update doc

_build/html/_sources/command_line.rst.txt

@@ -125,7 +125,40 @@ A portion of xLearn's output: ::
 Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes 
 down first, and then goes up. This is because our model has already overfitted current training data set. On 
 default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
-metric by using ``-x`` option.
+metric by using ``-x`` option. For classification problem, the metric can be : ``acc`` (accuracy), ``prec`` 
+(precision), ``f1`` (f1 score), ``auc`` (AUC score). For example: ::
+
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x acc
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x prec
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x f1
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x auc
+
+For regression problem, the metric can be ``mae``, ``mape``, and ``rmsd`` (rmse). For example: ::
+
+    cd demo/house_price/
+    ../../xlearn_train ./house_price_train.txt -s 3 -x rmse --cv
+    ../../xlearn_train ./house_price_train.txt -s 3 -x rmsd --cv
+
+Cross Validation
+----------------------------------------
+
+Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing 
+how the results of a statistical analysis will generalize to an independent data set. In xLearn, users 
+can use ``--cv`` option to use this technique. For example: ::
+
+    ./xlearn_train ./small_train.txt --cv
+
+On default, xLearn uses 5-folds cross validation, and users can set the number of fold by using 
+``-f`` option: ::
+    
+    ./xlearn_train ./small_train.txt -f 3 --cv
+
+The xLearn will calcluate the avergae validation loss at the end of it's message. ::
+
+    [------------] Average log_loss: 0.549417
+    [ ACTION     ] Finish Cross-Validation
+    [ ACTION     ] Clear the xLearn environment ...
+    [------------] Total time cost: 0.03 (sec)
 
 
 

