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<li><a class="reference internal" href="#">Prediction Latency</a></li>
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<div class="section" id="prediction-latency">
<span id="example-applications-plot-prediction-latency-py"></span><h1>Prediction Latency<a class="headerlink" href="#prediction-latency" title="Permalink to this headline">¶</a></h1>
<p>This is an example showing the prediction latency of various scikit-learn
estimators.</p>
<p>The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.</p>
<p>The plots represent the distribution of the prediction latency as a boxplot.</p>
<ul class="horizontal">
<li><a class="first reference internal image-reference" href="../../_images/plot_prediction_latency_001.png"><img alt="../../_images/plot_prediction_latency_001.png" src="../../_images/plot_prediction_latency_001.png" style="width: 470.0px; height: 282.0px;" /></a>
</li>
<li><a class="first reference internal image-reference" href="../../_images/plot_prediction_latency_002.png"><img alt="../../_images/plot_prediction_latency_002.png" src="../../_images/plot_prediction_latency_002.png" style="width: 470.0px; height: 282.0px;" /></a>
</li>
<li><a class="first reference internal image-reference" href="../../_images/plot_prediction_latency_003.png"><img alt="../../_images/plot_prediction_latency_003.png" src="../../_images/plot_prediction_latency_003.png" style="width: 470.0px; height: 282.0px;" /></a>
</li>
<li><a class="first reference internal image-reference" href="../../_images/plot_prediction_latency_004.png"><img alt="../../_images/plot_prediction_latency_004.png" src="../../_images/plot_prediction_latency_004.png" style="width: 470.0px; height: 282.0px;" /></a>
</li>
</ul>
<p><strong>Script output</strong>:</p>
<div class="highlight-python"><div class="highlight"><pre>Benchmarking SGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.25, learning_rate='invscaling',
loss='squared_loss', n_iter=5, penalty='elasticnet', power_t=0.25,
random_state=None, shuffle=True, verbose=0, warm_start=False)
Benchmarking RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
Benchmarking SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
benchmarking with 100 features
benchmarking with 250 features
benchmarking with 500 features
example run in 3.86s
</pre></div>
</div>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_prediction_latency.py"><code class="xref download docutils literal"><span class="pre">plot_prediction_latency.py</span></code></a></p>
<div class="highlight-python"><div class="highlight"><pre>
<span class="c1"># Authors: Eustache Diemert <[email protected]></span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">gc</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <a href="http://docs.scipy.org/doc/scipy-0.11.0/reference/generated/scipy.stats.scoreatpercentile.html#scipy.stats.scoreatpercentile"><span class="n">scoreatpercentile</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets.samples_generator</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression"><span class="n">make_regression</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble.forest</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor"><span class="n">RandomForestRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model.ridge</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge"><span class="n">Ridge</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model.stochastic_gradient</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor"><span class="n">SGDRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm.classes</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR"><span class="n">SVR</span></a>
<span class="k">def</span> <span class="nf">_not_in_sphinx</span><span class="p">():</span>
<span class="c1"># Hack to detect whether we are running by the sphinx builder</span>
<span class="k">return</span> <span class="s1">'__file__'</span> <span class="ow">in</span> <span class="nb">globals</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">atomic_benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Measure runtime prediction of each instance."""</span>
<span class="n">n_instances</span> <span class="o">=</span> <span class="n">X_test</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">runtimes</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.zeros.html#numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="n">n_instances</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_instances</span><span class="p">):</span>
<span class="n">instance</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[[</span><span class="n">i</span><span class="p">],</span> <span class="p">:]</span>
<span class="n">start</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">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instance</span><span class="p">)</span>
<span class="n">runtimes</span><span class="p">[</span><span class="n">i</span><span class="p">]</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="o">-</span> <span class="n">start</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"atomic_benchmark runtimes:"</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">runtimes</span><span class="p">),</span> <a href="http://docs.scipy.org/doc/scipy-0.11.0/reference/generated/scipy.stats.scoreatpercentile.html#scipy.stats.scoreatpercentile"><span class="n">scoreatpercentile</span></a><span class="p">(</span>
<span class="n">runtimes</span><span class="p">,</span> <span class="mi">50</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">runtimes</span><span class="p">))</span>
<span class="k">return</span> <span class="n">runtimes</span>
<span class="k">def</span> <span class="nf">bulk_benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">n_bulk_repeats</span><span class="p">,</span> <span class="n">verbose</span><span class="p">):</span>
<span class="sd">"""Measure runtime prediction of the whole input."""</span>
<span class="n">n_instances</span> <span class="o">=</span> <span class="n">X_test</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">runtimes</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.zeros.html#numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="n">n_bulk_repeats</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_bulk_repeats</span><span class="p">):</span>
