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<div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-train-convert-predict-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
</div>
<section class="sphx-glr-example-title" id="train-convert-and-predict-with-onnx-runtime">
<span id="l-logreg-example"></span><span id="sphx-glr-auto-examples-plot-train-convert-predict-py"></span><h1>Train, convert and predict with ONNX Runtime<a class="headerlink" href="#train-convert-and-predict-with-onnx-runtime" title="Permalink to this headline"></a></h1>
<p>This example demonstrates an end to end scenario
starting with the training of a machine learned model
to its use in its converted from.</p>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><p><a class="reference internal" href="#train-a-logistic-regression" id="id1">Train a logistic regression</a></p></li>
<li><p><a class="reference internal" href="#conversion-to-onnx-format" id="id2">Conversion to ONNX format</a></p></li>
<li><p><a class="reference internal" href="#probabilities" id="id3">Probabilities</a></p></li>
<li><p><a class="reference internal" href="#benchmark-with-randomforest" id="id4">Benchmark with RandomForest</a></p></li>
</ul>
</div>
<section id="train-a-logistic-regression">
<h2><a class="toc-backref" href="#id1">Train a logistic regression</a><a class="headerlink" href="#train-a-logistic-regression" title="Permalink to this headline"></a></h2>
<p>The first step consists in retrieving the iris datset.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_iris</span>
<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>Then we fit a model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span>
<span class="n">clr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">clr</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>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>LogisticRegression()
</pre></div>
</div>
<p>We compute the prediction on the test set
and we show the confusion matrix.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">clr</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="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[13 0 0]
[ 0 10 1]
[ 0 0 14]]
</pre></div>
</div>
</section>
<section id="conversion-to-onnx-format">
<h2><a class="toc-backref" href="#id2">Conversion to ONNX format</a><a class="headerlink" href="#conversion-to-onnx-format" title="Permalink to this headline"></a></h2>
<p>We use module
<a class="reference external" href="https://github.com/onnx/sklearn-onnx">sklearn-onnx</a>
to convert the model into ONNX format.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skl2onnx</span> <span class="k">import</span> <span class="n">convert_sklearn</span>
<span class="kn">from</span> <span class="nn">skl2onnx.common.data_types</span> <span class="k">import</span> <span class="n">FloatTensorType</span>
<span class="n">initial_type</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;float_input&#39;</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">]))]</span>
<span class="n">onx</span> <span class="o">=</span> <span class="n">convert_sklearn</span><span class="p">(</span><span class="n">clr</span><span class="p">,</span> <span class="n">initial_types</span><span class="o">=</span><span class="n">initial_type</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;logreg_iris.onnx&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">onx</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
</pre></div>
</div>
<p>We load the model with ONNX Runtime and look at
its input and output.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s2">&quot;logreg_iris.onnx&quot;</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input name=&#39;</span><span class="si">{}</span><span class="s2">&#39; and shape=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output name=&#39;</span><span class="si">{}</span><span class="s2">&#39; and shape=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>input name=&#39;float_input&#39; and shape=[None, 4]
output name=&#39;output_label&#39; and shape=[None]
</pre></div>
</div>
<p>We compute the predictions.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">input_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="n">label_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="n">pred_onx</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">label_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">X_test</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">pred_onx</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[13 0 0]
[ 0 10 0]
[ 0 0 15]]
</pre></div>
</div>
<p>The prediction are perfectly identical.</p>
</section>
<section id="probabilities">
<h2><a class="toc-backref" href="#id3">Probabilities</a><a class="headerlink" href="#probabilities" title="Permalink to this headline"></a></h2>
<p>Probabilities are needed to compute other
relevant metrics such as the ROC Curve.
