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<title>Train, convert and predict with ONNX Runtime — ONNX Runtime 1.2.0 documentation</title>
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<div class="sphx-glr-download-link-note admonition note">
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<p class="admonition-title">Note</p>
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<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>
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<div class="sphx-glr-example-title section" id="train-convert-and-predict-with-onnx-runtime">
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<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>
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<p>This example demonstrates an end to end scenario
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starting with the training of a machine learned model
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to its use in its converted from.</p>
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<div class="contents local topic" id="contents">
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<ul class="simple">
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<li><p><a class="reference internal" href="#train-a-logistic-regression" id="id1">Train a logistic regression</a></p></li>
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<li><p><a class="reference internal" href="#conversion-to-onnx-format" id="id2">Conversion to ONNX format</a></p></li>
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<li><p><a class="reference internal" href="#probabilities" id="id3">Probabilities</a></p></li>
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<li><p><a class="reference internal" href="#benchmark-with-randomforest" id="id4">Benchmark with RandomForest</a></p></li>
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</ul>
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</div>
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<div class="section" id="train-a-logistic-regression">
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<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>
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<p>The first step consists in retrieving the iris datset.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
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<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
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<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>
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<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
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<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>
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</pre></div>
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</div>
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<p>Then we fit a model.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
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<span class="n">clr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
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<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>
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</pre></div>
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</div>
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<p>We compute the prediction on the test set
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and we show the confusion matrix.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
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<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>
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<span class="k">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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 9 0 0]
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[ 0 15 1]
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[ 0 1 12]]
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</pre></div>
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</div>
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</div>
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<div class="section" id="conversion-to-onnx-format">
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<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>
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<p>We use module
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<a class="reference external" href="https://github.com/onnx/sklearn-onnx">sklearn-onnx</a>
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to convert the model into ONNX format.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skl2onnx</span> <span class="kn">import</span> <span class="n">convert_sklearn</span>
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<span class="kn">from</span> <span class="nn">skl2onnx.common.data_types</span> <span class="kn">import</span> <span class="n">FloatTensorType</span>
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<span class="n">initial_type</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'float_input'</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="bp">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">]))]</span>
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<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>
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<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"logreg_iris.onnx"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
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<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>
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</pre></div>
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</div>
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<p>We load the model with ONNX Runtime and look at
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its input and output.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="kn">as</span> <span class="nn">rt</span>
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<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">"logreg_iris.onnx"</span><span class="p">)</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">"input name='{}' and shape={}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
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<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>
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<span class="k">print</span><span class="p">(</span><span class="s2">"output name='{}' and shape={}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
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<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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>input name='float_input' and shape=[None, 4]
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output name='output_label' and shape=[1]
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</pre></div>
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</div>
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<p>We compute the predictions.</p>
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<div class="highlight-python 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>
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<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>
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<span class="kn">import</span> <span class="nn">numpy</span>
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<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>
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<span class="k">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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 9 0 0]
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[ 0 16 0]
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[ 0 0 13]]
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</pre></div>
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</div>
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<p>The prediction are perfectly identical.</p>
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</div>
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<div class="section" id="probabilities">
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<h2><a class="toc-backref" href="#id3">Probabilities</a><a class="headerlink" href="#probabilities" title="Permalink to this headline">¶</a></h2>
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<p>Probabilities are needed to compute other
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relevant metrics such as the ROC Curve.
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Let’s see how to get them first with
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scikit-learn.</p>
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<div class="highlight-python 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>
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<span class="k">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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[0.00198628 0.71308408 0.28492964]
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[0.00481246 0.83770353 0.15748401]
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[0.00483108 0.75930899 0.23585993]]
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</pre></div>
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</div>
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<p>And then with ONNX Runtime.
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The probabilies appear to be</p>
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<div class="highlight-python 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>
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<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>
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<span class="kn">import</span> <span class="nn">pprint</span>
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<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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{0: 0.0019862803164869547, 1: 0.7130840420722961, 2: 0.28492966294288635},
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{0: 0.0048124659806489944, 1: 0.8377037048339844, 2: 0.15748386085033417},
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{0: 0.004831086378544569, 1: 0.7593092322349548, 2: 0.23585963249206543}]
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</pre></div>
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</div>
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<p>Let’s benchmark.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">Timer</span>
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<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>
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<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>
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<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>
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<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>
