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Author: Adwaith-Rajesh

ml/logregress/index.html

@@ -660,7 +660,88 @@ <h2 id="the-math">The math</h2>
 \]</div>
 <p>When you have the final values from your derivative calculation, you can use it in the gradient descent equation and update the weights and bias.</p>
 <h2 id="the-code">The code</h2>
-<p>Coming soon</p>
+<p>The data used here is the <a href="https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data">Breast Cancer Wisconsin (Diagnostic) Data Set</a> which has bee modified to look like <a href="https://gitlab.com/adwaithrajesh/linear-ml-test/-/blob/main/data/bcancer.csv">this</a>, where we
+don't have id's and M=0, and B=1</p>
+<div class="highlight"><pre><span></span><code><span class="cp">#define INCLUDE_MAT_CONVERSIONS</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;ds/mat.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;ml/logisticregress.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;model/metrics.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;model/train_test_split.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;parsers/csv.h&quot;</span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">CSV</span><span class="w"> </span><span class="o">*</span><span class="n">csv_reader</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">csv_init</span><span class="p">(</span><span class="mi">569</span><span class="p">,</span><span class="w"> </span><span class="mi">31</span><span class="p">,</span><span class="w"> </span><span class="sc">&#39;,&#39;</span><span class="p">);</span>
+<span class="w">    </span><span class="n">csv_parse</span><span class="p">(</span><span class="n">csv_reader</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;data/bcancer.csv&quot;</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">Mat</span><span class="w"> </span><span class="o">*</span><span class="n">X</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">csv_get_mat_slice</span><span class="p">(</span><span class="n">csv_reader</span><span class="p">,</span><span class="w"> </span><span class="p">(</span><span class="n">Slice</span><span class="p">){</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">31</span><span class="p">});</span>
+<span class="w">    </span><span class="n">Mat</span><span class="w"> </span><span class="o">*</span><span class="n">Y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">csv_get_mat_slice</span><span class="p">(</span><span class="n">csv_reader</span><span class="p">,</span><span class="w"> </span><span class="p">(</span><span class="n">Slice</span><span class="p">){</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">});</span>
+<span class="w">    </span><span class="n">Mat</span><span class="w"> </span><span class="o">*</span><span class="n">X_train</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">X_test</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">Y_train</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">Y_test</span><span class="p">;</span>
+
+<span class="w">    </span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="w"> </span><span class="n">Y</span><span class="p">,</span><span class="w"> </span><span class="o">&amp;</span><span class="n">X_train</span><span class="p">,</span><span class="w"> </span><span class="o">&amp;</span><span class="n">X_test</span><span class="p">,</span><span class="w"> </span><span class="o">&amp;</span><span class="n">Y_train</span><span class="p">,</span><span class="w"> </span><span class="o">&amp;</span><span class="n">Y_test</span><span class="p">,</span><span class="w"> </span><span class="mf">0.3</span><span class="p">,</span><span class="w"> </span><span class="mi">101</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">logregress_set_max_iter</span><span class="p">(</span><span class="mi">2000</span><span class="p">);</span>
+<span class="w">    </span><span class="n">LogisticRegressionModel</span><span class="w"> </span><span class="o">*</span><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">logregress_init</span><span class="p">();</span>
+<span class="w">    </span><span class="n">logregress_fit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">X_train</span><span class="p">,</span><span class="w"> </span><span class="n">Y_train</span><span class="p">);</span>
+
+<span class="w">    </span><span class="c1">// printf(&quot;prediction: %lf\n&quot;, logregress_predict(model, (double[]){15.22, 30.62, 103.4, 716.9, ... , 0}, 30));</span>