<span class="n">start</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">estimator</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="n">runtimes</span><span class="p">[</span><span class="n">i</span><span class="p">]</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="o">-</span> <span class="n">start</span>
<span class="n">runtimes</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">n_instances</span><span class="p">),</span> <span class="n">runtimes</span><span class="p">)))</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"bulk_benchmark runtimes:"</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">runtimes</span><span class="p">),</span> <a href="http://docs.scipy.org/doc/scipy-0.11.0/reference/generated/scipy.stats.scoreatpercentile.html#scipy.stats.scoreatpercentile"><span class="n">scoreatpercentile</span></a><span class="p">(</span>
<span class="n">runtimes</span><span class="p">,</span> <span class="mi">50</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">runtimes</span><span class="p">))</span>
<span class="k">return</span> <span class="n">runtimes</span>
<span class="k">def</span> <span class="nf">benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">n_bulk_repeats</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Measure runtimes of prediction in both atomic and bulk mode.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> estimator : already trained estimator supporting `predict()`</span>
<span class="sd"> X_test : test input</span>
<span class="sd"> n_bulk_repeats : how many times to repeat when evaluating bulk mode</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> atomic_runtimes, bulk_runtimes : a pair of `np.array` which contain the</span>
<span class="sd"> runtimes in seconds.</span>
<span class="sd"> """</span>
<span class="n">atomic_runtimes</span> <span class="o">=</span> <span class="n">atomic_benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
<span class="n">bulk_runtimes</span> <span class="o">=</span> <span class="n">bulk_benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">n_bulk_repeats</span><span class="p">,</span>
<span class="n">verbose</span><span class="p">)</span>
<span class="k">return</span> <span class="n">atomic_runtimes</span><span class="p">,</span> <span class="n">bulk_runtimes</span>
<span class="k">def</span> <span class="nf">generate_dataset</span><span class="p">(</span><span class="n">n_train</span><span class="p">,</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Generate a regression dataset with the given parameters."""</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"generating dataset..."</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">coef</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression"><span class="n">make_regression</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_train</span> <span class="o">+</span> <span class="n">n_test</span><span class="p">,</span>
<span class="n">n_features</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="n">noise</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">n_train</span><span class="p">]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">n_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">n_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">n_train</span><span class="p">:]</span>
<span class="n">idx</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.arange.html#numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">n_train</span><span class="p">)</span>
<a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.random.seed.html#numpy.random.seed"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_train</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_test</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="p">(</span><span class="n">y_train</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="p">(</span><span class="n">y_test</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
<span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"ok"</span><span class="p">)</span>
<span class="k">return</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="k">def</span> <span class="nf">boxplot_runtimes</span><span class="p">(</span><span class="n">runtimes</span><span class="p">,</span> <span class="n">pred_type</span><span class="p">,</span> <span class="n">configuration</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Plot a new `Figure` with boxplots of prediction runtimes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> runtimes : list of `np.array` of latencies in micro-seconds</span>
<span class="sd"> cls_names : list of estimator class names that generated the runtimes</span>
<span class="sd"> pred_type : 'bulk' or 'atomic'</span>
<span class="sd"> """</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">bp</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot"><span class="n">plt</span><span class="o">.</span><span class="n">boxplot</span></a><span class="p">(</span><span class="n">runtimes</span><span class="p">,</span> <span class="p">)</span>
<span class="n">cls_infos</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'</span><span class="si">%s</span><span class="se">\n</span><span class="s1">(</span><span class="si">%d</span><span class="s1"> </span><span class="si">%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="p">(</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'name'</span><span class="p">],</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'complexity_computer'</span><span class="p">](</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">]),</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'complexity_label'</span><span class="p">])</span> <span class="k">for</span>
