sphnix updates

docs/pages/html/Algorithm.html

@@ -97,10 +97,7 @@
 <li class="toctree-l2"><a class="reference internal" href="#residual-sum-of-squares-rss">Residual Sum of Squares (RSS)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#goodness-of-fit">Goodness-of-fit</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#predictions">Predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#output-parameters">Output parameters</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#fit-transform">fit_transform</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="#output-variables">Output variables</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Performance.html">Performance</a></li>
@@ -270,23 +267,21 @@ <h2>Predictions<a class="headerlink" href="#predictions" title="Permalink to thi
 </dd>
 </dl>
 </div>
-<div class="section" id="output-parameters">
-<h2>Output parameters<a class="headerlink" href="#output-parameters" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="output-variables">
+<h2>Output variables<a class="headerlink" href="#output-variables" title="Permalink to this headline">¶</a></h2>
 <p>There are many output parameters provided by <code class="docutils literal notranslate"><span class="pre">distfit</span></code>.
 It all starts with the initialization:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize model and select popular distributions</span>
 <span class="n">dist</span> <span class="o">=</span> <span class="n">distfit</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="section" id="fit-transform">
-<h3>fit_transform<a class="headerlink" href="#fit-transform" title="Permalink to this headline">¶</a></h3>
 <p>The object now returns variables that are set by default, except for the <code class="docutils literal notranslate"><span class="pre">alpha</span></code> parameter (nothing else is provided). For more details, see the <strong>returns</strong> in the docstrings at <a class="reference internal" href="distfit.distfit.html#distfit.distfit.distfit" title="distfit.distfit.distfit"><code class="xref py py-func docutils literal notranslate"><span class="pre">distfit.distfit.distfit()</span></code></a>. In the next step, input-data <em>X</em> can be provided:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize model and select popular distributions</span>
 <span class="n">dist</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The object can now be feeded with input data X that will add more output variables to the object.
-Feeding the object is done with the <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">transform</span></code> function. Instead of using the two functions seperately, it can be done with <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>: <a class="reference internal" href="distfit.distfit.html#distfit.distfit.distfit.fit_transform" title="distfit.distfit.distfit.fit_transform"><code class="xref py py-func docutils literal notranslate"><span class="pre">distfit.distfit.distfit.fit_transform()</span></code></a>.</p>
+<p>The object can now be feeded with data <em>X</em>, using <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">transform</span></code> function, that will add more output variables to the object.
+Instead of using the two functions seperately, it can also be performed with <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>: <a class="reference internal" href="distfit.distfit.html#distfit.distfit.distfit.fit_transform" title="distfit.distfit.distfit.fit_transform"><code class="xref py py-func docutils literal notranslate"><span class="pre">distfit.distfit.distfit.fit_transform()</span></code></a>.</p>
 <p>The fit_transform outputs the variables <em>summary</em>, <em>distributions</em> and <em>model</em></p>
 <dl class="simple">
 <dt>dist.summary</dt><dd><p>The summary of the fits across the distributions.</p>
@@ -307,9 +302,9 @@ <h3>fit_transform<a class="headerlink" href="#fit-transform" title="Permalink to
 </pre></div>
 </div>
 <dl class="simple">
-<dt><strong>dist.distributions</strong> is a list containing the extracted <code class="docutils literal notranslate"><span class="pre">pdfs</span></code> from <code class="docutils literal notranslate"><span class="pre">scripy</span></code></dt><dd><p>The collected distributions.</p>
+<dt><strong>dist.distributions</strong> is a list containing the extracted pdfs from <code class="docutils literal notranslate"><span class="pre">scripy</span></code></dt><dd><p>The collected distributions.</p>
 </dd>
-<dt><strong>dist.model</strong> contains information regarding the best scoring <code class="docutils literal notranslate"><span class="pre">pdf</span></code>:</dt><dd><ul class="simple">
+<dt><strong>dist.model</strong> contains information regarding the best scoring pdf:</dt><dd><ul class="simple">
 <li><p>dist.model[‘RSS’]</p></li>
 <li><p>dist.model[‘name’]</p></li>
 <li><p>dist.model[‘distr’]</p></li>
@@ -321,7 +316,6 @@ <h3>fit_transform<a class="headerlink" href="#fit-transform" title="Permalink to
 </dd>
 </dl>
 </div>
-</div>
 </div>
 