+<span class="w">    </span><span class="n">Array</span><span class="w"> </span><span class="o">*</span><span class="n">preds</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">logregress_predict_many</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">X_test</span><span class="p">);</span>
+<span class="w">    </span><span class="n">Array</span><span class="w"> </span><span class="o">*</span><span class="nb">true</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mat_get_col_arr</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">logregress_print</span><span class="p">(</span><span class="n">model</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">printf</span><span class="p">(</span><span class="s">&quot;confusion matrix: </span><span class="se">\n</span><span class="s">&quot;</span><span class="p">);</span>
+<span class="w">    </span><span class="n">Mat</span><span class="w"> </span><span class="o">*</span><span class="n">conf_mat</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model_confusion_matrix</span><span class="p">(</span><span class="nb">true</span><span class="p">,</span><span class="w"> </span><span class="n">preds</span><span class="p">);</span>
+<span class="w">    </span><span class="n">mat_print</span><span class="p">(</span><span class="n">conf_mat</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">arr_free</span><span class="p">(</span><span class="nb">true</span><span class="p">);</span>
+<span class="w">    </span><span class="n">arr_free</span><span class="p">(</span><span class="n">preds</span><span class="p">);</span>
+<span class="w">    </span><span class="n">logregress_free</span><span class="p">(</span><span class="n">model</span><span class="p">);</span>
+<span class="w">    </span><span class="n">mat_free_many</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="p">,</span><span class="w"> </span><span class="n">Y</span><span class="p">,</span><span class="w"> </span><span class="n">X_test</span><span class="p">,</span><span class="w"> </span><span class="n">X_train</span><span class="p">,</span><span class="w"> </span><span class="n">Y_test</span><span class="p">,</span><span class="w"> </span><span class="n">Y_train</span><span class="p">,</span><span class="w"> </span><span class="n">conf_mat</span><span class="p">);</span>
+<span class="w">    </span><span class="n">csv_free</span><span class="p">(</span><span class="n">csv_reader</span><span class="p">);</span>
+<span class="p">}</span>
+</code></pre></div>
+<div class="highlight"><pre><span></span><code><span class="go">LogisticRegressionModel(bias: 0.5159147, loss: -12.4263621, weights: 0x5556e8a732c0)</span>
+<span class="go">weights:</span>
+<span class="go">1546.6922009</span>
+<span class="go">1139.6829595</span>
+<span class="go">8552.1648900</span>
+<span class="go">2522.0044946</span>
+<span class="go">11.8724211</span>
+<span class="go">-19.3345598</span>
+<span class="go">-44.9646156</span>
+<span class="go">-18.4984994</span>
+<span class="go">23.8378678</span>
+<span class="go">10.1676564</span>
+<span class="go">0.2338315</span>
+<span class="go">103.3839701</span>
+<span class="go">-139.7864354</span>
+<span class="go">-4498.8563443</span>
+<span class="go">0.2662770</span>
+<span class="go">-6.5798244</span>
+<span class="go">-8.6158697</span>
+<span class="go">-1.6938180</span>
+<span class="go">1.6508702</span>
+<span class="go">-0.3857419</span>
+<span class="go">1650.7843571</span>
+<span class="go">1445.0283208</span>
+<span class="go">8312.7672485</span>
+<span class="go">-4024.9280673</span>
+<span class="go">13.2972726</span>
+<span class="go">-72.4527931</span>
+<span class="go">-111.8298475</span>
+<span class="go">-26.6204266</span>
+<span class="go">28.0612275</span>
+<span class="go">5.4099162</span>
+<span class="go">confusion matrix:</span>
+<span class="go">  57.00   10.00</span>
+<span class="go">  2.00   101.00</span>
+</code></pre></div>
+<p>Now, what does the confusion matrix generated by sklean look like.</p>
+<div class="highlight"><pre><span></span><code><span class="n">array</span><span class="p">([[</span> <span class="mi">59</span><span class="p">,</span>   <span class="mi">7</span><span class="p">],</span>
+       <span class="p">[</span>  <span class="mi">3</span><span class="p">,</span> <span class="mi">102</span><span class="p">]])</span>
+</code></pre></div>
+<p>we are pretty close...
+checkout the python implementation <a href="https://gitlab.com/adwaithrajesh/linear-ml-test/-/blob/main/notebooks/log.ipynb">here</a></p>