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   <h1>Source code for apache_beam.ml.inference.xgboost_inference</h1><div class="highlight"><pre>
 <span></span><span class="c1">#</span>
 <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
 <span class="c1"># contributor license agreements.  See the NOTICE file distributed with</span>
 <span class="c1"># this work for additional information regarding copyright ownership.</span>
 <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
 <span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
 <span class="c1"># the License.  You may obtain a copy of the License at</span>
 <span class="c1">#</span>
 <span class="c1">#    http://www.apache.org/licenses/LICENSE-2.0</span>
 <span class="c1">#</span>
 <span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
 <span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
 <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
 <span class="c1"># See the License for the specific language governing permissions and</span>
 <span class="c1"># limitations under the License.</span>
 <span class="c1">#</span>

 <span class="kn">import</span> <span class="nn">sys</span>
 <span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Mapping</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>

 <span class="kn">import</span> <span class="nn">numpy</span>
 <span class="kn">import</span> <span class="nn">pandas</span>
 <span class="kn">import</span> <span class="nn">scipy</span>

 <span class="kn">import</span> <span class="nn">datatable</span>
 <span class="kn">import</span> <span class="nn">xgboost</span>
 <span class="kn">from</span> <span class="nn">apache_beam.io.filesystems</span> <span class="kn">import</span> <span class="n">FileSystems</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">ExampleT</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">ModelHandler</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">ModelT</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">PredictionResult</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">PredictionT</span>

 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
     <span class="s1">&#39;XGBoostModelHandler&#39;</span><span class="p">,</span>
     <span class="s1">&#39;XGBoostModelHandlerNumpy&#39;</span><span class="p">,</span>
     <span class="s1">&#39;XGBoostModelHandlerPandas&#39;</span><span class="p">,</span>
     <span class="s1">&#39;XGBoostModelHandlerSciPy&#39;</span><span class="p">,</span>
     <span class="s1">&#39;XGBoostModelHandlerDatatable&#39;</span>
 <span class="p">]</span>

 <span class="n">XGBoostInferenceFn</span> <span class="o">=</span> <span class="n">Callable</span><span class="p">[[</span>
     <span class="n">Sequence</span><span class="p">[</span><span class="nb">object</span><span class="p">],</span>
     <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
     <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span>
 <span class="p">],</span>
                               <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]]</span>


 <span class="k">def</span> <span class="nf">default_xgboost_inference_fn</span><span class="p">(</span>
     <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">object</span><span class="p">],</span>
     <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
     <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span>
                                   <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
   <span class="n">inference_args</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">inference_args</span> <span class="k">else</span> <span class="n">inference_args</span>

   <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">==</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">:</span>
     <span class="n">batch</span> <span class="o">=</span> <span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">DMatrix</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
   <span class="n">predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">el</span><span class="p">,</span> <span class="o">**</span><span class="n">inference_args</span><span class="p">)</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>

   <span class="k">return</span> <span class="p">[</span><span class="n">PredictionResult</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)]</span>


 <div class="viewcode-block" id="XGBoostModelHandler"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler">[docs]</a><span class="k">class</span> <span class="nc">XGBoostModelHandler</span><span class="p">(</span><span class="n">ModelHandler</span><span class="p">[</span><span class="n">ExampleT</span><span class="p">,</span> <span class="n">PredictionT</span><span class="p">,</span> <span class="n">ModelT</span><span class="p">],</span> <span class="n">ABC</span><span class="p">):</span>
   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">model_class</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">],</span>
                          <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]],</span>
       <span class="n">model_state</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
       <span class="n">inference_fn</span><span class="p">:</span> <span class="n">XGBoostInferenceFn</span> <span class="o">=</span> <span class="n">default_xgboost_inference_fn</span><span class="p">,</span>
       <span class="o">*</span><span class="p">,</span>
       <span class="n">min_batch_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
       <span class="n">max_batch_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
       <span class="n">max_batch_duration_secs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
       <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Implementation of the ModelHandler interface for XGBoost.</span>

