TensorFlow Transform 1.16.0

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

TensorFlow Transform 1.15.0

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.

TensorFlow Transform 1.14.0

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

TensorFlow Transform 1.13.0

Major Features and Improvements

• RaggedTensors 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.

TensorFlow Transform 1.12.0

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

TensorFlow Transform 1.11.0

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 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

TensorFlow Transform 1.10.1

Major Features and Improvements

• N/A

Bug Fixes and Other Changes

• Depends on tfx-bsl>=1.10.1,<1.11.0.

Breaking Changes

• N/A

Deprecations

• N/A

TensorFlow Transform 1.10.0

Major Features and Improvements

• N/A

Bug Fixes and Other Changes

• Assign different close_to_resources resource hints to both original and
  cloned PTransforms in deep copy optimization. The reason of adding these
  resource hints is to prevent root Reads that are generated from deep copy
  being merged due to common subexpression elimination.
• Depends on apache-beam[gcp]>=2.40,<3.
• Depends on pyarrow>=6,<7.
• Depends on tensorflow-metadata>=1.10.0,<1.11.0.
• Depends on tfx-bsl>=1.10.0,<1.11.0.

Breaking Changes

• N/A

Deprecations

• N/A

TensorFlow Transform 1.9.0

Major Features and Improvements

• Adds element-wise scaling support to scale_by_min_max_per_key,
  scale_to_0_1_per_key and scale_to_z_score_per_key for
  key_vocabulary_filename = None.

Bug Fixes and Other Changes

• Depends on tensorflow>=1.15.5,<2 or tensorflow>=2.9,<2.10
• Depends on tensorflow-metadata>=1.9.0,<1.10.0.
• Depends on tfx-bsl>=1.9.0,<1.10.0.

Breaking Changes

• N/A

Deprecations

• N/A

TensorFlow Transform 1.8.0

Major Features and Improvements

• Adds tft.DatasetMetadata and its factory method from_feature_spec as
  public APIs to be used when using the "instance dict" data format.

Bug Fixes and Other Changes

• Depends on apache-beam[gcp]>=2.38,<3.
• Depends on
  tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<2.9.
• Depends on tensorflow-metadata>=1.8.0,<1.9.0.
• Depends on tfx-bsl>=1.8.0,<1.9.0.

Breaking Changes

• N/A

Deprecations

• N/A