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<div class="section" id="credit-card-fraud-detection-using-snap-ml-local">
<span id="notebook-credit-local"></span><h1>Credit Card Fraud Detection using snap-ml-local<a class="headerlink" href="#credit-card-fraud-detection-using-snap-ml-local" title="Permalink to this headline">¶</a></h1>
<p>In this example we will train a Logistic Regression model a credit card fraud dataset, using <code class="docutils literal notranslate"><span class="pre">snap-ml-local</span></code>.</p>
<div class="section" id="getting-data">
<h2>Getting Data<a class="headerlink" href="#getting-data" title="Permalink to this headline">¶</a></h2>
<p>For this example we use the dataset from the <a class="reference external" href="https://www.kaggle.com/mlg-ulb/creditcardfraud/data">Kaggle credit card fraud detection competition</a>. To prepare the data, first, create a new directory:</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>
</pre></div>
</div>
<p>and then download the data from the <a class="reference external" href="https://www.kaggle.com/mlg-ulb/creditcardfraud/data">Kaggle webpage</a> into the data directory and unzip it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">unzip</span> <span class="n">creditcardfraud</span><span class="o">.</span><span class="n">zip</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>Before doing the training we show how to preprocess the dataset and dump it into numpy binary format for fast loading</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">StratifiedShuffleSplit</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">normalize</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</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 the data from csv format</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data/creditcard.csv"</span><span class="p">)</span>
<span class="c1"># Standardize features by removing the mean and scaling to unit variance</span>
<span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">29</span><span class="p">]</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">29</span><span class="p">])</span>
<span class="c1"># Convert the data frame to its Numpy-array representation</span>
<span class="n">data_matrix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">data_matrix</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">29</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">data_matrix</span><span class="p">[:,</span> <span class="mi">30</span><span class="p">]</span>
<span class="c1"># Normalize the data</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s2">"l1"</span><span class="p">)</span>
<span class="c1"># Split the data in train and test</span>
<span class="n">stratSplit</span> <span class="o">=</span> <span class="n">StratifiedShuffleSplit</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">1</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="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">stratSplit</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"># 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/creditcard.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/creditcard.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/creditcard.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/creditcard.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-using-snap-ml">
<h2>Training using Snap ML<a class="headerlink" href="#training-using-snap-ml" title="Permalink to this headline">¶</a></h2>
<p>After preprocessing the data you are good to go and train a logistic regression classifier using <code class="docutils literal notranslate"><span class="pre">snap-ml-local</span></code>.</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="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">load_npz</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">roc_curve</span><span class="p">,</span> <span class="n">auc</span><span class="p">,</span> <span class="n">precision_recall_curve</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">StratifiedShuffleSplit</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"># timing</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="c1"># Import the data</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/creditcard.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/creditcard.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/creditcard.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/creditcard.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>
<span class="c1"># specify whether to use GPUs for training or not</span>
<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">True</span>
<span class="n">device_ids</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="n">use_gpu</span><span class="p">:</span>
<span class="n">num_threads</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">cpu_gpu</span> <span class="o">=</span> <span class="s2">"GPU"</span>
<span class="c1"># specify how many and which GPUs to use</span>
<span class="n">device_ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">num_threads</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">cpu_gpu</span> <span class="o">=</span> <span class="s2">"CPU"</span>
<span class="c1"># specify whether to balance class weights</span>
<span class="n">use_balanced_class_weights</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">if</span> <span class="n">use_balanced_class_weights</span><span class="p">:</span>
<span class="n">class_weight</span> <span class="o">=</span> <span class="s2">"balanced"</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">class_weight</span> <span class="o">=</span> <span class="bp">None</span>
<span class="c1"># Import the LogisticRegression classifier from pai4sk</span>
<span class="kn">from</span> <span class="nn">pai4sk</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="c1"># Alternatively you can also use the LogisticRegression classifier from pai4sk.linear_model</span>
<span class="c1"># from pai4sk.linear_model import LogisticRegression</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">use_gpu</span> <span class="o">=</span> <span class="n">use_gpu</span><span class="p">,</span> <span class="n">device_ids</span> <span class="o">=</span> <span class="n">device_ids</span><span class="p">,</span>
<span class="n">num_threads</span> <span class="o">=</span> <span class="n">num_threads</span><span class="p">,</span> <span class="n">class_weight</span> <span class="o">=</span> <span class="n">class_weight</span><span class="p">,</span>
<span class="n">fit_intercept</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">regularizer</span> <span class="o">=</span> <span class="mi">100</span><span class="p">)</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">lr</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="k">print</span><span class="p">(</span><span class="s2">"[pai4sk] Training time (s): {1:.2f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cpu_gpu</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"># set num_threads to use for inference</span>
<span class="n">num_threads_inference</span> <span class="o">=</span> <span class="mi">2</span>
<span class="c1"># Evaluate log-loss on test set</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span><span class="n">num_threads</span> <span class="o">=</span> <span class="n">num_threads_inference</span><span class="p">)[:,</span><span class="mi">1</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">average_precision_score</span>
<span class="n">acc_snap</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"[pai4sk] Average Precision Score : {1:.4f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cpu_gpu</span><span class="p">,</span> <span class="n">acc_snap</span><span class="p">))</span>
</pre></div>
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