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| <div class="section" id="module-apache_beam.ml.transforms.base"> |
| <span id="apache-beam-ml-transforms-base-module"></span><h1>apache_beam.ml.transforms.base module<a class="headerlink" href="#module-apache_beam.ml.transforms.base" title="Permalink to this headline">¶</a></h1> |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.base.MLTransformProvider"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.base.</code><code class="descname">MLTransformProvider</code><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransformProvider"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransformProvider" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p> |
| <p>Data processing transforms that are intended to be used with MLTransform |
| should subclass MLTransformProvider and implement |
| get_ptransform_for_processing().</p> |
| <p>get_ptransform_for_processing() method should return a PTransform that can be |
| used to process the data.</p> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.MLTransformProvider.get_ptransform_for_processing"> |
| <code class="descname">get_ptransform_for_processing</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span> → apache_beam.transforms.ptransform.PTransform<a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransformProvider.get_ptransform_for_processing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransformProvider.get_ptransform_for_processing" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a PTransform that can be used to process the data.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.MLTransformProvider.get_counter"> |
| <code class="descname">get_counter</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransformProvider.get_counter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransformProvider.get_counter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the counter name for the data processing transform.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.base.BaseOperation"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.base.</code><code class="descname">BaseOperation</code><span class="sig-paren">(</span><em>columns: List[str]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#BaseOperation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.BaseOperation" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Generic" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Generic</span></code></a>, <a class="reference internal" href="#apache_beam.ml.transforms.base.MLTransformProvider" title="apache_beam.ml.transforms.base.MLTransformProvider"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.transforms.base.MLTransformProvider</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/abc.html#abc.ABC" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></a></p> |
| <p>Base Opertation class data processing transformations. |
| :param columns: List of column names to apply the transformation.</p> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.BaseOperation.apply_transform"> |
| <code class="descname">apply_transform</code><span class="sig-paren">(</span><em>data: OperationInputT</em>, <em>output_column_name: str</em><span class="sig-paren">)</span> → Dict[str, OperationOutputT]<a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#BaseOperation.apply_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.BaseOperation.apply_transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Define any processing logic in the apply_transform() method. |
| processing logics are applied on inputs and returns a transformed |
| output. |
| :param inputs: input data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.base.ProcessHandler"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.base.</code><code class="descname">ProcessHandler</code><span class="sig-paren">(</span><em>label=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#ProcessHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.ProcessHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/abc.html#abc.ABC" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></a></p> |
| <p>Only for internal use. No backwards compatibility guarantees.</p> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.ProcessHandler.append_transform"> |
| <code class="descname">append_transform</code><span class="sig-paren">(</span><em>transform: apache_beam.ml.transforms.base.BaseOperation</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#ProcessHandler.append_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.ProcessHandler.append_transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Append transforms to the ProcessHandler.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.base.EmbeddingsManager"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.base.</code><code class="descname">EmbeddingsManager</code><span class="sig-paren">(</span><em>columns: List[str], *, load_model_args: Optional[Dict[str, Any]] = None, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, large_model: bool = False, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#EmbeddingsManager"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.EmbeddingsManager" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="#apache_beam.ml.transforms.base.MLTransformProvider" title="apache_beam.ml.transforms.base.MLTransformProvider"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.transforms.base.MLTransformProvider</span></code></a></p> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.EmbeddingsManager.get_model_handler"> |
| <code class="descname">get_model_handler</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → apache_beam.ml.inference.base.ModelHandler<a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#EmbeddingsManager.get_model_handler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.EmbeddingsManager.get_model_handler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return framework specific model handler.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.EmbeddingsManager.get_columns_to_apply"> |
