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<!DOCTYPE html>
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<title><no title> — MRCpy 0.1.0 documentation</title>
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<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-example2-py"><span class="std std-ref">here</span></a>
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<p class="sphx-glr-example-title" id="sphx-glr-auto-examples-example2-py">Example of using CMRC with some of the common classification datasets.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">StratifiedKFold</span>
<span class="kn">from</span> <span class="nn">MRCpy</span> <span class="kn">import</span> <span class="n">CMRC</span>
<span class="c1"># Import the datasets</span>
<span class="kn">from</span> <span class="nn">MRCpy.datasets</span> <span class="kn">import</span> <span class="o">*</span>
<span class="c1"># Data sets</span>
<span class="n">loaders</span> <span class="o">=</span> <span class="p">[</span><span class="n">load_mammographic</span><span class="p">,</span> <span class="n">load_haberman</span><span class="p">,</span> <span class="n">load_indian_liver</span><span class="p">,</span>
<span class="n">load_diabetes</span><span class="p">,</span> <span class="n">load_credit</span><span class="p">]</span>
<span class="n">dataName</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"mammographic"</span><span class="p">,</span> <span class="s2">"haberman"</span><span class="p">,</span> <span class="s2">"indian_liver"</span><span class="p">,</span> <span class="s2">"diabetes"</span><span class="p">,</span> <span class="s2">"credit"</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="p">,</span> <span class="n">loss</span><span class="p">):</span>
<span class="n">random_seed</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">res_mean</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataName</span><span class="p">))</span>
<span class="n">res_std</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataName</span><span class="p">))</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="n">random_seed</span><span class="p">)</span>
<span class="c1"># Iterate through each of the dataset and fit the MRC classfier.</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">load</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">loaders</span><span class="p">):</span>
<span class="c1"># Loading the dataset</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
<span class="n">n</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="c1"># Print the dataset name</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" ############## </span><span class="se">\n</span><span class="s2">"</span> <span class="o">+</span> <span class="n">dataName</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+</span> <span class="s2">" n= "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span>
<span class="s2">" , d= "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">+</span> <span class="s2">", cardY= "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">r</span><span class="p">))</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">CMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="n">phi</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">use_cvx</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">solver</span><span class="o">=</span><span class="s1">'MOSEK'</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="c1"># Generate the partitions of the stratified cross-validation</span>
<span class="n">cv</span> <span class="o">=</span> <span class="n">StratifiedKFold</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_seed</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="n">random_seed</span><span class="p">)</span>
<span class="n">cvError</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">auxTime</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Paired and stratified cross-validation</span>
<span class="k">for</span> <span class="n">train_index</span><span class="p">,</span> <span class="n">test_index</span> <span class="ow">in</span> <span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="c1"># Normalizing the data</span>
<span class="n">std_scale</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">std_scale</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">std_scale</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Save start time for computing training time</span>
<span class="n">startTime</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Calculate the training time</span>
<span class="n">auxTime</span> <span class="o">+=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <span class="n">startTime</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">cvError</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy.average" title="numpy.average" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">average</span></a><span class="p">(</span><span class="n">y_pred</span> <span class="o">!=</span> <span class="n">y_test</span><span class="p">))</span>
<span class="n">res_mean</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy.average" title="numpy.average" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">average</span></a><span class="p">(</span><span class="n">cvError</span><span class="p">)</span>
<span class="n">res_std</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">cvError</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" error= "</span> <span class="o">+</span> <span class="s2">":</span><span class="se">\t</span><span class="s2">"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">res_mean</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\t</span><span class="s2">+/-</span><span class="se">\t</span><span class="s2">"</span> <span class="o">+</span>
<span class="nb">str</span><span class="p">(</span><span class="n">res_std</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">avg_train_time= "</span> <span class="o">+</span> <span class="s2">":</span><span class="se">\t</span><span class="s2">"</span> <span class="o">+</span>
<span class="nb">str</span><span class="p">(</span><span class="n">auxTime</span> <span class="o">/</span> <span class="mi">10</span><span class="p">)</span> <span class="o">+</span> <span class="s1">' secs'</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> ############## </span><span class="se">\n\n\n</span><span class="s2">"</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'******************** </span><span class="se">\</span>
<span class="s1"> Example 2 (CMRC with the additional marginal constraints) </span><span class="se">\</span>
<span class="s1"> ********************** </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t\t</span><span class="s1"> 1. Using 0-1 loss and relu feature mapping </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'0-1'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t\t</span><span class="s1"> 2. Using log loss and relu feature mapping </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'log'</span><span class="p">)</span>
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