_build/html/command_line.html

@@ -95,9 +95,8 @@
 <li class="toctree-l1 current"><a class="current reference internal" href="#">xLearn Command Line Guide</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#quick-start">Quick Start</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#choose-machine-learning-model">Choose Machine Learning Model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#set-validation-dataset">Set Validation Dataset</a><ul class="simple">
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="#set-validation-dataset">Set Validation Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#cross-validation">Cross Validation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="python_api.html">xLearn Python API Guide</a></li>
@@ -289,11 +288,46 @@ <h2>Set Validation Dataset<a class="headerlink" href="#set-validation-dataset" t
 <p>Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes
 down first, and then goes up. This is because our model has already overfitted current training data set. On
 default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
-metric by using <code class="docutils literal"><span class="pre">-x</span></code> option.</p>
-<blockquote>
-<div><div class="toctree-wrapper compound">
+metric by using <code class="docutils literal"><span class="pre">-x</span></code> option. For classification problem, the metric can be : <code class="docutils literal"><span class="pre">acc</span></code> (accuracy), <code class="docutils literal"><span class="pre">prec</span></code>
+(precision), <code class="docutils literal"><span class="pre">f1</span></code> (f1 score), <code class="docutils literal"><span class="pre">auc</span></code> (AUC score). For example:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">v</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">x</span> <span class="n">acc</span>
+<span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">v</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">x</span> <span class="n">prec</span>
+<span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">v</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">x</span> <span class="n">f1</span>
+<span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">v</span> <span class="o">./</span><span class="n">small_test</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">x</span> <span class="n">auc</span>
+</pre></div>
+</div>
+<p>For regression problem, the metric can be <code class="docutils literal"><span class="pre">mae</span></code>, <code class="docutils literal"><span class="pre">mape</span></code>, and <code class="docutils literal"><span class="pre">rmsd</span></code> (rmse). For example:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">demo</span><span class="o">/</span><span class="n">house_price</span><span class="o">/</span>
+<span class="o">../../</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">house_price_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">s</span> <span class="mi">3</span> <span class="o">-</span><span class="n">x</span> <span class="n">rmse</span> <span class="o">--</span><span class="n">cv</span>
+<span class="o">../../</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">house_price_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">s</span> <span class="mi">3</span> <span class="o">-</span><span class="n">x</span> <span class="n">rmsd</span> <span class="o">--</span><span class="n">cv</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="cross-validation">
+<h2>Cross Validation<a class="headerlink" href="#cross-validation" title="Permalink to this headline">¶</a></h2>
+<p>Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing
+how the results of a statistical analysis will generalize to an independent data set. In xLearn, users
+can use <code class="docutils literal"><span class="pre">--cv</span></code> option to use this technique. For example:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">--</span><span class="n">cv</span>
+</pre></div>
+</div>
+<p>On default, xLearn uses 5-folds cross validation, and users can set the number of fold by using
+<code class="docutils literal"><span class="pre">-f</span></code> option:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">xlearn_train</span> <span class="o">./</span><span class="n">small_train</span><span class="o">.</span><span class="n">txt</span> <span class="o">-</span><span class="n">f</span> <span class="mi">3</span> <span class="o">--</span><span class="n">cv</span>
+</pre></div>
+</div>
+<p>The xLearn will calcluate the avergae validation loss at the end of it’s message.</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span>   <span class="p">[</span><span class="o">------------</span><span class="p">]</span> <span class="n">Average</span> <span class="n">log_loss</span><span class="p">:</span> <span class="mf">0.549417</span>
+   <span class="p">[</span> <span class="n">ACTION</span>     <span class="p">]</span> <span class="n">Finish</span> <span class="n">Cross</span><span class="o">-</span><span class="n">Validation</span>
+   <span class="p">[</span> <span class="n">ACTION</span>     <span class="p">]</span> <span class="n">Clear</span> <span class="n">the</span> <span class="n">xLearn</span> <span class="n">environment</span> <span class="o">...</span>
+   <span class="p">[</span><span class="o">------------</span><span class="p">]</span> <span class="n">Total</span> <span class="n">time</span> <span class="n">cost</span><span class="p">:</span> <span class="mf">0.03</span> <span class="p">(</span><span class="n">sec</span><span class="p">)</span>
+
+
+
+<span class="o">..</span> <span class="n">toctree</span><span class="p">::</span>
+  <span class="p">:</span><span class="n">hidden</span><span class="p">:</span>
+</pre></div>
 </div>
-</div></blockquote>
 </div>
 </div>
 

command_line.rst

@@ -125,7 +125,40 @@ A portion of xLearn's output: ::
 Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes 
 down first, and then goes up. This is because our model has already overfitted current training data set. On 
 default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
-metric by using ``-x`` option.
+metric by using ``-x`` option. For classification problem, the metric can be : ``acc`` (accuracy), ``prec`` 
+(precision), ``f1`` (f1 score), ``auc`` (AUC score). For example: ::
+
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x acc
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x prec
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x f1
+    ./xlearn_train ./small_train.txt -v ./small_test.txt -x auc
+
+For regression problem, the metric can be ``mae``, ``mape``, and ``rmsd`` (rmse). For example: ::
+
+    cd demo/house_price/
+    ../../xlearn_train ./house_price_train.txt -s 3 -x rmse --cv
+    ../../xlearn_train ./house_price_train.txt -s 3 -x rmsd --cv
+
+Cross Validation
+----------------------------------------
+
+Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing 
+how the results of a statistical analysis will generalize to an independent data set. In xLearn, users 
+can use ``--cv`` option to use this technique. For example: ::
+
+    ./xlearn_train ./small_train.txt --cv
+
+On default, xLearn uses 5-folds cross validation, and users can set the number of fold by using 
+``-f`` option: ::
+    
+    ./xlearn_train ./small_train.txt -f 3 --cv
+
+The xLearn will calcluate the avergae validation loss at the end of it's message. ::
+
+    [------------] Average log_loss: 0.549417
+    [ ACTION     ] Finish Cross-Validation
+    [ ACTION     ] Clear the xLearn environment ...
+    [------------] Total time cost: 0.03 (sec)