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| <div class="section" id="module-apache_beam.ml.transforms.handlers"> |
| <span id="apache-beam-ml-transforms-handlers-module"></span><h1>apache_beam.ml.transforms.handlers module<a class="headerlink" href="#module-apache_beam.ml.transforms.handlers" title="Permalink to this headline">¶</a></h1> |
| <dl class="class"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler"> |
| <em class="property">class </em><code class="descclassname">apache_beam.ml.transforms.handlers.</code><code class="descname">TFTProcessHandler</code><span class="sig-paren">(</span><em>*</em>, <em>artifact_location: str</em>, <em>transforms: Optional[Sequence[apache_beam.ml.transforms.tft.TFTOperation]] = None</em>, <em>artifact_mode: str = 'produce'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <a class="reference internal" href="apache_beam.ml.transforms.base.html#apache_beam.ml.transforms.base.ProcessHandler" title="apache_beam.ml.transforms.base.ProcessHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.ml.transforms.base.ProcessHandler</span></code></a></p> |
| <p>A handler class for processing data with TensorFlow Transform (TFT) |
| operations.</p> |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.append_transform"> |
| <code class="descname">append_transform</code><span class="sig-paren">(</span><em>transform</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.append_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.append_transform" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.get_raw_data_feature_spec"> |
| <code class="descname">get_raw_data_feature_spec</code><span class="sig-paren">(</span><em>input_types: Dict[str, type]</em><span class="sig-paren">)</span> → Dict[str, <sphinx.ext.autodoc.importer._MockObject object at 0x7f7372829d30>]<a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.get_raw_data_feature_spec"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.get_raw_data_feature_spec" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a DatasetMetadata object to be used with |
| tft_beam.AnalyzeAndTransformDataset. |
| :param input_types: A dictionary of column names and types.</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 DatasetMetadata object.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.convert_raw_data_feature_spec_to_dataset_metadata"> |
| <code class="descname">convert_raw_data_feature_spec_to_dataset_metadata</code><span class="sig-paren">(</span><em>raw_data_feature_spec</em><span class="sig-paren">)</span> → <sphinx.ext.autodoc.importer._MockObject object at 0x7f73728298b0><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.convert_raw_data_feature_spec_to_dataset_metadata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.convert_raw_data_feature_spec_to_dataset_metadata" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.get_raw_data_metadata"> |
| <code class="descname">get_raw_data_metadata</code><span class="sig-paren">(</span><em>input_types: Dict[str, type]</em><span class="sig-paren">)</span> → <sphinx.ext.autodoc.importer._MockObject object at 0x7f73728298b0><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.get_raw_data_metadata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.get_raw_data_metadata" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.write_transform_artifacts"> |
| <code class="descname">write_transform_artifacts</code><span class="sig-paren">(</span><em>transform_fn</em>, <em>location</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.write_transform_artifacts"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.write_transform_artifacts" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Write transform artifacts to the given location. |
| :param transform_fn: A transform_fn object. |
| :param location: A location to write the artifacts.</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 WriteTransformFn writing a TF transform graph.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.process_data_fn"> |
| <code class="descname">process_data_fn</code><span class="sig-paren">(</span><em>inputs: Dict[str, <sphinx.ext.autodoc.importer._MockObject object at 0x7f737281fbe0>]</em><span class="sig-paren">)</span> → Dict[str, <sphinx.ext.autodoc.importer._MockObject object at 0x7f737281fbe0>]<a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.process_data_fn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.process_data_fn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This method is used in the AnalyzeAndTransformDataset step. It applies |
| the transforms to the <cite>inputs</cite> in sequential order on the columns |
| provided for a given transform. |
| :param inputs: A dictionary of column names and data.</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 dictionary of column names and transformed data.</td> |
| </tr> |
| </tbody> |
| </table> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.expand"> |
| <code class="descname">expand</code><span class="sig-paren">(</span><em>raw_data: apache_beam.pvalue.PCollection[typing.Union[typing.NamedTuple, apache_beam.pvalue.Row, typing.Dict[str, typing.Union[str, float, int, bytes, numpy.ndarray]]]][Union[NamedTuple, apache_beam.pvalue.Row, Dict[str, Union[str, float, int, bytes, numpy.ndarray]]]]</em><span class="sig-paren">)</span> → apache_beam.pvalue.PCollection[typing.Union[apache_beam.pvalue.Row, typing.Dict[str, numpy.ndarray]]][Union[apache_beam.pvalue.Row, Dict[str, numpy.ndarray]]]<a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.expand"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.expand" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This method also computes the required dataset metadata for the tft |
| AnalyzeDataset/TransformDataset step.</p> |
| <p>This method uses tensorflow_transform’s Analyze step to produce the |
| artifacts and Transform step to apply the transforms on the data. |
| Artifacts are only produced if the artifact_mode is set to <cite>produce</cite>. |
| If artifact_mode is set to <cite>consume</cite>, then the artifacts are read from the |
| artifact_location, which was previously used to store the produced |
| artifacts.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="apache_beam.ml.transforms.handlers.TFTProcessHandler.with_exception_handling"> |
| <code class="descname">with_exception_handling</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/ml/transforms/handlers.html#TFTProcessHandler.with_exception_handling"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.ml.transforms.handlers.TFTProcessHandler.with_exception_handling" title="Permalink to this definition">¶</a></dt> |
| <dd></dd></dl> |
| |
| </dd></dl> |
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