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<div class="section" id="module-apache_beam.ml.inference.sklearn_inference">
<span id="apache-beam-ml-inference-sklearn-inference-module"></span><h1>apache_beam.ml.inference.sklearn_inference module<a class="headerlink" href="#module-apache_beam.ml.inference.sklearn_inference" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.sklearn_inference.</code><code class="descname">SklearnModelHandlerNumpy</code><span class="sig-paren">(</span><em>model_uri: str, model_file_type: apache_beam.ml.inference.sklearn_inference.ModelFileType = &lt;ModelFileType.PICKLE: 1&gt;, *, inference_fn: Callable[[sklearn.base.BaseEstimator, Sequence[numpy.ndarray], Optional[Dict[str, Any]]], Any] = &lt;function _default_numpy_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for scikit-learn
using numpy arrays as input.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span><span class="n">SklearnModelHandlerNumpy</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</span><span class="p">))</span>
</pre></div>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_uri</strong> – The URI to where the model is saved.</li>
<li><strong>model_file_type</strong> – The method of serialization of the argument.
default=pickle</li>
<li><strong>inference_fn</strong> – The inference function to use.
default=_default_numpy_inference_fn</li>
<li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
batch will be fed into the inference_fn as a Sequence of Numpy
ndarrays.</li>
<li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
batch will be fed into the inference_fn as a Sequence of Numpy
ndarrays.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; sklearn.base.BaseEstimator<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Loads and initializes a model for processing.</p>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.update_model_path">
<code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.update_model_path" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray], model: sklearn.base.BaseEstimator, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of numpy arrays.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as numpy arrays. They should
be single examples.</li>
<li><strong>model</strong> – A numpy model or pipeline. Must implement predict(X).
Where the parameter X is a numpy array.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[numpy.ndarray]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.get_metrics_namespace">
<code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.get_metrics_namespace" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A namespace for metrics collected by the RunInference transform.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.batch_elements_kwargs">
<code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerNumpy.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerNumpy.batch_elements_kwargs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="class">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas">
<em class="property">class </em><code class="descclassname">apache_beam.ml.inference.sklearn_inference.</code><code class="descname">SklearnModelHandlerPandas</code><span class="sig-paren">(</span><em>model_uri: str, model_file_type: apache_beam.ml.inference.sklearn_inference.ModelFileType = &lt;ModelFileType.PICKLE: 1&gt;, *, inference_fn: Callable[[sklearn.base.BaseEstimator, Sequence[pandas.core.frame.DataFrame], Optional[Dict[str, Any]]], Any] = &lt;function _default_pandas_inference_fn&gt;, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler" title="apache_beam.ml.inference.base.ModelHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.inference.base.ModelHandler</span></code></a></p>
<p>Implementation of the ModelHandler interface for scikit-learn that
supports pandas dataframes.</p>
<p>Example Usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcoll</span> <span class="o">|</span> <span class="n">RunInference</span><span class="p">(</span><span class="n">SklearnModelHandlerPandas</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="s2">&quot;my_uri&quot;</span><span class="p">))</span>
</pre></div>
</div>
<p><strong>NOTE:</strong> This API and its implementation are under development and
do not provide backward compatibility guarantees.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model_uri</strong> – The URI to where the model is saved.</li>
<li><strong>model_file_type</strong> – The method of serialization of the argument.
default=pickle</li>
<li><strong>inference_fn</strong> – The inference function to use.
default=_default_pandas_inference_fn</li>
<li><strong>min_batch_size</strong> – the minimum batch size to use when batching inputs. This
batch will be fed into the inference_fn as a Sequence of Pandas
Dataframes.</li>
<li><strong>max_batch_size</strong> – the maximum batch size to use when batching inputs. This
batch will be fed into the inference_fn as a Sequence of Pandas
Dataframes.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; sklearn.base.BaseEstimator<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.load_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Loads and initializes a model for processing.</p>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.update_model_path">
<code class="descname">update_model_path</code><span class="sig-paren">(</span><em>model_path: Optional[str] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.update_model_path"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.update_model_path" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.run_inference">
<code class="descname">run_inference</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame], model: sklearn.base.BaseEstimator, inference_args: Optional[Dict[str, Any]] = None</em><span class="sig-paren">)</span> &#x2192; Iterable[apache_beam.ml.inference.base.PredictionResult]<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.run_inference"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.run_inference" title="Permalink to this definition"></a></dt>
<dd><p>Runs inferences on a batch of pandas dataframes.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>batch</strong> – A sequence of examples as numpy arrays. They should
be single examples.</li>
<li><strong>model</strong> – A dataframe model or pipeline. Must implement predict(X).
Where the parameter X is a pandas dataframe.</li>
<li><strong>inference_args</strong> – Any additional arguments for an inference.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">An Iterable of type PredictionResult.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.get_num_bytes">
<code class="descname">get_num_bytes</code><span class="sig-paren">(</span><em>batch: Sequence[pandas.core.frame.DataFrame]</em><span class="sig-paren">)</span> &#x2192; int<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.get_num_bytes"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.get_num_bytes" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The number of bytes of data for a batch.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.get_metrics_namespace">
<code class="descname">get_metrics_namespace</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.get_metrics_namespace"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.get_metrics_namespace" title="Permalink to this definition"></a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A namespace for metrics collected by the RunInference transform.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.batch_elements_kwargs">
<code class="descname">batch_elements_kwargs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/inference/sklearn_inference.html#SklearnModelHandlerPandas.batch_elements_kwargs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.inference.sklearn_inference.SklearnModelHandlerPandas.batch_elements_kwargs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
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