Let’s see how to get them first with
scikit-learn.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">prob_sklearn</span> <span class="o">=</span> <span class="n">clr</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prob_sklearn</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[2.95951945e-05 5.69800366e-02 9.42990368e-01]
[1.84923705e-05 3.93636566e-02 9.60617851e-01]
[1.30004554e-02 8.03678159e-01 1.83321386e-01]]
</pre></div>
</div>
<p>And then with ONNX Runtime.
The probabilies appear to be</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">prob_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="n">prob_rt</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">prob_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">X_test</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">pprint</span>
<span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">prob_rt</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">])</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{0: 2.9595235901069827e-05, 1: 0.05698008090257645, 2: 0.9429903030395508},
{0: 1.8492364688427188e-05, 1: 0.03936365991830826, 2: 0.9606178402900696},
{0: 0.013000461272895336, 1: 0.8036782741546631, 2: 0.1833212822675705}]
</pre></div>
</div>
<p>Let’s benchmark.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="k">import</span> <span class="n">Timer</span>
<span class="k">def</span> <span class="nf">speed</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
<span class="n">timer</span> <span class="o">=</span> <span class="n">Timer</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
<span class="n">raw</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">timer</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">repeat</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="n">number</span><span class="p">))</span>
<span class="n">ave</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span> <span class="o">/</span> <span class="n">number</span>
<span class="n">mi</span><span class="p">,</span> <span class="n">ma</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">/</span> <span class="n">number</span><span class="p">,</span> <span class="n">raw</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">/</span> <span class="n">number</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Average </span><span class="si">%1.3g</span><span class="s2"> min=</span><span class="si">%1.3g</span><span class="s2"> max=</span><span class="si">%1.3g</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">ave</span><span class="p">,</span> <span class="n">mi</span><span class="p">,</span> <span class="n">ma</span><span class="p">))</span>
<span class="k">return</span> <span class="n">ave</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for clr.predict&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;clr.predict(X_test)&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for ONNX Runtime&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
Average 4.38e-05 min=4.25e-05 max=6.07e-05
Execution time for ONNX Runtime
Average 1.97e-05 min=1.92e-05 max=2.46e-05
1.9671264999914226e-05
</pre></div>
</div>
<p>Let’s benchmark a scenario similar to what a webservice
experiences: the model has to do one prediction at a time
as opposed to a batch of prediction.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loop</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">fct</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">nrow</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="k">if</span> <span class="n">n</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">nrow</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="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">i</span> <span class="o">%</span> <span class="n">nrow</span>
<span class="n">fct</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="n">im</span><span class="p">:</span> <span class="n">im</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for clr.predict&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, clr.predict, 100)&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sess_predict</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">label_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for sess_predict&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, sess_predict, 100)&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
Average 0.00404 min=0.00402 max=0.00409
Execution time for sess_predict
Average 0.000881 min=0.000874 max=0.000912
0.0008813192099997735
</pre></div>
</div>
<p>Let’s do the same for the probabilities.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for predict_proba&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, clr.predict_proba, 100)&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sess_predict_proba</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">prob_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for sess_predict_proba&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, sess_predict_proba, 100)&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
Average 0.00599 min=0.00597 max=0.00606
Execution time for sess_predict_proba
Average 0.000883 min=0.000876 max=0.000916
0.0008830492349999729
</pre></div>
</div>
<p>This second comparison is better as
ONNX Runtime, in this experience,
computes the label and the probabilities
in every case.</p>
</section>
<section id="benchmark-with-randomforest">
<h2><a class="toc-backref" href="#id4">Benchmark with RandomForest</a><a class="headerlink" href="#benchmark-with-randomforest" title="Permalink to this headline"></a></h2>
<p>We first train and save a model in ONNX format.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">RandomForestClassifier</span>
<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
