-centroid cosine similarity matrix</span>
-            <span class="k">del</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1"> is trained already (trainable = False)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span></div>
-
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1"> is trained already (trainable = False)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">user_center_sim</span> <span class="o">=</span> <span class="n">X1</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">centroids</span><span class="o">.</span><span class="n">T</span>   <span class="c1">#user-centroid cosine similarity matrix</span>
+        <span class="k">del</span><span class="p">(</span><span class="n">X1</span><span class="p">)</span></div>
 
 
 

docs/build/html/models.html

@@ -383,7 +383,7 @@
 <li><strong>k</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em><em>, </em><em>default: 5</em>) – The number of clusters.</li>
 <li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em><em>, </em><em>default: 100</em>) – Maximum number of iterations.</li>
 <li><strong>name</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default: 'Skmeans'</em>) – The name of the recommender model.</li>
-<li><strong>trainable</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default: True</em>) – When False, the model is not trained and Cornac assumes that the model is already         pre-trained (Theta and Beta are not None).</li>
+<li><strong>trainable</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default: True</em>) – When False, the model is not trained and Cornac assumes that the model is already         trained.</li>
 <li><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>optional</em><em>, </em><em>default: 1e-6</em>) – Relative tolerance with regards to skmeans’ criterion to declare convergence.</li>
 <li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default: True</em>) – When True, the skmeans criterion (likelihood) is displayed after each iteration.</li>
 <li><strong>init_par</strong> (<em>numpy 1d array</em><em>, </em><em>optional</em><em>, </em><em>default: None</em>) – The initial object parition, 1d array contaning the cluster label (int type starting from 0)         of each object (user). If par = None, then skmeans is initialized randomly.</li>