 <span class="sd">    Example Usage::</span>

 <span class="sd">        pcoll | RunInference(</span>
 <span class="sd">                    XGBoostModelHandler(</span>
 <span class="sd">                        model_class=&quot;XGBoost Model Class&quot;,</span>
 <span class="sd">                        model_state=&quot;my_model_state.json&quot;)))</span>

 <span class="sd">    See https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html</span>
 <span class="sd">    for details</span>

 <span class="sd">    Args:</span>
 <span class="sd">      model_class: class of the XGBoost model that defines the model</span>
 <span class="sd">        structure.</span>
 <span class="sd">      model_state: path to a json file that contains the model&#39;s</span>
 <span class="sd">        configuration.</span>
 <span class="sd">      inference_fn: the inference function to use during RunInference.</span>
 <span class="sd">        default=default_xgboost_inference_fn</span>
 <span class="sd">      min_batch_size: optional. the minimum batch size to use when batching</span>
 <span class="sd">        inputs.</span>
 <span class="sd">      max_batch_size: optional. the maximum batch size to use when batching</span>
 <span class="sd">        inputs.</span>
 <span class="sd">      max_batch_duration_secs: optional. the maximum amount of time to buffer </span>
 <span class="sd">        a batch before emitting; used in streaming contexts.</span>
 <span class="sd">      kwargs: &#39;env_vars&#39; can be used to set environment variables</span>
 <span class="sd">        before loading the model.</span>

 <span class="sd">    **Supported Versions:** RunInference APIs in Apache Beam have been tested</span>
 <span class="sd">    with XGBoost 1.6.0 and 1.7.0</span>

 <span class="sd">    XGBoost 1.0.0 introduced support for using JSON to save and load</span>
 <span class="sd">    XGBoost models. XGBoost 1.6.0, additional support for Universal Binary JSON.</span>
 <span class="sd">    It is recommended to use a model trained in XGBoost 1.6.0 or higher.</span>
 <span class="sd">    While you should be able to load models created in older versions, there</span>
 <span class="sd">    are no guarantees this will work as expected.</span>

 <span class="sd">    This class is the superclass of all the various XGBoostModelhandlers</span>
 <span class="sd">    and should not be instantiated directly. (See instead</span>
 <span class="sd">    XGBoostModelHandlerNumpy, XGBoostModelHandlerPandas, etc.)</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_model_class</span> <span class="o">=</span> <span class="n">model_class</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_model_state</span> <span class="o">=</span> <span class="n">model_state</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_inference_fn</span> <span class="o">=</span> <span class="n">inference_fn</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_env_vars</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;env_vars&#39;</span><span class="p">,</span> <span class="p">{})</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_batching_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="k">if</span> <span class="n">min_batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
       <span class="bp">self</span><span class="o">.</span><span class="n">_batching_kwargs</span><span class="p">[</span><span class="s2">&quot;min_batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">min_batch_size</span>
     <span class="k">if</span> <span class="n">max_batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
       <span class="bp">self</span><span class="o">.</span><span class="n">_batching_kwargs</span><span class="p">[</span><span class="s2">&quot;max_batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_batch_size</span>
     <span class="k">if</span> <span class="n">max_batch_duration_secs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
       <span class="bp">self</span><span class="o">.</span><span class="n">_batching_kwargs</span><span class="p">[</span><span class="s2">&quot;max_batch_duration_secs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_batch_duration_secs</span>

 <div class="viewcode-block" id="XGBoostModelHandler.load_model"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.load_model">[docs]</a>  <span class="k">def</span> <span class="nf">load_model</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]:</span>
     <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_class</span><span class="p">()</span>
     <span class="n">model_state_file_handler</span> <span class="o">=</span> <span class="n">FileSystems</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_model_state</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span>
     <span class="n">model_state_bytes</span> <span class="o">=</span> <span class="n">model_state_file_handler</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
     <span class="c1"># Convert into a bytearray so that the</span>
     <span class="c1"># model state can be loaded in XGBoost</span>
     <span class="n">model_state_bytearray</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="n">model_state_bytes</span><span class="p">)</span>
     <span class="n">model</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_state_bytearray</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">model</span></div>

