updating the documentation with large-scale learning algorithms

Author: doubleblindcode1

_modules/MRCpy/mrc.html

@@ -318,20 +318,45 @@ <h1>Source code for MRCpy.mrc</h1><div class="highlight"><pre>
 <span class="sd">                    Uncertainty in Artificial Intelligence, 206-215.</span>
 <span class="sd">                    &lt;https://proceedings.mlr.press/v216/bondugula23a.html&gt;`_</span>
 
+<span class="sd">        ’ccg’</span>
+<span class="sd">            Efficient learning algorithm for large number of samples</span>
+<span class="sd">            and features based on constraint generation. Efficiently</span>
+<span class="sd">            handles the multi-class case (with quasi linear complexity).</span>
+<span class="sd">            `eps1` and `eps2` are the parameters that provide a </span>
+<span class="sd">            trade-off between time complexity and acccuracy. </span>
+<span class="sd">            The maximum number of constraints selected in each iteration</span>
+<span class="sd">            is controlled by the hyperparamters `n_max` and `m_max`. </span>
+<span class="sd">            These hyperparameters also affect the comptutional complexity.</span>
+
+<span class="sd">        .. seealso:: For more information about the large-scale learning </span>
+<span class="sd">            algorithms for 0-1 MRC, one can refer to the following resource:</span>
+<span class="sd">    </span>
+<span class="sd">                [1] `Bondugula, K., Mazuelas, S., &amp; Pérez, A. (2025).</span>
+<span class="sd">                Efficient Large-Scale Learning of Minimax Risk Classifiers.</span>
+<span class="sd">                IEEE International Conference on Data Mining</span>
+<span class="sd">                &lt;https://www3.cs.stonybrook.edu/~icdm2025/acceptedpapers.html&gt;`_</span>
+
 <span class="sd">    max_iters : `int`, default = `10000`</span>
 <span class="sd">        Maximum number of iterations to use</span>
 <span class="sd">        for finding the solution of optimization when</span>
 <span class="sd">        using the subgradient approach.</span>
 
-<span class="sd">    n_max : `int`, default = `100`</span>
+<span class="sd">    n_max : `int`, default = `400`</span>
+<span class="sd">        Maximum number of constraints selected in each iteration</span>
+<span class="sd">        in case of ’ccg’ solver.</span>
+
+<span class="sd">    m_max : `int`, default = `100`</span>
 <span class="sd">        Maximum number of features selected in each iteration</span>
-<span class="sd">        in case of ’cg’ solver.</span>
+<span class="sd">        in case of ’ccg’ and ’cg’ solver.</span>
 
 <span class="sd">    k_max : `int`, default = `20`</span>
 <span class="sd">        Maximum number of iterations in case of ’cg’ solver.</span>
 
-<span class="sd">    eps : `float`, default = `1e-4`</span>
-<span class="sd">        Dual constraints&#39; violation threshold for ’cg’ solver. </span>
+<span class="sd">    eps1 : `float`, default = `1e-2`</span>
+<span class="sd">        Primal constraints&#39; violation threshold for ’ccg’ solver. </span>
+
+<span class="sd">    eps2 : `float`, default = `1e-5`</span>
+<span class="sd">        Dual constraints&#39; violation threshold for ’cg’/’ccg’ solver. </span>
 
 <span class="sd">    phi : `str` or `BasePhi` instance, default = &#39;linear&#39;</span>
 <span class="sd">        Type of feature mapping function to use for mapping the input data.</span>

_modules/index.html

@@ -172,6 +172,8 @@ <h1>All modules for which code is available</h1>
 <li><a href="MRCpy/phi/random_fourier_phi.html">MRCpy.phi.random_fourier_phi</a></li>
 <li><a href="MRCpy/phi/random_relu_phi.html">MRCpy.phi.random_relu_phi</a></li>
 <li><a href="MRCpy/phi/threshold_phi.html">MRCpy.phi.threshold_phi</a></li>
+<li><a href="sklearn/base.html">sklearn.base</a></li>
+<li><a href="sklearn/utils/_metadata_requests.html">sklearn.utils._metadata_requests</a></li>
 </ul>
 
            </div>

_modules/sklearn/base.html

@@ -668,7 +668,7 @@ <h3>Load classification function:<a class="headerlink" href="#load-classificatio
         <span class="s2">&quot;True Negative&quot;</span><span class="p">,</span>
         <span class="s2">&quot;False Positive&quot;</span><span class="p">,</span>
     <span class="p">]</span>
-    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Category&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">select</span></a><span class="p">(</span><span class="n">conditions</span><span class="p">,</span> <span class="n">choices</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s2">&quot;No&quot;</span><span class="p">)</span>
+    <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Category&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html#numpy.select" title="numpy.select" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">select</span></a></a></a></a></a><span class="p">(</span><span class="n">conditions</span><span class="p">,</span> <span class="n">choices</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s2">&quot;No&quot;</span><span class="p">)</span>
     <span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;Category&quot;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">df</span>