Tensorflow-transform

Major Features and Improvements

* N/A

Bug Fixes and Other Changes

* Depends on `tensorflow 2.16`
* Relax dependency on Protobuf to include version 5.x

Breaking Changes

* N/A

Deprecations

* N/A

Major Features and Improvements

* Added support for sparse labels in AMI vocabulary computation.

Bug Fixes and Other Changes

* Bumped the Ubuntu version on which `tensorflow_transform` is tested to 20.04
(previously was 16.04).
* Explicitly use Keras 2 or `tf_keras`` if Keras 3 is installed.
* Added python 3.11 support.
* Depends on `tensorflow 2.15`.
* Enable passing `tf.saved_model.SaveOptions` to model saving functionality.
* Census and sentiment examples updated to only use Keras instead of
estimator.
* Depends on `apache-beam[gcp]>=2.53.0,<3` for Python 3.11 and on
`apache-beam[gcp]>=2.47.0,<3` for 3.9 and 3.10.
* Depends on `protobuf>=4.25.2,<5` for Python 3.11 and on `protobuf>3.20.3,<5`
for 3.9 and 3.10.

Breaking Changes

* Existing analyzer cache is automatically invalidated.

Deprecations

* Deprecated python 3.8 support.

Major Features and Improvements

* Adds a `reserved_tokens` parameter to vocabulary APIs, a list of tokens that
must appear in the vocabulary and maintain their order at the beginning of
the vocabulary.

Bug Fixes and Other Changes

* `approximate_vocabulary` now returns tokens with the same frequency in
reverse lexicographical order (similarly to `tft.vocabulary`).
* Transformed data batches are now sliced into smaller chunks if their size
exceeds 200MB.
* Depends on `pyarrow>=10,<11`.
* Depends on `apache-beam>=2.47,<3`.
* Depends on `numpy>=1.22.0`.
* Depends on `tensorflow>=2.13.0,<3`.

Breaking Changes

* Vocabulary related APIs now require passing non-positional parameters by
key.

Deprecations

* N/A

Major Features and Improvements

* `RaggedTensor`s can now be automatically inferred for variable length
features by setting `represent_variable_length_as_ragged=true` in TFMD
schema.
* New experimental APIs added for annotating sparse output tensors:
`tft.experimental.annotate_sparse_output_shape` and
`tft.experimental.annotate_true_sparse_output`.
* `DatasetKey.non_cacheable` added to allow for some datasets to not produce
cache. This may be useful for gradual cache generation when operating on a
large rolling range of datasets.
* Vocabularies produced by `compute_and_apply_vocabulary` can now store
frequencies. Controlled by the `store_frequency` parameter.

Bug Fixes and Other Changes

* Depends on `numpy~=1.22.0`.
* Depends on `tensorflow>=2.12.0,<2.13`.
* Depends on `protobuf>=3.20.3,<5`.
* Depends on `tensorflow-metadata>=1.13.1,<1.14.0`.
* Depends on `tfx-bsl>=1.13.0,<1.14.0`.
* Modifies `get_vocabulary_size_by_name` to return a minimum of 1.

Breaking Changes

* N/A

Deprecations

* Deprecated python 3.7 support.

Major Features and Improvements

* N/A

Bug Fixes and Other Changes

* Depends on `tensorflow>=2.11,<2.12`
* Depends on `tensorflow-metadata>=1.12.0,<1.13.0`.
* Depends on `tfx-bsl>=1.12.0,<1.13.0`.

Breaking Changes

* N/A

Deprecations

* N/A

Major Features and Improvements

* This is the last version that supports TensorFlow 1.15.x. TF 1.15.x support
will be removed in the next version. Please check the
[TF2 migration guide](https://www.tensorflow.org/guide/migrate) to migrate
to TF2.

* Introduced `tft.experimental.document_frequency` and `tft.experimental.idf`
which map each term to its document frequency and inverse document frequency
in the same order as the terms in documents.
* `schema_utils.schema_as_feature_spec` now supports struct features as a way
to describe `tf.SequenceExample` data.
* TensorRepresentations in schema used for
`schema_utils.schema_as_feature_spec` can now share name with their source
features.
* Introduced `tft_beam.EncodeTransformedDataset` which can be used to easily
encode transformed data in preparation for materialization.

Bug Fixes and Other Changes

* Depends on `tensorflow>=1.15.5,<2` or `tensorflow>=2.10,<2.11`
* Depends on `apache-beam[gcp]>=2.41,<3`.

Breaking Changes

* N/A

Deprecations

* N/A