<span class="n">estimator_conf</span> <span class="ow">in</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'estimators'</span><span class="p">]]</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.setp"><span class="n">plt</span><span class="o">.</span><span class="n">setp</span></a><span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">xticklabels</span><span class="o">=</span><span class="n">cls_infos</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.setp"><span class="n">plt</span><span class="o">.</span><span class="n">setp</span></a><span class="p">(</span><span class="n">bp</span><span class="p">[</span><span class="s1">'boxes'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.setp"><span class="n">plt</span><span class="o">.</span><span class="n">setp</span></a><span class="p">(</span><span class="n">bp</span><span class="p">[</span><span class="s1">'whiskers'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.setp"><span class="n">plt</span><span class="o">.</span><span class="n">setp</span></a><span class="p">(</span><span class="n">bp</span><span class="p">[</span><span class="s1">'fliers'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'+'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="bp">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgrey'</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_axisbelow</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Prediction Time per Instance - </span><span class="si">%s</span><span class="s1">, </span><span class="si">%d</span><span class="s1"> feats.'</span> <span class="o">%</span> <span class="p">(</span>
<span class="n">pred_type</span><span class="o">.</span><span class="n">capitalize</span><span class="p">(),</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_features'</span><span class="p">]))</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Prediction Time (us)'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">configuration</span><span class="p">):</span>
<span class="sd">"""Run the whole benchmark."""</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">generate_dataset</span><span class="p">(</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_train'</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'n_test'</span><span class="p">],</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_features'</span><span class="p">])</span>
<span class="n">stats</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">estimator_conf</span> <span class="ow">in</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'estimators'</span><span class="p">]:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Benchmarking"</span><span class="p">,</span> <span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">])</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">]</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">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">benchmark_estimator</span><span class="p">(</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">],</span> <span class="n">X_test</span><span class="p">)</span>
<span class="n">stats</span><span class="p">[</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'atomic'</span><span class="p">:</span> <span class="n">a</span><span class="p">,</span> <span class="s1">'bulk'</span><span class="p">:</span> <span class="n">b</span><span class="p">}</span>
<span class="n">cls_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]</span> <span class="k">for</span> <span class="n">estimator_conf</span> <span class="ow">in</span> <span class="n">configuration</span><span class="p">[</span>
<span class="s1">'estimators'</span><span class="p">]]</span>
<span class="n">runtimes</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e6</span> <span class="o">*</span> <span class="n">stats</span><span class="p">[</span><span class="n">clf_name</span><span class="p">][</span><span class="s1">'atomic'</span><span class="p">]</span> <span class="k">for</span> <span class="n">clf_name</span> <span class="ow">in</span> <span class="n">cls_names</span><span class="p">]</span>
<span class="n">boxplot_runtimes</span><span class="p">(</span><span class="n">runtimes</span><span class="p">,</span> <span class="s1">'atomic'</span><span class="p">,</span> <span class="n">configuration</span><span class="p">)</span>
<span class="n">runtimes</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e6</span> <span class="o">*</span> <span class="n">stats</span><span class="p">[</span><span class="n">clf_name</span><span class="p">][</span><span class="s1">'bulk'</span><span class="p">]</span> <span class="k">for</span> <span class="n">clf_name</span> <span class="ow">in</span> <span class="n">cls_names</span><span class="p">]</span>
<span class="n">boxplot_runtimes</span><span class="p">(</span><span class="n">runtimes</span><span class="p">,</span> <span class="s1">'bulk (</span><span class="si">%d</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'n_test'</span><span class="p">],</span>
<span class="n">configuration</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">n_feature_influence</span><span class="p">(</span><span class="n">estimators</span><span class="p">,</span> <span class="n">n_train</span><span class="p">,</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">n_features</span><span class="p">,</span> <span class="n">percentile</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Estimate influence of the number of features on prediction time.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> estimators : dict of (name (str), estimator) to benchmark</span>
<span class="sd"> n_train : nber of training instances (int)</span>
<span class="sd"> n_test : nber of testing instances (int)</span>
<span class="sd"> n_features : list of feature-space dimensionality to test (int)</span>
<span class="sd"> percentile : percentile at which to measure the speed (int [0-100])</span>
<span class="sd"> Returns:</span>
<span class="sd"> --------</span>
<span class="sd"> percentiles : dict(estimator_name,</span>
<span class="sd"> dict(n_features, percentile_perf_in_us))</span>
<span class="sd"> """</span>
<span class="n">percentiles</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="n">defaultdict</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">n_features</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"benchmarking with </span><span class="si">%d</span><span class="s2"> features"</span> <span class="o">%</span> <span class="n">n</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">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">generate_dataset</span><span class="p">(</span><span class="n">n_train</span><span class="p">,</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">for</span> <span class="n">cls_name</span><span class="p">,</span> <span class="n">estimator</span> <span class="ow">in</span> <span class="n">estimators</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">estimator</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">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="n">runtimes</span> <span class="o">=</span> <span class="n">bulk_benchmark_estimator</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="bp">False</span><span class="p">)</span>
