Added tensorflow dependency due to PCRL

docs/build/html/_sources/example.rst.txt

@@ -20,7 +20,7 @@ This example will show you how to run your very first experiment using Cornac. I
 
 	#Instantiate a pfm recommender model.
 	#Please refer to the documentation for details on parameter settings.
-	rec_pmf = Pmf(k=10, max_iter=10, learning_rate=0.001, lamda=0.001, init_params={'U':None,'V':None})
+	rec_pmf = Pmf(k=10, max_iter=100, learning_rate=0.001, lamda=0.001, init_params={'U':None,'V':None})
 
 	#Instantiate an evaluation strategy.
 	es_split = Split(data = mat_office, prop_test=0.2, prop_validation=0.0, good_rating=4)

docs/build/html/example.html

@@ -176,7 +176,7 @@ <h1>First example<a class="headerlink" href="#first-example" title="Permalink to
 
 <span class="c1">#Instantiate a pfm recommender model.</span>
 <span class="c1">#Please refer to the documentation for details on parameter settings.</span>
-<span class="n">rec_pmf</span> <span class="o">=</span> <span class="n">Pmf</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">lamda</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">init_params</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;U&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span><span class="s1">&#39;V&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">})</span>
+<span class="n">rec_pmf</span> <span class="o">=</span> <span class="n">Pmf</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">lamda</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">init_params</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;U&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">,</span><span class="s1">&#39;V&#39;</span><span class="p">:</span><span class="kc">None</span><span class="p">})</span>
 
 <span class="c1">#Instantiate an evaluation strategy.</span>
 <span class="n">es_split</span> <span class="o">=</span> <span class="n">Split</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">mat_office</span><span class="p">,</span> <span class="n">prop_test</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">prop_validation</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">good_rating</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>

setup.py

@@ -60,7 +60,7 @@
     url="",
     zip_safe=False,
     ext_modules = external_modules,
-	install_requires=['numpy', 'scipy', 'pandas','torch==0.4.0'],
+	install_requires=['numpy', 'scipy', 'pandas','tensorflow>=1.2.1','torch==0.4.0'],
 	dependency_links = [torch_dl],
     packages=setuptools.find_packages(),
     classifiers=(