DOC: update lightning doc

Author: fabianp

lightning/_downloads/plot_1d_total_variation.py

@@ -12,10 +12,11 @@
      y = sign(X ground_truth + noise)
 
 where X is a random matrix. We obtain the vector ground_truth by solving
-an optimization problem using lightning FistaClassifier.
+an optimization problem using lightning's :class:`lightning.classification.FistaClassifier`.
 
 The 1D total variation is also known as fused lasso.
 """
+# Author: Fabian Pedregosa <[email protected]>
 
 import numpy as np
 import matplotlib.pyplot as plt

lightning/_modules/lightning/impl/sdca.html

@@ -281,6 +281,29 @@ <h1>Source code for lightning.impl.sdca</h1><div class="highlight"><pre>
 <span class="sd">        minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)</span>
 <span class="sd">                    + alpha * l1_ratio * ||w||_1</span>
 <span class="sd">                    + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    loss: string, {&#39;squared&#39;, &#39;absolute&#39;, &#39;hinge&#39;, &#39;smooth_hinge&#39;, &#39;squared_hinge&#39;}</span>
+<span class="sd">         Loss function to use in the model.</span>
+<span class="sd">    alpha: float</span>
+<span class="sd">         Amount of regularization (see model formulation above).</span>
+<span class="sd">    l1_ratio: float</span>
+<span class="sd">         Ratio between the L1 and L2 regularization (see model formulation above).</span>
+<span class="sd">    gamma : float</span>
+<span class="sd">        gamma parameter in the &quot;smooth_hinge&quot; loss (not used for other</span>
+<span class="sd">        loss functions)</span>
+<span class="sd">    tol : float</span>
+<span class="sd">        stopping criterion tolerance.</span>
+<span class="sd">    max_iter : int</span>
+<span class="sd">        maximum number of outer iterations (also known as epochs).</span>
+<span class="sd">    verbose : int</span>
+<span class="sd">        verbosity level. Set positive to print progress information.</span>
+<span class="sd">    callback : callable or None</span>
+<span class="sd">        if given, callback(self) will be called on each outer iteration</span>
+<span class="sd">        (epoch).</span>
+<span class="sd">    random_state: int or RandomState</span>
+<span class="sd">        Pseudo-random number generator state used for random sampling.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
 <div class="viewcode-block" id="SDCAClassifier.__init__"><a class="viewcode-back" href="../../../generated/lightning.classification.SDCAClassifier.html#lightning.classification.SDCAClassifier.__init__">[docs]</a>    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">l1_ratio</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;hinge&quot;</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
@@ -323,15 +346,34 @@ <h1>Source code for lightning.impl.sdca</h1><div class="highlight"><pre>
 <span class="sd">        minimize_w  1 / n_samples * \sum_i loss(w^T x_i, y_i)</span>
 <span class="sd">                    + alpha * l1_ratio * ||w||_1</span>
 <span class="sd">                    + alpha * (1 - l1_ratio) * 0.5 * ||w||^2_2</span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    loss: string, {&#39;squared&#39;, &#39;absolute&#39;}</span>
+<span class="sd">         Loss function to use in the model.</span>
+<span class="sd">    alpha: float</span>
+<span class="sd">         Amount of regularization (see model formulation above).</span>
+<span class="sd">    l1_ratio: float</span>
+<span class="sd">         Ratio between the L1 and L2 regularization (see model formulation above).</span>
+<span class="sd">    tol : float</span>
+<span class="sd">        stopping criterion tolerance.</span>
+<span class="sd">    max_iter : int</span>
+<span class="sd">        maximum number of outer iterations (also known as epochs).</span>
+<span class="sd">    verbose : int</span>
+<span class="sd">        verbosity level. Set positive to print progress information.</span>
+<span class="sd">    callback : callable or None</span>
+<span class="sd">        if given, callback(self) will be called on each outer iteration</span>
+<span class="sd">        (epoch).</span>
+<span class="sd">    random_state: int or RandomState</span>
+<span class="sd">        Pseudo-random number generator state used for random sampling.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="SDCARegressor.__init__"><a class="viewcode-back" href="../../../generated/lightning.regression.SDCARegressor.html#lightning.classification.SDCARegressor.__init__">[docs]</a>    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">l1_ratio</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;squared&quot;</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+<div class="viewcode-block" id="SDCARegressor.__init__"><a class="viewcode-back" href="../../../generated/lightning.regression.SDCARegressor.html#lightning.classification.SDCARegressor.__init__">[docs]</a>    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">l1_ratio</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;squared&quot;</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">tol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">n_calls</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                  <span class="n">random_state</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">l1_ratio</span> <span class="o">=</span> <span class="n">l1_ratio</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="mf">1.0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">max_iter</span> <span class="o">=</span> <span class="n">max_iter</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">tol</span> <span class="o">=</span> <span class="n">tol</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">callback</span> <span class="o">=</span> <span class="n">callback</span>

lightning/_sources/auto_examples/plot_1d_total_variation.txt

@@ -16,7 +16,7 @@ Given a ground truth vectors, the signal that we observe is given by
      y = sign(X ground_truth + noise)
 
 where X is a random matrix. We obtain the vector ground_truth by solving
-an optimization problem using lightning FistaClassifier.
+an optimization problem using lightning's :class:`lightning.classification.FistaClassifier`.
 
 The 1D total variation is also known as fused lasso.
 
