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<div class="section" id="training-higgs-using-ibm-snap-ml">
<span id="notebook-higgs-local"></span><h1>Training HIGGS using IBM Snap ML<a class="headerlink" href="#training-higgs-using-ibm-snap-ml" title="Permalink to this headline">¶</a></h1>
<p>In this example we will train a Decision Tree model on the HIGGS dataset, using both <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> and <code class="docutils literal notranslate"><span class="pre">snap-ml-local</span></code>.</p>
<div class="section" id="getting-the-data">
<h2>Getting the Data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">¶</a></h2>
<p>Download and decompress the data from the LIBSVM repository:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mkdir</span> <span class="n">data</span>
<span class="n">cd</span> <span class="n">data</span>
<span class="n">wget</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">www</span><span class="o">.</span><span class="n">csie</span><span class="o">.</span><span class="n">ntu</span><span class="o">.</span><span class="n">edu</span><span class="o">.</span><span class="n">tw</span><span class="o">/~</span><span class="n">cjlin</span><span class="o">/</span><span class="n">libsvmtools</span><span class="o">/</span><span class="n">datasets</span><span class="o">/</span><span class="n">binary</span><span class="o">/</span><span class="n">HIGGS</span><span class="o">.</span><span class="n">bz2</span>
<span class="n">bunzip2</span> <span class="n">HIGGS</span><span class="o">.</span><span class="n">bz2</span>
<span class="n">cd</span> <span class="o">../</span>
</pre></div>
</div>
</div>
<div class="section" id="data-preprocessing">
<h2>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">¶</a></h2>
<p>The data is in SvmLight format which is not very efficient since this dataset is dense. Therefore, we suggest to perform the following pre-processing, which converts it to dense format, performs normalization and then dumps it to numpy binary format for fast loading. Note that <code class="docutils literal notranslate"><span class="pre">snapml</span></code> is compatible with <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>. Thus we can use the broad functionality of scikit-learn to perform preprocessing as needed.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="c1"># import preprocessing functions from scikit-learn</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_svmlight_file</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">normalize</span>
<span class="c1"># load data from libsvm format</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span> <span class="o">=</span> <span class="n">load_svmlight_file</span><span class="p">(</span><span class="s2">"data/HIGGS"</span><span class="p">)</span>
<span class="c1"># Make the train-test split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</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">train_test_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">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="c1"># Convert to numpy ararys</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="c1"># Normalize the training data</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">)</span>
<span class="c1"># Save the dense matrices</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"data/HIGGS.X_train"</span><span class="p">,</span> <span class="n">X_train</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"data/HIGGS.X_test"</span><span class="p">,</span> <span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Save the labels</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"data/HIGGS.y_train"</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"data/HIGGS.y_test"</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="training-and-evaluating-a-decision-tree">
<h2>Training and Evaluating a Decision Tree<a class="headerlink" href="#training-and-evaluating-a-decision-tree" title="Permalink to this headline">¶</a></h2>
<p>In the following we will show how to train a decision tree classifier using <code class="docutils literal notranslate"><span class="pre">snapml</span></code> on the HIGGS dataset.
Therefore we first load the preprocessed data for numpy binary format</p>
<div class="highlight-python 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="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
<span class="c1"># load the data</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"data/HIGGS.X_train.npy"</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"data/HIGGS.X_test.npy"</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"data/HIGGS.y_train.npy"</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"data/HIGGS.y_test.npy"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Data load time (s): {0:.2f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
</pre></div>
</div>
<p>Then we specify the model parameters and initialize the decision tree classifier</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># specify model parameters</span>
<span class="n">max_depth</span> <span class="o">=</span> <span class="bp">None</span>
<span class="c1"># import Snap ML DecisionTreeClassifier from pai4sk module directly</span>
<span class="kn">from</span> <span class="nn">pai4sk</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span> <span class="k">as</span> <span class="n">SnapTree</span>
<span class="c1"># initialize classifier</span>
<span class="n">dt</span> <span class="o">=</span> <span class="n">SnapTree</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">)</span>
</pre></div>
</div>
<p>In the next step we train our classifier on the training dataset. We will introduce a parameter <code class="docutils literal notranslate"><span class="pre">num_ex_used</span></code> for the user to specify how many examples should be used for training. This serves for reducing runtimes for testing.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># specify how many examples should be used for training</span>
<span class="n">num_ex_used</span> <span class="o">=</span> <span class="n">X_train</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="c1"># use the full training set</span>
<span class="c1"># Training</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">dt</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="mi">0</span><span class="p">:</span><span class="n">num_ex_used</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">num_ex_used</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"[snap] Training time (s): {0:.2f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
</pre></div>
</div>
<p>After the training has finished, we can validate the predictive performance of our model on the hold-out test set. Again, we have the option to reuse evaluation metrics implemented in scikit-learn to evaluate our model.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Inference</span>
<span class="n">pred_test</span> <span class="o">=</span> <span class="n">dt</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="c1"># Evaluate accuracy_score on test set</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="n">acc_snap</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"[snap] Accuracy score: {0:.4f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">acc_snap</span><span class="p">))</span>
</pre></div>
</div>
<p>For the user interested in the performance comparison of <code class="docutils literal notranslate"><span class="pre">snapml</span></code> to the standard <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> library, we will show how the same classifier can be trained using <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>. This only requires minimal changes to the above code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># load data and specify parameters as in the example above</span>
<span class="c1"># [...]</span>
<span class="c1"># Import DecisionTreeClassifier from sklearn</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span> <span class="k">as</span> <span class="n">skTree</span>
<span class="n">dt</span> <span class="o">=</span> <span class="n">skTree</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">presort</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">)</span>
<span class="c1"># Training time</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">dt</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="mi">0</span><span class="p">:</span><span class="n">n_ex</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n_ex</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"[sklearn] Training time (s): {0:.2f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
<span class="c1"># Inference</span>
<span class="n">pred_test</span> <span class="o">=</span> <span class="n">dt</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="c1"># Evaluate accuracy_score on test set</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="n">acc_sklearn</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"[sklearn] Accuracy score: {0:.4f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">acc_sklearn</span><span class="p">))</span>
</pre></div>
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