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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mirapy.data.load_dataset</span> <span class="k">import</span> <span class="n">load_atlas_star_data</span>
<span class="kn">from</span> <span class="nn">mirapy.classifiers.models</span> <span class="k">import</span> <span class="n">AtlasVarStarClassifier</span>
<span class="kn">import</span> <span class="nn">mirapy</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">os</span> <span class="k">import</span> <span class="n">walk</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="k">import</span> <span class="n">Adam</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">LabelEncoder</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Using</span> <span class="n">TensorFlow</span> <span class="n">backend</span><span class="o">.</span>
</pre></div>
</div>
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="s1">'D:\MTP\ATLAS\dataset'</span>
<span class="n">csv_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"non_dub.csv"</span><span class="p">)</span>
</pre></div>
</div>
<p>Ignore feature list to use features selected using feature selection</p>
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></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_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_atlas_star_data</span><span class="p">(</span><span class="n">csv_file</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">classifier</span> <span class="o">=</span> <span class="n">AtlasVarStarClassifier</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">,</span> <span class="s1">'adam'</span><span class="p">,</span> <span class="n">input_size</span><span class="o">=</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s1">'mean_squared_error'</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># classifier.train(x_train, y_train, epochs=50,</span>
<span class="c1"># batch_size=32)</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">model</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">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Epoch</span> <span class="mi">1</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">10</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0072</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9576</span>
<span class="n">Epoch</span> <span class="mi">2</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0073</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9567</span>
<span class="n">Epoch</span> <span class="mi">3</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0072</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9569</span>
<span class="n">Epoch</span> <span class="mi">4</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">10</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0071</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9583</span>
<span class="n">Epoch</span> <span class="mi">5</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">10</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0070</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9585</span>
<span class="n">Epoch</span> <span class="mi">6</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">10</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0069</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9592</span>
<span class="n">Epoch</span> <span class="mi">7</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0069</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9587</span>
<span class="n">Epoch</span> <span class="mi">8</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0068</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9596</span>
<span class="n">Epoch</span> <span class="mi">9</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0068</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9599</span>
<span class="n">Epoch</span> <span class="mi">10</span><span class="o">/</span><span class="mi">10</span>
<span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mf">0.0067</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mf">0.9606</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o"><</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">History</span> <span class="n">at</span> <span class="mh">0x1c568a10898</span><span class="o">></span>
</pre></div>
</div>
<p>convert string classes to integer encoded</p>
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">label_encoder</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
<span class="n">integer_encoded</span> <span class="o">=</span> <span class="n">label_encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">integer_encoded</span>
<span class="n">y_predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Accuracy:"</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="s2">"%"</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">precision</span> <span class="n">recall</span> <span class="n">f1</span><span class="o">-</span><span class="n">score</span> <span class="n">support</span>
<span class="mi">0</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mi">3373</span>
<span class="mi">1</span> <span class="mf">0.97</span> <span class="mf">0.98</span> <span class="mf">0.97</span> <span class="mi">2977</span>
<span class="mi">2</span> <span class="mf">0.84</span> <span class="mf">0.87</span> <span class="mf">0.85</span> <span class="mi">840</span>
<span class="mi">3</span> <span class="mf">0.94</span> <span class="mf">0.91</span> <span class="mf">0.93</span> <span class="mi">1406</span>
<span class="mi">4</span> <span class="mf">0.98</span> <span class="mf">0.99</span> <span class="mf">0.99</span> <span class="mi">439</span>
<span class="mi">5</span> <span class="mf">0.86</span> <span class="mf">0.80</span> <span class="mf">0.83</span> <span class="mi">396</span>
<span class="mi">6</span> <span class="mf">0.94</span> <span class="mf">0.97</span> <span class="mf">0.96</span> <span class="mi">2655</span>
<span class="mi">7</span> <span class="mf">0.98</span> <span class="mf">0.97</span> <span class="mf">0.98</span> <span class="mi">1839</span>
<span class="mi">8</span> <span class="mf">1.00</span> <span class="mf">0.98</span> <span class="mf">0.99</span> <span class="mi">2472</span>
<span class="n">micro</span> <span class="n">avg</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mi">16397</span>
<span class="n">macro</span> <span class="n">avg</span> <span class="mf">0.94</span> <span class="mf">0.94</span> <span class="mf">0.94</span> <span class="mi">16397</span>
<span class="n">weighted</span> <span class="n">avg</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mf">0.95</span> <span class="mi">16397</span>
<span class="n">Accuracy</span><span class="p">:</span> <span class="mf">95.45</span> <span class="o">%</span>
</pre></div>
</div>
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