@@ -33,6 +33,6 @@ The 1D total variation is also known as fused lasso.
 .. literalinclude:: plot_1d_total_variation.py
     :lines: 19-
 
-**Total running time of the example:**  0.90 seconds
-( 0 minutes  0.90 seconds)
+**Total running time of the example:**  0.92 seconds
+( 0 minutes  0.92 seconds)
 

lightning/auto_examples/plot_1d_total_variation.html

@@ -208,11 +208,12 @@
 <blockquote>
 <div>y = sign(X ground_truth + noise)</div></blockquote>
 <p>where X is a random matrix. We obtain the vector ground_truth by solving
-an optimization problem using lightning FistaClassifier.</p>
+an optimization problem using lightning&#8217;s <a class="reference internal" href="../generated/lightning.classification.FistaClassifier.html#lightning.classification.FistaClassifier" title="lightning.classification.FistaClassifier"><code class="xref py py-class docutils literal"><span class="pre">lightning.classification.FistaClassifier</span></code></a>.</p>
 <p>The 1D total variation is also known as fused lasso.</p>
 <img alt="../_images/plot_1d_total_variation_001.png" class="align-center" src="../_images/plot_1d_total_variation_001.png" />
 <p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_1d_total_variation.py"><code class="xref download docutils literal"><span class="pre">plot_1d_total_variation.py</span></code></a></p>
-<div class="highlight-python"><div class="highlight"><pre><span></span>
+<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Author: Fabian Pedregosa &lt;[email protected]&gt;</span>
+
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
 <span class="kn">from</span> <span class="nn">lightning.classification</span> <span class="kn">import</span> <span class="n">FistaClassifier</span>
@@ -243,8 +244,8 @@
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p><strong>Total running time of the example:</strong>  0.90 seconds
-( 0 minutes  0.90 seconds)</p>
+<p><strong>Total running time of the example:</strong>  0.92 seconds
+( 0 minutes  0.92 seconds)</p>
 </div>
 
 

lightning/auto_examples/plot_l2_solvers.html

@@ -306,7 +306,7 @@
 <span class="n">clf6</span> <span class="o">=</span> <span class="n">SAGClassifier</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s2">&quot;squared_hinge&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</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">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="n">tol</span><span class="p">)</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
 
 <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
 <span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">((</span><span class="n">clf1</span><span class="p">,</span> <span class="s2">&quot;SVRG&quot;</span><span class="p">),</span>
@@ -335,7 +335,7 @@
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.yscale"><span class="n">plt</span><span class="o">.</span><span class="n">yscale</span></a><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">&quot;CPU time&quot;</span><span class="p">)</span>
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">&quot;Objective value minus optimum&quot;</span><span class="p">)</span>
-<a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.grid"><span class="n">plt</span><span class="o">.</span><span class="n">grid</span></a><span class="p">()</span>
 
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>

lightning/auto_examples/plot_svrg.html

@@ -248,7 +248,7 @@
         <span class="n">test_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">clock</span><span class="p">()</span>
         <span class="n">clf</span><span class="o">.</span><span class="n">_finalize_coef</span><span class="p">()</span>
         <span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
-        <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.maximum.html#numpy.maximum"><span class="n">np</span><span class="o">.</span><span class="n">maximum</span></a><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">*</span> <span class="n">y_pred</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">*</span> <span class="n">y_pred</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
         <span class="n">coef</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
         <span class="n">regul</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">clf</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.dot.html#numpy.dot"><span class="n">np</span><span class="o">.</span><span class="n">dot</span></a><span class="p">(</span><span class="n">coef</span><span class="p">,</span> <span class="n">coef</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span> <span class="o">+</span> <span class="n">regul</span><span class="p">)</span>
@@ -279,7 +279,7 @@
 <span class="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span>
 
 
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
 
 <span class="k">for</span> <span class="n">eta</span> <span class="ow">in</span> <span class="n">etas</span><span class="p">:</span>
     <span class="k">print</span><span class="p">(</span><span class="s2">&quot;eta =&quot;</span><span class="p">,</span> <span class="n">eta</span><span class="p">)</span>
@@ -293,7 +293,7 @@
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">&quot;Objective value&quot;</span><span class="p">)</span>
 <a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
 
 <span class="k">for</span> <span class="n">n_inner</span> <span class="ow">in</span> <span class="n">n_inners</span><span class="p">:</span>
     <span class="k">print</span><span class="p">(</span><span class="s2">&quot;n_inner =&quot;</span><span class="p">,</span> <span class="n">n_inner</span><span class="p">)</span>

lightning/generated/lightning.classification.AdaGradClassifier.html

@@ -76,6 +76,7 @@
       role="menu"
       aria-labelledby="dLabelGlobalToc"><ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../auto_examples/index.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_1d_total_variation.html">Signal recovery by 1D total variation</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_sgd_loss_functions.html">SGD: Convex Loss Functions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../auto_examples/plot_robust_regression.html">Robust regression</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../auto_examples/trace.html">Trace norm</a></li>
@@ -126,6 +127,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../intro.html#stochastic-variance-reduced-gradient-svrg">Stochastic variance-reduced gradient (SVRG)</a></li>
 <li class="toctree-l2 current"><a class="reference internal" href="../intro.html#prank">PRank</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="../auto_examples/index.html">Examples</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../auto_examples/plot_1d_total_variation.html">Signal recovery by 1D total variation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../auto_examples/plot_sgd_loss_functions.html">SGD: Convex Loss Functions</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../auto_examples/plot_robust_regression.html">Robust regression</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../auto_examples/trace.html">Trace norm</a></li>