<span class="n">percentiles</span><span class="p">[</span><span class="n">cls_name</span><span class="p">][</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1e6</span> <span class="o">*</span> <a href="http://docs.scipy.org/doc/scipy-0.11.0/reference/generated/scipy.stats.scoreatpercentile.html#scipy.stats.scoreatpercentile"><span class="n">scoreatpercentile</span></a><span class="p">(</span><span class="n">runtimes</span><span class="p">,</span>
<span class="n">percentile</span><span class="p">)</span>
<span class="k">return</span> <span class="n">percentiles</span>
<span class="k">def</span> <span class="nf">plot_n_features_influence</span><span class="p">(</span><span class="n">percentiles</span><span class="p">,</span> <span class="n">percentile</span><span class="p">):</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'r'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">cls_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">percentiles</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">x</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="nb">sorted</span><span class="p">([</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">percentiles</span><span class="p">[</span><span class="n">cls_name</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
<span class="n">y</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="n">percentiles</span><span class="p">[</span><span class="n">cls_name</span><span class="p">][</span><span class="n">n</span><span class="p">]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><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">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="bp">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgrey'</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_axisbelow</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Evolution of Prediction Time with #Features'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'#Features'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Prediction Time at </span><span class="si">%d%%</span><span class="s1">-ile (us)'</span> <span class="o">%</span> <span class="n">percentile</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
<span class="k">def</span> <span class="nf">benchmark_throughputs</span><span class="p">(</span><span class="n">configuration</span><span class="p">,</span> <span class="n">duration_secs</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
<span class="sd">"""benchmark throughput for different estimators."""</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">generate_dataset</span><span class="p">(</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_train'</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'n_test'</span><span class="p">],</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_features'</span><span class="p">])</span>
<span class="n">throughputs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">estimator_config</span> <span class="ow">in</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'estimators'</span><span class="p">]:</span>
<span class="n">estimator_config</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">]</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">start_time</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">n_predictions</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</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">start_time</span><span class="p">)</span> <span class="o"><</span> <span class="n">duration_secs</span><span class="p">:</span>
<span class="n">estimator_config</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">]</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="mi">0</span><span class="p">]])</span>
<span class="n">n_predictions</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">throughputs</span><span class="p">[</span><span class="n">estimator_config</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]]</span> <span class="o">=</span> <span class="n">n_predictions</span> <span class="o">/</span> <span class="n">duration_secs</span>
<span class="k">return</span> <span class="n">throughputs</span>
<span class="k">def</span> <span class="nf">plot_benchmark_throughput</span><span class="p">(</span><span class="n">throughputs</span><span class="p">,</span> <span class="n">configuration</span><span class="p">):</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'r'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">]</span>
<span class="n">cls_infos</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'</span><span class="si">%s</span><span class="se">\n</span><span class="s1">(</span><span class="si">%d</span><span class="s1"> </span><span class="si">%s</span><span class="s1">)'</span> <span class="o">%</span> <span class="p">(</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'name'</span><span class="p">],</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'complexity_computer'</span><span class="p">](</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'instance'</span><span class="p">]),</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'complexity_label'</span><span class="p">])</span> <span class="k">for</span>
<span class="n">estimator_conf</span> <span class="ow">in</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'estimators'</span><span class="p">]]</span>
