|---| | [ View source on GitHub](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L58-L469) | A wrapper around the output of the tf.Transform. tft.TFTransformOutput( transform_output_dir: str ) ### Used in the notebooks | Used in the tutorials | |---| | - [Preprocessing data with TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/census) - [Preprocess data with TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/simple) - [TFX Estimator Component Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/components) - [TFX Keras Component Tutorial](https://www.tensorflow.org/tfx/tutorials/tfx/components_keras) - [Graph-based Neural Structured Learning in TFX](https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning) |


| ## Args || |---|---|



| ## Attributes || |---|---| | `post_transform_statistics_path` | Returns the path to the post-transform datum statistics.
> [!NOTE] > **Note:** post_transform_statistics is not guaranteed to exist in the output of tf.transform and hence using this could fail, if post_transform statistics is not present in TFTransformOutput. | | `pre_transform_statistics_path` | Returns the path to the pre-transform datum statistics. > [!NOTE] > **Note:** pre_transform_statistics is not guaranteed to exist in the output of tf.transform and hence using this could fail, if pre_transform statistics is not present in TFTransformOutput. | | `raw_metadata` | A DatasetMetadata. > [!NOTE] > **Note:** raw_metadata is not guaranteed to exist in the output of tf.transform and hence using this could fail, if raw_metadata is not present in TFTransformOutput. ## Returns A DatasetMetadata | | `transform_savedmodel_dir` | A python str. | | `transformed_metadata` | A DatasetMetadata. |
## Methods ### `load_transform_graph` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L378-L397) load_transform_graph() Load the transform graph without replacing any placeholders. This is necessary to ensure that variables in the transform graph are included in the training checkpoint when using tf.Estimator. This should be called in the training input_fn. ### `num_buckets_for_transformed_feature` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L265-L281) num_buckets_for_transformed_feature( name: str ) -> int Returns the number of buckets for an integerized transformed feature. ### `raw_domains` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L437-L445) raw_domains() -> Dict[str, common_types.DomainType] Returns domains for the raw features. ## Returns A dict from feature names to one of schema_pb2.IntDomain, schema_pb2.StringDomain or schema_pb2.FloatDomain. ### `raw_feature_spec` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L426-L435) raw_feature_spec() -> Dict[str, common_types.FeatureSpecType] Returns a feature_spec for the raw features. ## Returns A dict from feature names to FixedLenFeature/SparseFeature/VarLenFeature. ### `transform_features_layer` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L283-L297) transform_features_layer() -> tf_keras.Model Creates a `TransformFeaturesLayer` from this transform output. If a `TransformFeaturesLayer` has already been created for self, the same one will be returned. ## Returns A `TransformFeaturesLayer` instance. ### `transform_raw_features` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L299-L350) transform_raw_features( raw_features: Mapping[str, common_types.TensorType], drop_unused_features: bool = True ) -> Dict[str, common_types.TensorType] Takes a dict of tensors representing raw features and transforms them. Takes a dictionary of `Tensor`, `SparseTensor`, or `RaggedTensor`s that represent the raw features, and applies the transformation defined by tf.Transform. If False it returns all transformed features defined by tf.Transform. To only return features transformed from the given 'raw_features', set `drop_unused_features` to True. > [!NOTE] > **Note:** If eager execution is enabled and this API is invoked inside a tf.function or an API that uses tf.function such as dataset.map, please use `transform_features_layer` instead. It separates out loading of the transform graph and hence resources will not be initialized on each invocation. This can have significant performance improvement if the transform graph was exported as a TF1 SavedModel and guarantees correctness if it was exported as a TF2 SavedModel.


| Args || |---|---|
*** ** * ** *** raw_features: A dict whose keys are feature names and values are `Tensor`s, `SparseTensor`s, or `RaggedTensor`s. drop_unused_features: If True, the result will be filtered. Only the features that are transformed from 'raw_features' will be included in the returned result. If a feature is transformed from multiple raw features (e.g, feature cross), it will only be included if all its base raw features are present in `raw_features`.


| Returns || |---|---|
*** ** * ** *** A dict whose keys are feature names and values are `Tensor`s, `SparseTensor`s, or `RaggedTensor`s representing transformed features. ### `transformed_domains` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L125-L137) transformed_domains() -> Dict[str, common_types.DomainType] Returns domains for the transformed features. ## Returns A dict from feature names to one of schema_pb2.IntDomain, schema_pb2.StringDomain or schema_pb2.FloatDomain. ### `transformed_feature_spec` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L114-L123) transformed_feature_spec() -> Dict[str, common_types.FeatureSpecType] Returns a feature_spec for the transformed features. ## Returns A dict from feature names to FixedLenFeature/SparseFeature/VarLenFeature. ### `vocabulary_by_name` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L245-L262) vocabulary_by_name( vocab_filename: str ) -> List[bytes] Like vocabulary_file_by_name but returns a list. ### `vocabulary_file_by_name` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L139-L188) vocabulary_file_by_name( vocab_filename: str ) -> Optional[str] Returns the vocabulary file path created in the preprocessing function. `vocab_filename` must either be (i) the name used as the vocab_filename argument to tft.compute_and_apply_vocabulary / tft.vocabulary or (ii) the key used in tft.annotate_asset. When a mapping has been specified by calls to tft.annotate_asset, it will be checked first for the provided filename. If present, this filename will be used directly to construct a path. If the mapping does not exist or `vocab_filename` is not present within it, we will default to sanitizing `vocab_filename` and searching for files matching it within the assets directory. In either case, if the constructed path does not point to an existing file within the assets subdirectory, we will return a None.


| Args || |---|---|
*** ** * ** *** vocab_filename: The vocabulary name to lookup. ### `vocabulary_size_by_name` [View source](https://github.com/tensorflow/transform/blob/v1.21.0/tensorflow_transform/output_wrapper.py#L217-L243) vocabulary_size_by_name( vocab_filename: str ) -> int Like vocabulary_file_by_name, but returns the size of vocabulary.


| ## Class Variables || |---|---| | ASSET_MAP | `'asset_map'` | | POST_TRANSFORM_FEATURE_STATS_PATH | `'post_transform_feature_stats/FeatureStats.pb'` | | PRE_TRANSFORM_FEATURE_STATS_PATH | `'pre_transform_feature_stats/FeatureStats.pb'` | | RAW_METADATA_DIR | `'metadata'` | | TRANSFORMED_METADATA_DIR | `'transformed_metadata'` | | TRANSFORM_FN_DIR | `'transform_fn'` |