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<li><a class="reference internal" href="#">Prediction Latency</a><ul>
<li><a class="reference internal" href="#benchmark-and-plot-helper-functions">Benchmark and plot helper functions</a></li>
<li><a class="reference internal" href="#benchmark-bulk-atomic-prediction-speed-for-various-regressors">Benchmark bulk/atomic prediction speed for various regressors</a></li>
<li><a class="reference internal" href="#benchmark-n-features-influence-on-prediction-speed">Benchmark n_features influence on prediction speed</a></li>
<li><a class="reference internal" href="#benchmark-throughput">Benchmark throughput</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="prediction-latency">
<span id="sphx-glr-auto-examples-applications-plot-prediction-latency-py"></span><h1>Prediction Latency<a class="headerlink" href="#prediction-latency" title="Link to this heading">¶</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>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Eustache Diemert <[email protected]></span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">gc</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.html#collections.defaultdict" title="collections.defaultdict" class="sphx-glr-backref-module-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">defaultdict</span></a>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_regression</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Ridge</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SGDRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR" title="sklearn.svm.SVR" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVR</span></a>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-function"><span class="n">shuffle</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="s2">"__file__"</span> <span class="ow">in</span> <span class="nb">globals</span><span class="p">()</span>
</pre></div>
</div>
<section id="benchmark-and-plot-helper-functions">
<h2>Benchmark and plot helper functions<a class="headerlink" href="#benchmark-and-plot-helper-functions" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></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="kc">False</span><span class="p">):</span>
<span class="w"> </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="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="nb">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> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><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> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><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="nb">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="https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile" title="numpy.percentile" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">percentile</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="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="w"> </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="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="nb">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> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><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> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">runtimes</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="nb">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="https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile" title="numpy.percentile" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">percentile</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="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="kc">False</span><span class="p">):</span>
<span class="w"> </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="kc">False</span><span class="p">):</span>
<span class="w"> </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="nb">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" title="sklearn.datasets.make_regression" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><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="kc">True</span>
<span class="p">)</span>
<span class="n">random_seed</span> <span class="o">=</span> <span class="mi">13</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> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</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">train_size</span><span class="o">=</span><span class="n">n_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">n_test</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_seed</span>
<span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle" class="sphx-glr-backref-module-sklearn-utils sphx-glr-backref-type-py-function"><span class="n">shuffle</span></a><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">random_state</span><span class="o">=</span><span class="n">random_seed</span><span class="p">)</span>
<span class="n">X_scaler</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_scaler</span><span class="o">.</span><span class="n">fit_transform</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_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">y_scaler</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">()</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y_scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])[:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y_test</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])[:,</span> <span class="mi">0</span><span class="p">]</span>
<a href="https://docs.python.org/3/library/gc.html#gc.collect" title="gc.collect" class="sphx-glr-backref-module-gc sphx-glr-backref-type-py-function"><span class="n">gc</span><span class="o">.</span><span class="n">collect</span></a><span class="p">()</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="nb">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="w"> </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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html#matplotlib.pyplot.boxplot" title="matplotlib.pyplot.boxplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"</span><span class="si">%s</span><span class="se">\n</span><span class="s2">(</span><span class="si">%d</span><span class="s2"> </span><span class="si">%s</span><span class="s2">)"</span>
<span class="o">%</span> <span class="p">(</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"name"</span><span class="p">],</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"complexity_computer"</span><span class="p">](</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"instance"</span><span class="p">]),</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"complexity_label"</span><span class="p">],</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="s2">"estimators"</span><span class="p">]</span>
<span class="p">]</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"boxes"</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"whiskers"</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.setp.html#matplotlib.pyplot.setp" title="matplotlib.pyplot.setp" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"fliers"</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"+"</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="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"-"</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">"major"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"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="kc">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="s2">"Prediction Time per Instance - </span><span class="si">%s</span><span class="s2">, </span><span class="si">%d</span><span class="s2"> 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="s2">"n_features"</span><span class="p">])</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="s2">"Prediction Time (us)"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="w"> </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="s2">"n_train"</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"n_test"</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"n_features"</span><span class="p">]</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="s2">"estimators"</span><span class="p">]:</span>
<span class="nb">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="s2">"instance"</span><span class="p">])</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"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>
<a href="https://docs.python.org/3/library/gc.html#gc.collect" title="gc.collect" class="sphx-glr-backref-module-gc sphx-glr-backref-type-py-function"><span class="n">gc</span><span class="o">.</span><span class="n">collect</span></a><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="s2">"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="s2">"name"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"atomic"</span><span class="p">:</span> <span class="n">a</span><span class="p">,</span> <span class="s2">"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="s2">"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="s2">"estimators"</span><span class="p">]</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="s2">"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="s2">"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="s2">"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="s2">"bulk (</span><span class="si">%d</span><span class="s2">)"</span> <span class="o">%</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"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="w"> </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> <a href="https://docs.python.org/3/library/collections.html#collections.defaultdict" title="collections.defaultdict" class="sphx-glr-backref-module-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">defaultdict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/collections.html#collections.defaultdict" title="collections.defaultdict" class="sphx-glr-backref-module-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">defaultdict</span></a><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="nb">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>