<span class="n">cls_values</span> <span class="o">=</span> <span class="p">[</span><span class="n">throughputs</span><span class="p">[</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]]</span> <span class="k">for</span> <span class="n">estimator_conf</span> <span class="ow">in</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'estimators'</span><span class="p">]]</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar"><span class="n">plt</span><span class="o">.</span><span class="n">bar</span></a><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">throughputs</span><span class="p">)),</span> <span class="n">cls_values</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.linspace.html#numpy.linspace"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">throughputs</span><span class="p">)</span> <span class="o">-</span> <span class="mf">0.75</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">throughputs</span><span class="p">)))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">cls_infos</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">ymax</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">cls_values</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.2</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">ymax</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Throughput (predictions/sec)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Prediction Throughput for different estimators (</span><span class="si">%d</span><span class="s1"> '</span>
<span class="s1">'features)'</span> <span class="o">%</span> <span class="n">configuration</span><span class="p">[</span><span class="s1">'n_features'</span><span class="p">])</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
<span class="c1">###############################################################################</span>
<span class="c1"># main code</span>
<span class="n">start_time</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"># benchmark bulk/atomic prediction speed for various regressors</span>
<span class="n">configuration</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'n_train'</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e3</span><span class="p">),</span>
<span class="s1">'n_test'</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e2</span><span class="p">),</span>
<span class="s1">'n_features'</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e2</span><span class="p">),</span>
<span class="s1">'estimators'</span><span class="p">:</span> <span class="p">[</span>
<span class="p">{</span><span class="s1">'name'</span><span class="p">:</span> <span class="s1">'Linear Model'</span><span class="p">,</span>
<span class="s1">'instance'</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor"><span class="n">SGDRegressor</span></a><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s1">'elasticnet'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
<span class="n">l1_ratio</span><span class="o">=</span><span class="mf">0.25</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="s1">'complexity_label'</span><span class="p">:</span> <span class="s1">'non-zero coefficients'</span><span class="p">,</span>
<span class="s1">'complexity_computer'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">clf</span><span class="p">:</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.count_nonzero.html#numpy.count_nonzero"><span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span></a><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">)},</span>
<span class="p">{</span><span class="s1">'name'</span><span class="p">:</span> <span class="s1">'RandomForest'</span><span class="p">,</span>
<span class="s1">'instance'</span><span class="p">:</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor"><span class="n">RandomForestRegressor</span></a><span class="p">(),</span>
<span class="s1">'complexity_label'</span><span class="p">:</span> <span class="s1">'estimators'</span><span class="p">,</span>
<span class="s1">'complexity_computer'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">clf</span><span class="p">:</span> <span class="n">clf</span><span class="o">.</span><span class="n">n_estimators</span><span class="p">},</span>
<span class="p">{</span><span class="s1">'name'</span><span class="p">:</span> <span class="s1">'SVR'</span><span class="p">,</span>
<span class="s1">'instance'</span><span class="p">:</span> <a href="../../modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR"><span class="n">SVR</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'rbf'</span><span class="p">),</span>
<span class="s1">'complexity_label'</span><span class="p">:</span> <span class="s1">'support vectors'</span><span class="p">,</span>
<span class="s1">'complexity_computer'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">clf</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">)},</span>
<span class="p">]</span>
<span class="p">}</span>
<span class="n">benchmark</span><span class="p">(</span><span class="n">configuration</span><span class="p">)</span>
<span class="c1"># benchmark n_features influence on prediction speed</span>
<span class="n">percentile</span> <span class="o">=</span> <span class="mi">90</span>
<span class="n">percentiles</span> <span class="o">=</span> <span class="n">n_feature_influence</span><span class="p">({</span><span class="s1">'ridge'</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge"><span class="n">Ridge</span></a><span class="p">()},</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_train'</span><span class="p">],</span>
<span class="n">configuration</span><span class="p">[</span><span class="s1">'n_test'</span><span class="p">],</span>
<span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="mi">500</span><span class="p">],</span> <span class="n">percentile</span><span class="p">)</span>
<span class="n">plot_n_features_influence</span><span class="p">(</span><span class="n">percentiles</span><span class="p">,</span> <span class="n">percentile</span><span class="p">)</span>
<span class="c1"># benchmark throughput</span>
<span class="n">throughputs</span> <span class="o">=</span> <span class="n">benchmark_throughputs</span><span class="p">(</span><span class="n">configuration</span><span class="p">)</span>
<span class="n">plot_benchmark_throughput</span><span class="p">(</span><span class="n">throughputs</span><span class="p">,</span> <span class="n">configuration</span><span class="p">)</span>
<span class="n">stop_time</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="k">print</span><span class="p">(</span><span class="s2">"example run in </span><span class="si">%.2f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><span class="n">stop_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">))</span>
</pre></div>
</div>
<p><strong>Total running time of the example:</strong> 3.86 seconds
( 0 minutes 3.86 seconds)</p>
</div>
</div>
</div>
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