 <div class="viewcode-block" id="XGBoostModelHandler.get_metrics_namespace"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.get_metrics_namespace">[docs]</a>  <span class="k">def</span> <span class="nf">get_metrics_namespace</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
     <span class="k">return</span> <span class="s1">&#39;BeamML_XGBoost&#39;</span></div>

 <div class="viewcode-block" id="XGBoostModelHandler.batch_elements_kwargs"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandler.batch_elements_kwargs">[docs]</a>  <span class="k">def</span> <span class="nf">batch_elements_kwargs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batching_kwargs</span></div></div>


 <div class="viewcode-block" id="XGBoostModelHandlerNumpy"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy">[docs]</a><span class="k">class</span> <span class="nc">XGBoostModelHandlerNumpy</span><span class="p">(</span><span class="n">XGBoostModelHandler</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                                                    <span class="n">PredictionResult</span><span class="p">,</span>
                                                    <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span>
                                                          <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]]):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;Implementation of the ModelHandler interface for XGBoost</span>
 <span class="sd">  using numpy arrays as input.</span>

 <span class="sd">  Example Usage::</span>

 <span class="sd">      pcoll | RunInference(</span>
 <span class="sd">                  XGBoostModelHandlerNumpy(</span>
 <span class="sd">                      model_class=&quot;XGBoost Model Class&quot;,</span>
 <span class="sd">                      model_state=&quot;my_model_state.json&quot;)))</span>

 <span class="sd">  Args:</span>
 <span class="sd">    model_class: class of the XGBoost model that defines the model</span>
 <span class="sd">      structure.</span>
 <span class="sd">    model_state: path to a json file that contains the model&#39;s</span>
 <span class="sd">      configuration.</span>
 <span class="sd">    inference_fn: the inference function to use during RunInference.</span>
 <span class="sd">      default=default_xgboost_inference_fn</span>
 <span class="sd">  &quot;&quot;&quot;</span>
 <div class="viewcode-block" id="XGBoostModelHandlerNumpy.run_inference"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.run_inference">[docs]</a>  <span class="k">def</span> <span class="nf">run_inference</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
       <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
       <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
   <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Runs inferences on a batch of 2d numpy arrays.</span>

 <span class="sd">    Args:</span>
 <span class="sd">      batch: A sequence of examples as 2d numpy arrays. Each</span>
 <span class="sd">        row in an array is a single example. The dimensions</span>
 <span class="sd">        must match the dimensions of the data used to train</span>
 <span class="sd">        the model.</span>
 <span class="sd">      model: XGBoost booster or XBGModel (sklearn interface). Must</span>
 <span class="sd">        implement predict(X). Where the parameter X is a 2d numpy array.</span>
 <span class="sd">      inference_args: Any additional arguments for an inference.</span>

 <span class="sd">    Returns:</span>
 <span class="sd">      An Iterable of type PredictionResult.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">inference_args</span><span class="p">)</span></div>

 <div class="viewcode-block" id="XGBoostModelHandlerNumpy.get_num_bytes"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerNumpy.get_num_bytes">[docs]</a>  <span class="k">def</span> <span class="nf">get_num_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">      The number of bytes of data for a batch.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> <span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span></div></div>


 <div class="viewcode-block" id="XGBoostModelHandlerPandas"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas">[docs]</a><span class="k">class</span> <span class="nc">XGBoostModelHandlerPandas</span><span class="p">(</span><span class="n">XGBoostModelHandler</span><span class="p">[</span><span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
                                                     <span class="n">PredictionResult</span><span class="p">,</span>
                                                     <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span>
                                                           <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]]):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;Implementation of the ModelHandler interface for XGBoost</span>
 <span class="sd">  using pandas dataframes as input.</span>

