added early_termination criteria

Author: pwrose

example1/3-FitModel.html

@@ -12067,13 +12067,13 @@ <h2 id="Select-a-Classification-Method-(default-SVM)">Select a Classification Me
 
 
 
-<div id="603ff06b-a4df-4091-b524-3acb21a775a2"></div>
+<div id="6b3611d1-e0af-421e-b0f1-f0df1e9309d2"></div>
 <div class="output_subarea output_widget_view ">
 <script type="text/javascript">
-var element = $('#603ff06b-a4df-4091-b524-3acb21a775a2');
+var element = $('#6b3611d1-e0af-421e-b0f1-f0df1e9309d2');
 </script>
 <script type="application/vnd.jupyter.widget-view+json">
-{"model_id": "3c60c6fc2644436ebf8b9b092e6f8612", "version_major": 2, "version_minor": 0}
+{"model_id": "ab03394bfe0b457ea53b160d07957fc3", "version_major": 2, "version_minor": 0}
 </script>
 </div>
 
@@ -12101,7 +12101,8 @@ <h2 id="Train-a-classifier">Train a classifier<a class="anchor-link" href="#Trai
 <span class="k">elif</span> <span class="p">(</span><span class="n">method</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">&#39;LogisticRegression&#39;</span><span class="p">):</span>
     <span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s1">&#39;lbfgs&#39;</span><span class="p">,</span> <span class="n">multi_class</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
 <span class="k">elif</span> <span class="p">(</span><span class="n">method</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">&#39;NeuralNetwork&#39;</span><span class="p">):</span>
-    <span class="n">classifier</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span> <span class="o">=</span> <span class="p">(</span><span class="mi">20</span><span class="p">),</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+    <span class="c1"># Neural network with one hidden layer of 20 nodes</span>
+    <span class="n">classifier</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span> <span class="o">=</span> <span class="p">(</span><span class="mi">20</span><span class="p">),</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span> <span class="n">early_stopping</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 
 <span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">[</span><span class="n">feature_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">train</span><span class="p">[</span><span class="n">value_col</span><span class="p">])</span>

example1/3-FitModel.ipynb

@@ -249,7 +249,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c60c6fc2644436ebf8b9b092e6f8612",
+       "model_id": "ab03394bfe0b457ea53b160d07957fc3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -297,7 +297,8 @@
     "elif (method.value == 'LogisticRegression'):\n",
     "    classifier = LogisticRegression(class_weight='balanced', random_state=13, solver='lbfgs', multi_class='auto', max_iter=500)\n",
     "elif (method.value == 'NeuralNetwork'):\n",
-    "    classifier = MLPClassifier(hidden_layer_sizes = (20), random_state=13, max_iter=1000)\n",
+    "    # Neural network with one hidden layer of 20 nodes\n",
+    "    classifier = MLPClassifier(hidden_layer_sizes = (20), random_state=13, early_stopping=True)\n",
     "\n",
     "\n",
     "classifier.fit(train[feature_col].tolist(), train[value_col])"

example1/4-Predict.html

@@ -11855,13 +11855,13 @@ <h2 id="Enter-a-Protein-Sequence-in-Text-Box">Enter a Protein Sequence in Text B
 
 
 
-<div id="5ae3b825-8c7b-49d5-8799-28d0a7342f60"></div>
+<div id="e25fe3ca-d369-498b-84b3-ff010d88ef5f"></div>
 <div class="output_subarea output_widget_view ">
 <script type="text/javascript">
-var element = $('#5ae3b825-8c7b-49d5-8799-28d0a7342f60');
+var element = $('#e25fe3ca-d369-498b-84b3-ff010d88ef5f');
 </script>
 <script type="application/vnd.jupyter.widget-view+json">
-{"model_id": "e070e5e45045460e91c7c2b703f2e994", "version_major": 2, "version_minor": 0}
+{"model_id": "9bef296ce825422592439bdcd9ad4e96", "version_major": 2, "version_minor": 0}
 </script>
 </div>
 

example1/4-Predict.ipynb

@@ -56,7 +56,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e070e5e45045460e91c7c2b703f2e994",
+       "model_id": "9bef296ce825422592439bdcd9ad4e96",
        "version_major": 2,
        "version_minor": 0
       },