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<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>
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<span class="k">print</span><span class="p">(</span><span class="s2">"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">"</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>
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<span class="k">return</span> <span class="n">ave</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">"Execution time for clr.predict"</span><span class="p">)</span>
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<span class="n">speed</span><span class="p">(</span><span class="s2">"clr.predict(X_test)"</span><span class="p">)</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">"Execution time for ONNX Runtime"</span><span class="p">)</span>
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<span class="n">speed</span><span class="p">(</span><span class="s2">"sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]"</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
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Average 4.8e-05 min=4.63e-05 max=5.71e-05
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Execution time for ONNX Runtime
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Average 3.19e-05 min=3.08e-05 max=3.81e-05
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</pre></div>
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</div>
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<p>Let’s benchmark a scenario similar to what a webservice
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experiences: the model has to do one prediction at a time
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as opposed to a batch of prediction.</p>
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<div class="highlight-python 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="bp">None</span><span class="p">):</span>
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<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>
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<span class="k">if</span> <span class="n">n</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
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<span class="n">n</span> <span class="o">=</span> <span class="n">nrow</span>
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<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>
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||
<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="k">print</span><span class="p">(</span><span class="s2">"Execution time for clr.predict"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, clr.predict, 100)"</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="k">print</span><span class="p">(</span><span class="s2">"Execution time for sess_predict"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, sess_predict, 100)"</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 0.00445 min=0.00423 max=0.00495
|
||
Execution time for sess_predict
|
||
Average 0.00151 min=0.00145 max=0.00164
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s do the same for the probabilities.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s2">"Execution time for predict_proba"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, clr.predict_proba, 100)"</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="k">print</span><span class="p">(</span><span class="s2">"Execution time for sess_predict_proba"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, sess_predict_proba, 100)"</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 0.00643 min=0.00632 max=0.00666
|
||
Execution time for sess_predict_proba
|
||
Average 0.00158 min=0.00152 max=0.00177
|
||
</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>
|
||
</div>
|
||
<div class="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-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">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">'float_input'</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">"rf_iris.onnx"</span><span class="p">,</span> <span class="s2">"wb"</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-python 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">"rf_iris.onnx"</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="k">print</span><span class="p">(</span><span class="s2">"Execution time for predict_proba"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, rf.predict_proba, 100)"</span><span class="p">)</span>
|
||
|
||
<span class="k">print</span><span class="p">(</span><span class="s2">"Execution time for sess_predict_proba"</span><span class="p">)</span>
|
||
<span class="n">speed</span><span class="p">(</span><span class="s2">"loop(X_test, sess_predict_proba_rf, 100)"</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 0.516 min=0.483 max=0.572
|
||
Execution time for sess_predict_proba
|
||
Average 0.00176 min=0.00171 max=0.00188
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s see with different number of trees.</p>
|
||
<div class="highlight-python 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="k">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">'float_input'</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">"rf_iris_</span><span class="si">%d</span><span class="s2">.onnx"</span> <span class="o">%</span> <span class="n">n_trees</span><span class="p">,</span> <span class="s2">"wb"</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">"rf_iris_</span><span class="si">%d</span><span class="s2">.onnx"</span> <span class="o">%</span> <span class="n">n_trees</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">"loop(X_test, rf.predict_proba, 100)"</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">"loop(X_test, sess_predict_proba_loop, 100)"</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">'n_trees'</span><span class="p">:</span> <span class="n">n_trees</span><span class="p">,</span> <span class="s1">'sklearn'</span><span class="p">:</span> <span class="n">tsk</span><span class="p">,</span> <span class="s1">'rt'</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="kn">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">"n_trees"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"sklearn"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"scikit-learn"</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="bp">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">"n_trees"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"rt"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"onnxruntime"</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">"green"</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="bp">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">"Number of trees"</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">"Prediction time (s)"</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">"Speed comparison between scikit-learn and ONNX Runtime</span><span class="se">\n</span><span class="s2">For a random forest on Iris dataset"</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 alt="../_images/sphx_glr_plot_train_convert_predict_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_plot_train_convert_predict_001.png" />
|
||
<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 0.042 min=0.0412 max=0.0432
|
||
Average 0.00137 min=0.00136 max=0.00138
|
||
10
|
||
Average 0.065 min=0.0641 max=0.0662
|
||
Average 0.00151 min=0.00139 max=0.00159
|
||
15
|
||
Average 0.0886 min=0.0873 max=0.0911
|
||
Average 0.00145 min=0.00143 max=0.00148
|
||
20
|
||
Average 0.113 min=0.112 max=0.115
|
||
Average 0.0015 min=0.00149 max=0.00152
|
||
25
|
||
Average 0.14 min=0.135 max=0.15
|
||
Average 0.00161 min=0.0016 max=0.00164
|
||
30
|
||
Average 0.159 min=0.154 max=0.167
|
||
Average 0.00149 min=0.00146 max=0.00158
|
||
35
|
||
Average 0.183 min=0.177 max=0.195
|
||
Average 0.00153 min=0.0015 max=0.00159
|
||
40
|
||
Average 0.221 min=0.203 max=0.242
|
||
Average 0.0019 min=0.00153 max=0.0024
|
||
45
|
||
Average 0.23 min=0.22 max=0.248
|
||
Average 0.00177 min=0.00171 max=0.0018
|
||
50
|
||
Average 0.245 min=0.244 max=0.248
|
||
Average 0.0016 min=0.00157 max=0.00165
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Total running time of the script:</strong> ( 2 minutes 25.165 seconds)</p>
|
||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-train-convert-predict-py">
|
||
<div class="sphx-glr-download docutils container">
|
||
<p><a class="reference download internal" download="" href="../_downloads/d83345a79a181a29892287297803aeec/plot_train_convert_predict.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_train_convert_predict.py</span></code></a></p>
|
||
</div>
|
||
<div class="sphx-glr-download docutils container">
|
||
<p><a class="reference download internal" download="" href="../_downloads/3a0c2ba94405e579c84b6f356705659d/plot_train_convert_predict.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_train_convert_predict.ipynb</span></code></a></p>
|
||
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|
||
</div>
|
||
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
|
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|
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|
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|
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|
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<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||
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|
||
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|
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<img class="logo" src="../_static/ONNX_Runtime_icon.png" alt="Logo"/>
|
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|
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<h1 class="logo"><a href="../index.html">ONNX Runtime</a></h1>
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|
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|
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|
||
<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a></li>
|
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|
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<li class="toctree-l1"><a class="reference internal" href="index.html">Gallery of examples</a></li>
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|
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|
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<h3>Related Topics</h3>
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|
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<li><a href="../index.html">Documentation overview</a><ul>
|
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<li>Previous: <a href="plot_common_errors.html" title="previous chapter">Common errors with onnxruntime</a></li>
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|
||
& <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
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<a href="../_sources/auto_examples/plot_train_convert_predict.rst.txt"
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rel="nofollow">Page source</a>
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</div>
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</body>
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</html> |