 

docs/pages/html/Examples.html

@@ -99,8 +99,8 @@
 <p class="caption"><span class="caption-text">Examples</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#learn-new-model-with-gridsearch-and-train-test-set">Learn new model with gridsearch and train-test set</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#learn-new-model-on-the-entire-data-set">Learn new model on the entire data set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fit-distribution">Fit distribution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#make-predictions">Make predictions</a></li>
 </ul>
 </li>
 </ul>
@@ -175,39 +175,58 @@
   <hr class="docutils" id="code-directive" />
 <div class="section" id="examples">
 <h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="learn-new-model-with-gridsearch-and-train-test-set">
-<h2>Learn new model with gridsearch and train-test set<a class="headerlink" href="#learn-new-model-with-gridsearch-and-train-test-set" title="Permalink to this headline">¶</a></h2>
-<p>AAA</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import library</span>
-<span class="kn">import</span> <span class="nn">distfit</span>
-
-<span class="c1"># Load example data set</span>
-<span class="n">X</span><span class="p">,</span><span class="n">y_true</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">load_example</span><span class="p">()</span>
-
-<span class="c1"># Retrieve URLs of malicous and normal urls:</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="s1">&#39;bad&#39;</span><span class="p">,</span> <span class="n">train_test</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">gridsearch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># The test error will be shown</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<div class="section" id="fit-distribution">
+<h2>Fit distribution<a class="headerlink" href="#fit-distribution" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example data</span>
+<span class="n">X</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">normal</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">20</span><span class="p">]</span>
+
+<span class="c1"># Initialize</span>
+<span class="n">dist</span> <span class="o">=</span> <span class="n">distfit</span><span class="p">()</span>
+<span class="n">dist</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="c1"># Make prediction</span>
+<span class="n">dist</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
+<table class="docutils align-center" id="id1">
+<caption><span class="caption-text">Distribution fit</span><a class="headerlink" href="#id1" title="Permalink to this table">¶</a></caption>
+<colgroup>
+<col style="width: 100%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="_images/example_fig1a.png"><img alt="fig1a" src="_images/example_fig1a.png" style="width: 492.0px; height: 420.8px;" /></a></p></td>
+</tr>
+</tbody>
+</table>
 </div>
-<div class="section" id="learn-new-model-on-the-entire-data-set">
-<h2>Learn new model on the entire data set<a class="headerlink" href="#learn-new-model-on-the-entire-data-set" title="Permalink to this headline">¶</a></h2>
-<p>BBBB</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import library</span>
-<span class="kn">import</span> <span class="nn">distfit</span>
-
-<span class="c1"># Load example data set</span>
-<span class="n">X</span><span class="p">,</span><span class="n">y_true</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">load_example</span><span class="p">()</span>
-
-<span class="c1"># Retrieve URLs of malicous and normal urls:</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="s1">&#39;bad&#39;</span><span class="p">,</span> <span class="n">train_test</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">gridsearch</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># The train error will be shown. Such results are heavily biased as the model also learned on this set of data</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">distfit</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<div class="section" id="make-predictions">
+<h2>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">¶</a></h2>
+<p>Make some predictions can with the <code class="docutils literal notranslate"><span class="pre">predict</span></code> function.
+Due to multiple testing it can occur that samples are outside the boundary
+of the distribution confidence interval but are not marked as significant.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example data</span>
+<span class="n">X</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">normal</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">20</span><span class="p">]</span>
+
+<span class="c1"># Initialize</span>
+<span class="n">dist</span> <span class="o">=</span> <span class="n">distfit</span><span class="p">(</span><span class="n">distr</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="n">dist</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+
+<span class="c1"># Make prediction</span>
+<span class="n">dist</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
+<table class="docutils align-center" id="id2">
+<caption><span class="caption-text">Plot distribution with predictions</span><a class="headerlink" href="#id2" title="Permalink to this table">¶</a></caption>
+<colgroup>
+<col style="width: 100%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="_images/example_fig1b.png"><img alt="fig1b" src="_images/example_fig1b.png" style="width: 492.0px; height: 420.8px;" /></a></p></td>
+</tr>
+</tbody>
+</table>
 </div>
 </div>