 <span class="sd">  Example Usage::</span>

 <span class="sd">      pcoll | RunInference(</span>
 <span class="sd">                  XGBoostModelHandlerPandas(</span>
 <span class="sd">                      model_class=&quot;XGBoost Model Class&quot;,</span>
 <span class="sd">                      model_state=&quot;my_model_state.json&quot;)))</span>

 <span class="sd">  Args:</span>
 <span class="sd">    model_class: class of the XGBoost model that defines the model</span>
 <span class="sd">      structure.</span>
 <span class="sd">    model_state: path to a json file that contains the model&#39;s</span>
 <span class="sd">      configuration.</span>
 <span class="sd">    inference_fn: the inference function to use during RunInference.</span>
 <span class="sd">      default=default_xgboost_inference_fn</span>
 <span class="sd">  &quot;&quot;&quot;</span>
 <div class="viewcode-block" id="XGBoostModelHandlerPandas.run_inference"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.run_inference">[docs]</a>  <span class="k">def</span> <span class="nf">run_inference</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">],</span>
       <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
       <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
   <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Runs inferences on a batch of pandas dataframes.</span>

 <span class="sd">    Args:</span>
 <span class="sd">      batch: A sequence of examples as pandas dataframes. Each</span>
 <span class="sd">        row in a dataframe is a single example. The dimensions</span>
 <span class="sd">        must match the dimensions of the data used to train</span>
 <span class="sd">        the model.</span>
 <span class="sd">      model: XGBoost booster or XBGModel (sklearn interface). Must</span>
 <span class="sd">        implement predict(X). Where the parameter X is a pandas dataframe.</span>
 <span class="sd">      inference_args: Any additional arguments for an inference.</span>

 <span class="sd">    Returns:</span>
 <span class="sd">      An Iterable of type PredictionResult.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">inference_args</span><span class="p">)</span></div>

 <div class="viewcode-block" id="XGBoostModelHandlerPandas.get_num_bytes"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerPandas.get_num_bytes">[docs]</a>  <span class="k">def</span> <span class="nf">get_num_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">        The number of bytes of data for a batch of Numpy arrays.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">memory_usage</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span></div></div>


 <div class="viewcode-block" id="XGBoostModelHandlerSciPy"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy">[docs]</a><span class="k">class</span> <span class="nc">XGBoostModelHandlerSciPy</span><span class="p">(</span><span class="n">XGBoostModelHandler</span><span class="p">[</span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">,</span>
                                                    <span class="n">PredictionResult</span><span class="p">,</span>
                                                    <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span>
                                                          <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]]):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot; Implementation of the ModelHandler interface for XGBoost</span>
 <span class="sd">  using scipy matrices as input.</span>

 <span class="sd">  Example Usage::</span>

 <span class="sd">      pcoll | RunInference(</span>
 <span class="sd">                  XGBoostModelHandlerSciPy(</span>
 <span class="sd">                      model_class=&quot;XGBoost Model Class&quot;,</span>
 <span class="sd">                      model_state=&quot;my_model_state.json&quot;)))</span>

 <span class="sd">  Args:</span>
 <span class="sd">    model_class: class of the XGBoost model that defines the model</span>
 <span class="sd">      structure.</span>
 <span class="sd">    model_state: path to a json file that contains the model&#39;s</span>
 <span class="sd">      configuration.</span>
 <span class="sd">    inference_fn: the inference function to use during RunInference.</span>
 <span class="sd">      default=default_xgboost_inference_fn</span>
 <span class="sd">  &quot;&quot;&quot;</span>
 <div class="viewcode-block" id="XGBoostModelHandlerSciPy.run_inference"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.run_inference">[docs]</a>  <span class="k">def</span> <span class="nf">run_inference</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">],</span>
       <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
       <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
   <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Runs inferences on a batch of SciPy sparse matrices.</span>

 <span class="sd">    Args:</span>
 <span class="sd">      batch: A sequence of examples as Scipy sparse matrices.</span>
 <span class="sd">       The dimensions must match the dimensions of the data</span>
 <span class="sd">       used to train the model.</span>
 <span class="sd">      model: XGBoost booster or XBGModel (sklearn interface). Must implement</span>
 <span class="sd">        predict(X). Where the parameter X is a SciPy sparse matrix.</span>
 <span class="sd">      inference_args: Any additional arguments for an inference.</span>