<span class="n">rf</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">initial_type</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;float_input&#39;</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]))]</span>
<span class="n">onx</span> <span class="o">=</span> <span class="n">convert_sklearn</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">initial_types</span><span class="o">=</span><span class="n">initial_type</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;rf_iris.onnx&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">onx</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
</pre></div>
</div>
<p>We compare.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s2">&quot;rf_iris.onnx&quot;</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">sess_predict_proba_rf</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">prob_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for predict_proba&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, rf.predict_proba, 100)&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Execution time for sess_predict_proba&quot;</span><span class="p">)</span>
<span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, sess_predict_proba_rf, 100)&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
Average 0.717 min=0.715 max=0.72
Execution time for sess_predict_proba
Average 0.00108 min=0.00107 max=0.00111
0.0010817126199989956
</pre></div>
</div>
<p>Let’s see with different number of trees.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">measures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">n_trees</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">n_trees</span><span class="p">)</span>
<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">n_trees</span><span class="p">)</span>
<span class="n">rf</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">initial_type</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;float_input&#39;</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]))]</span>
<span class="n">onx</span> <span class="o">=</span> <span class="n">convert_sklearn</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">initial_types</span><span class="o">=</span><span class="n">initial_type</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;rf_iris_</span><span class="si">%d</span><span class="s2">.onnx&quot;</span> <span class="o">%</span> <span class="n">n_trees</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">onx</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="s2">&quot;rf_iris_</span><span class="si">%d</span><span class="s2">.onnx&quot;</span> <span class="o">%</span> <span class="n">n_trees</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">sess_predict_proba_loop</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">prob_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)})[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">tsk</span> <span class="o">=</span> <span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, rf.predict_proba, 100)&quot;</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">trt</span> <span class="o">=</span> <span class="n">speed</span><span class="p">(</span><span class="s2">&quot;loop(X_test, sess_predict_proba_loop, 100)&quot;</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">measures</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">&#39;n_trees&#39;</span><span class="p">:</span> <span class="n">n_trees</span><span class="p">,</span> <span class="s1">&#39;sklearn&#39;</span><span class="p">:</span> <span class="n">tsk</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">:</span> <span class="n">trt</span><span class="p">})</span>
<span class="kn">from</span> <span class="nn">pandas</span> <span class="k">import</span> <span class="n">DataFrame</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">measures</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;n_trees&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;sklearn&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;scikit-learn&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;n_trees&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;rt&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;onnxruntime&quot;</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Number of trees&quot;</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">&quot;Prediction time (s)&quot;</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">&quot;Speed comparison between scikit-learn and ONNX Runtime</span><span class="se">\n</span><span class="s2">For a random forest on Iris dataset&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
</pre></div>
</div>
<img src="../images/sphx_glr_plot_train_convert_predict_001.png" srcset="../images/sphx_glr_plot_train_convert_predict_001.png" alt="Speed comparison between scikit-learn and ONNX Runtime For a random forest on Iris dataset" class = "sphx-glr-single-img"/><p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>5
Average 0.0637 min=0.0636 max=0.0639
Average 0.000869 min=0.000857 max=0.000899
10
Average 0.0982 min=0.098 max=0.0987
Average 0.000884 min=0.000873 max=0.00091
15
Average 0.133 min=0.133 max=0.133
Average 0.000895 min=0.000885 max=0.000921
20
Average 0.168 min=0.167 max=0.168
Average 0.000915 min=0.000907 max=0.00094
25
Average 0.202 min=0.201 max=0.203
Average 0.000921 min=0.000913 max=0.000948
30
Average 0.236 min=0.236 max=0.237
Average 0.000922 min=0.000914 max=0.000948
35
Average 0.271 min=0.271 max=0.271
Average 0.000941 min=0.000928 max=0.000967
40
Average 0.305 min=0.305 max=0.305
Average 0.000948 min=0.000934 max=0.000977
45
Average 0.34 min=0.339 max=0.34
Average 0.000983 min=0.000972 max=0.00101
50
Average 0.375 min=0.374 max=0.375
Average 0.000972 min=0.000966 max=0.000992
&lt;matplotlib.legend.Legend object at 0x7feaf2025c10&gt;
</pre></div>
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