<a href="https://docs.python.org/3/library/gc.html#gc.collect" title="gc.collect" class="sphx-glr-backref-module-gc sphx-glr-backref-type-py-function"><span class="n">gc</span><span class="o">.</span><span class="n">collect</span></a><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="kc">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="https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile" title="numpy.percentile" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">percentile</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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"r"</span><span class="p">,</span> <span class="s2">"g"</span><span class="p">,</span> <span class="s2">"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="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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">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="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"-"</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">"major"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"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="kc">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="s2">"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="s2">"#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="s2">"Prediction Time at </span><span class="si">%d%%</span><span class="s2">-ile (us)"</span> <span class="o">%</span> <span class="n">percentile</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="w"> </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="s2">"n_train"</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"n_test"</span><span class="p">],</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"n_features"</span><span class="p">]</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="s2">"estimators"</span><span class="p">]:</span>
<span class="n">estimator_config</span><span class="p">[</span><span class="s2">"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> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><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><a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><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="s2">"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="s2">"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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="s2">"r"</span><span class="p">,</span> <span class="s2">"g"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">]</span>
<span class="n">cls_infos</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"</span><span class="si">%s</span><span class="se">\n</span><span class="s2">(</span><span class="si">%d</span><span class="s2"> </span><span class="si">%s</span><span class="s2">)"</span>
<span class="o">%</span> <span class="p">(</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"name"</span><span class="p">],</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"complexity_computer"</span><span class="p">](</span><span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"instance"</span><span class="p">]),</span>
<span class="n">estimator_conf</span><span class="p">[</span><span class="s2">"complexity_label"</span><span class="p">],</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="s2">"estimators"</span><span class="p">]</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="s2">"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="s2">"estimators"</span><span class="p">]</span>
<span class="p">]</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar" title="matplotlib.pyplot.bar" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="s2">"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="s2">"Prediction Throughput for different estimators (</span><span class="si">%d</span><span class="s2"> features)"</span>
<span class="o">%</span> <span class="n">configuration</span><span class="p">[</span><span class="s2">"n_features"</span><span class="p">]</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="benchmark-bulk-atomic-prediction-speed-for-various-regressors">
<h2>Benchmark bulk/atomic prediction speed for various regressors<a class="headerlink" href="#benchmark-bulk-atomic-prediction-speed-for-various-regressors" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">configuration</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"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="s2">"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="s2">"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="s2">"estimators"</span><span class="p">:</span> <span class="p">[</span>
<span class="p">{</span>
<span class="s2">"name"</span><span class="p">:</span> <span class="s2">"Linear Model"</span><span class="p">,</span>
<span class="s2">"instance"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SGDRegressor</span></a><span class="p">(</span>
<span class="n">penalty</span><span class="o">=</span><span class="s2">"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">tol</span><span class="o">=</span><span class="mf">1e-4</span>
<span class="p">),</span>
<span class="s2">"complexity_label"</span><span class="p">:</span> <span class="s2">"non-zero coefficients"</span><span class="p">,</span>
<span class="s2">"complexity_computer"</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">clf</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.count_nonzero.html#numpy.count_nonzero" title="numpy.count_nonzero" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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="p">{</span>
<span class="s2">"name"</span><span class="p">:</span> <span class="s2">"RandomForest"</span><span class="p">,</span>
<span class="s2">"instance"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestRegressor</span></a><span class="p">(),</span>
<span class="s2">"complexity_label"</span><span class="p">:</span> <span class="s2">"estimators"</span><span class="p">,</span>
<span class="s2">"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="p">{</span>
<span class="s2">"name"</span><span class="p">:</span> <span class="s2">"SVR"</span><span class="p">,</span>
<span class="s2">"instance"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR" title="sklearn.svm.SVR" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVR</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">),</span>
<span class="s2">"complexity_label"</span><span class="p">:</span> <span class="s2">"support vectors"</span><span class="p">,</span>
<span class="s2">"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="p">}</span>
<span class="n">benchmark</span><span class="p">(</span><span class="n">configuration</span><span class="p">)</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_plot_prediction_latency_001.png" srcset="../../_images/sphx_glr_plot_prediction_latency_001.png" alt="Prediction Time per Instance - Atomic, 100 feats." class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_prediction_latency_002.png" srcset="../../_images/sphx_glr_plot_prediction_latency_002.png" alt="Prediction Time per Instance - Bulk (100), 100 feats." class = "sphx-glr-multi-img"/></li>
</ul>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Benchmarking SGDRegressor(alpha=0.01, l1_ratio=0.25, penalty='elasticnet', tol=0.0001)
Benchmarking RandomForestRegressor()
Benchmarking SVR()
</pre></div>
</div>
</section>
<section id="benchmark-n-features-influence-on-prediction-speed">
<h2>Benchmark n_features influence on prediction speed<a class="headerlink" href="#benchmark-n-features-influence-on-prediction-speed" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></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="p">{</span><span class="s2">"ridge"</span><span class="p">:</span> <a href="../../modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Ridge</span></a><span class="p">()},</span>
<span class="n">configuration</span><span class="p">[</span><span class="s2">"n_train"</span><span class="p">],</span>
<span class="n">configuration</span><span class="p">[</span><span class="s2">"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="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>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_prediction_latency_003.png" srcset="../../_images/sphx_glr_plot_prediction_latency_003.png" alt="Evolution of Prediction Time with #Features" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>benchmarking with 100 features
benchmarking with 250 features
benchmarking with 500 features
</pre></div>
</div>
</section>
<section id="benchmark-throughput">
<h2>Benchmark throughput<a class="headerlink" href="#benchmark-throughput" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></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>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_prediction_latency_004.png" srcset="../../_images/sphx_glr_plot_prediction_latency_004.png" alt="Prediction Throughput for different estimators (100 features)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 16.203 seconds)</p>
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