 <span class="sd">    Returns:</span>
 <span class="sd">      An Iterable of type PredictionResult.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">inference_args</span><span class="p">)</span></div>

 <div class="viewcode-block" id="XGBoostModelHandlerSciPy.get_num_bytes"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerSciPy.get_num_bytes">[docs]</a>  <span class="k">def</span> <span class="nf">get_num_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">      The number of bytes of data for a batch.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> <span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span></div></div>


 <div class="viewcode-block" id="XGBoostModelHandlerDatatable"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable">[docs]</a><span class="k">class</span> <span class="nc">XGBoostModelHandlerDatatable</span><span class="p">(</span><span class="n">XGBoostModelHandler</span><span class="p">[</span><span class="n">datatable</span><span class="o">.</span><span class="n">Frame</span><span class="p">,</span>
                                                        <span class="n">PredictionResult</span><span class="p">,</span>
                                                        <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span>
                                                              <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">]]</span>
                                    <span class="p">):</span>
 <span class="w">  </span><span class="sd">&quot;&quot;&quot;Implementation of the ModelHandler interface for XGBoost</span>
 <span class="sd">  using datatable dataframes as input.</span>

 <span class="sd">  Example Usage::</span>

 <span class="sd">      pcoll | RunInference(</span>
 <span class="sd">                  XGBoostModelHandlerDatatable(</span>
 <span class="sd">                      model_class=&quot;XGBoost Model Class&quot;,</span>
 <span class="sd">                      model_state=&quot;my_model_state.json&quot;)))</span>

 <span class="sd">  Args:</span>
 <span class="sd">    model_class: class of the XGBoost model that defines the model</span>
 <span class="sd">      structure.</span>
 <span class="sd">    model_state: path to a json file that contains the model&#39;s</span>
 <span class="sd">      configuration.</span>
 <span class="sd">    inference_fn: the inference function to use during RunInference.</span>
 <span class="sd">      default=default_xgboost_inference_fn</span>
 <span class="sd">  &quot;&quot;&quot;</span>
 <div class="viewcode-block" id="XGBoostModelHandlerDatatable.run_inference"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.run_inference">[docs]</a>  <span class="k">def</span> <span class="nf">run_inference</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">datatable</span><span class="o">.</span><span class="n">Frame</span><span class="p">],</span>
       <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">xgboost</span><span class="o">.</span><span class="n">Booster</span><span class="p">,</span> <span class="n">xgboost</span><span class="o">.</span><span class="n">XGBModel</span><span class="p">],</span>
       <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
   <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Runs inferences on a batch of datatable dataframe.</span>

 <span class="sd">    Args:</span>
 <span class="sd">      batch: A sequence of examples as datatable dataframes. Each</span>
 <span class="sd">        row in a dataframe is a single example. The dimensions</span>
 <span class="sd">        must match the dimensions of the data used to train</span>
 <span class="sd">        the model.</span>
 <span class="sd">      model: XGBoost booster or XBGModel (sklearn interface). Must implement</span>
 <span class="sd">        predict(X). Where the parameter X is a datatable dataframe.</span>
 <span class="sd">      inference_args: Any additional arguments for an inference.</span>

 <span class="sd">    Returns:</span>
 <span class="sd">      An Iterable of type PredictionResult.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">inference_args</span><span class="p">)</span></div>

 <div class="viewcode-block" id="XGBoostModelHandlerDatatable.get_num_bytes"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.xgboost_inference.html#apache_beam.ml.inference.xgboost_inference.XGBoostModelHandlerDatatable.get_num_bytes">[docs]</a>  <span class="k">def</span> <span class="nf">get_num_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">datatable</span><span class="o">.</span><span class="n">Frame</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">      The number of bytes of data for a batch.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">getsizeof</span><span class="p">(</span><span class="n">element</span><span class="p">)</span> <span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span></div></div>
 </pre></div>

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