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<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-1-train-a-model-using-your-favorite-framework">Step 1: Train a model using your favorite framework</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format">Step 2: Convert or export the model into ONNX format</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime">Step 3: Load and run the model using ONNX Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../api_summary.html">API Summary</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#device">Device</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#examples-and-datasets">Examples and datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#load-and-run-a-model">Load and run a model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#backend">Backend</a></li>
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</ul>
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Gallery of examples</a><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="plot_pipeline.html">Draw a pipeline</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_load_and_predict.html">Load and predict with ONNX Runtime and a very simple model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_profiling.html">Profile the execution of a simple model</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_metadata.html">Metadata</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_dl_keras.html">ONNX Runtime for Keras</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Train, convert and predict with ONNX Runtime</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_common_errors.html">Common errors with onnxruntime</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
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</ul>
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</ul>
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<div class="col-xs-12 col-sm-9">
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<div class="sphx-glr-download-link-note admonition note">
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<p class="first admonition-title">Note</p>
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<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-convert-pipeline-vectorizer-py"><span class="std std-ref">here</span></a> to download the full example code</p>
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</div>
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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="sphx-glr-auto-examples-plot-convert-pipeline-vectorizer-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 scikit-learn pipeline
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which takes as inputs not a regular vector but a
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dictionary <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">int:</span> <span class="pre">float</span> <span class="pre">}</span></code> as its first step is a
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<a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html">DictVectorizer</a>.</p>
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<div class="contents local topic" id="contents">
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<ul class="simple">
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<li><a class="reference internal" href="#train-a-pipeline" id="id1">Train a pipeline</a></li>
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<li><a class="reference internal" href="#conversion-to-onnx-format" id="id2">Conversion to ONNX format</a></li>
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</ul>
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</div>
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<div class="section" id="train-a-pipeline">
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<h2><a class="toc-backref" href="#id1">Train a pipeline</a><a class="headerlink" href="#train-a-pipeline" title="Permalink to this headline">¶</a></h2>
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<p>The first step consists in retrieving the boston datset.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
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<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
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<span class="n">boston</span> <span class="o">=</span> <span class="n">load_boston</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">boston</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">boston</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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<span class="n">X_train_dict</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
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<span class="n">X_test_dict</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
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</pre></div>
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</div>
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<p>We create a pipeline.</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.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
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<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
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<span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="kn">import</span> <span class="n">DictVectorizer</span>
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<span class="n">pipe</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
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<span class="n">DictVectorizer</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
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<span class="n">GradientBoostingRegressor</span><span class="p">())</span>
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<span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_dict</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">r2_score</span>
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<span class="n">pred</span> <span class="o">=</span> <span class="n">pipe</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_dict</span><span class="p">)</span>
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<span class="k">print</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span>
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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>0.8163803375363765
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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><span class="p">,</span> <span class="n">Int64TensorType</span><span class="p">,</span> <span class="n">DictionaryType</span><span class="p">,</span> <span class="n">SequenceType</span>
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<span class="c1"># initial_type = [('float_input', DictionaryType(Int64TensorType([1]), 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">DictionaryType</span><span class="p">(</span><span class="n">Int64TensorType</span><span class="p">([</span><span class="mi">1</span><span class="p">]),</span> <span class="n">FloatTensorType</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">pipe</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">"pipeline_vectorize.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="kn">from</span> <span class="nn">onnxruntime.capi.onnxruntime_pybind11_state</span> <span class="kn">import</span> <span class="n">InvalidArgument</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">"pipeline_vectorize.onnx"</span><span class="p">)</span>
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<span class="kn">import</span> <span class="nn">numpy</span>
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<span class="n">inp</span><span class="p">,</span> <span class="n">out</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="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>
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<span class="k">print</span><span class="p">(</span><span class="s2">"input name='{}' and shape={} and type={}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inp</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">inp</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">inp</span><span class="o">.</span><span class="n">type</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={} and type={}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">out</span><span class="o">.</span><span class="n">type</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=[] and type=map(int64,tensor(float))
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output name='variable1' and shape=[None, 1] and type=tensor(float)
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</pre></div>
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</div>
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<p>We compute the predictions.
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We could do that in one call:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</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">out</span><span class="o">.</span><span class="n">name</span><span class="p">],</span> <span class="p">{</span><span class="n">inp</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">X_test_dict</span><span class="p">})[</span><span class="mi">0</span><span class="p">]</span>
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<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="k">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
|
||
</pre></div>
|
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</div>
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<p class="sphx-glr-script-out">Out:</p>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (class onnxruntime::SequenceType<class std::vector<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > >,class std::allocator<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > > > >) , expected: (class onnxruntime::MapType<class std::map<__int64,float,struct std::less<__int64>,class std::allocator<struct std::pair<__int64 const ,float> > > >)
|
||
</pre></div>
|
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</div>
|
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<p>But it fails because, in case of a DictVectorizer,
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||
ONNX Runtime expects one observation at a time.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">pred_onx</span> <span class="o">=</span> <span class="p">[</span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">out</span><span class="o">.</span><span class="n">name</span><span class="p">],</span> <span class="p">{</span><span class="n">inp</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">row</span><span class="p">})[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">X_test_dict</span><span class="p">]</span>
|
||
</pre></div>
|
||
</div>
|
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<p>We compare them to the model’s ones.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="n">r2_score</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>
|
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</div>
|
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<p class="sphx-glr-script-out">Out:</p>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0.9999999999998694
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||
</pre></div>
|
||
</div>
|
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<p>Very similar. <em>ONNX Runtime</em> uses floats instead of doubles,
|
||
that explains the small discrepencies.</p>
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<p><strong>Total running time of the script:</strong> ( 0 minutes 1.223 seconds)</p>
|
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<a class="reference download internal" href="../_downloads/plot_convert_pipeline_vectorizer.py" download=""><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_convert_pipeline_vectorizer.py</span></code></a></div>
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<a class="reference download internal" href="../_downloads/plot_convert_pipeline_vectorizer.ipynb" download=""><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_convert_pipeline_vectorizer.ipynb</span></code></a></div>
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