| <code class="descname">get_columns_to_apply</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#EmbeddingsManager.get_columns_to_apply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.EmbeddingsManager.get_columns_to_apply" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.base.MLTransform"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.base.</code><code class="descname">MLTransform</code><span class="sig-paren">(</span><em>*</em>, <em>write_artifact_location: Optional[str] = None</em>, <em>read_artifact_location: Optional[str] = None</em>, <em>transforms: Optional[List[apache_beam.ml.transforms.base.MLTransformProvider]] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.ptransform.html#apache_beam.transforms.ptransform.PTransform" title="apache_beam.transforms.ptransform.PTransform"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.ptransform.PTransform</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Generic" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Generic</span></code></a></p> |
| <p>MLTransform is a Beam PTransform that can be used to apply |
| transformations to the data. MLTransform is used to wrap the |
| data processing transforms provided by Apache Beam. MLTransform |
| works in two modes: write and read. In the write mode, |
| MLTransform will apply the transforms to the data and store the |
| artifacts in the write_artifact_location. In the read mode, |
| MLTransform will read the artifacts from the |
| read_artifact_location and apply the transforms to the data. The |
| artifact location should be a valid storage path where the artifacts |
| can be written to or read from.</p> |
| <p>Note that when consuming artifacts, it is not necessary to pass the |
| transforms since they are inherently stored within the artifacts |
| themselves.</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>write_artifact_location</strong> – A storage location for artifacts resulting from |
| MLTransform. These artifacts include transformations applied to |
| the dataset and generated values like min, max from ScaleTo01, |
| and mean, var from ScaleToZScore. Artifacts are produced and written |
| to this location when using <cite>write_artifact_mode</cite>. |
| Later MLTransforms can reuse produced artifacts by setting |
| <cite>read_artifact_mode</cite> instead of <cite>write_artifact_mode</cite>. The value |
| assigned to <cite>write_artifact_location</cite> should be a valid storage |
| directory that the artifacts from this transform can be written to. |
| If no directory exists at this location, one will be created. This will |
| overwrite any artifacts already in this location, so distinct locations |
| should be used for each instance of MLTransform. Only one of |
| write_artifact_location and read_artifact_location should be specified.</li> |
| <li><strong>read_artifact_location</strong> – A storage location to read artifacts resulting |
| froma previous MLTransform. These artifacts include transformations |
| applied to the dataset and generated values like min, max from |
| ScaleTo01, and mean, var from ScaleToZScore. Note that when consuming |
| artifacts, it is not necessary to pass the transforms since they are |
| inherently stored within the artifacts themselves. The value assigned |
| to <cite>read_artifact_location</cite> should be a valid storage path where the |
| artifacts can be read from. Only one of write_artifact_location and |
| read_artifact_location should be specified.</li> |
| <li><strong>transforms</strong> – A list of transforms to apply to the data. All the transforms |
| are applied in the order they are specified. The input of the |
| i-th transform is the output of the (i-1)-th transform. Multi-input |
| transforms are not supported yet.</li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.MLTransform.expand"> |
| <code class="descname">expand</code><span class="sig-paren">(</span><em>pcoll: apache_beam.pvalue.PCollection[~ExampleT][ExampleT]</em><span class="sig-paren">)</span> → Union[apache_beam.pvalue.PCollection[~MLTransformOutputT][MLTransformOutputT], Tuple[apache_beam.pvalue.PCollection[~MLTransformOutputT][MLTransformOutputT], apache_beam.pvalue.PCollection[apache_beam.pvalue.Row][apache_beam.pvalue.Row]]]<a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransform.expand"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransform.expand" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This is the entrypoint for the MLTransform. This method will |
| invoke the process_data() method of the ProcessHandler instance |
| to process the incoming data.</p> |
| <p>process_data takes in a PCollection and applies the PTransforms |
| necessary to process the data and returns a PCollection of |
| transformed data. |
| :param pcoll: A PCollection of ExampleT type.</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">Returns:</th><td class="field-body">A PCollection of MLTransformOutputT type</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.MLTransform.with_transform"> |
| <code class="descname">with_transform</code><span class="sig-paren">(</span><em>transform: apache_beam.ml.transforms.base.MLTransformProvider</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransform.with_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransform.with_transform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Add a transform to the MLTransform pipeline. |
| :param transform: A BaseOperation instance.</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">Returns:</th><td class="field-body">A MLTransform instance.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.base.MLTransform.with_exception_handling"> |
| <code class="descname">with_exception_handling</code><span class="sig-paren">(</span><em>*</em>, <em>exc_class=<class 'Exception'></em>, <em>use_subprocess=False</em>, <em>threshold=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/base.html#MLTransform.with_exception_handling"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.base.MLTransform.with_exception_handling" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
| |
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