Release 0.12.0

Major Features and Improvements

• Python 3.5 readiness complete (all tests pass). Full Python 3.5 compatibility
  is expected to be available with the next version of Transform (after
  Apache Beam 2.11 is released).
• Performance improvements for vocabulary generation when using top_k.
• New optimized highly experimental API for analyzing a dataset was added,
  AnalyzeDatasetWithCache, which allows reading and writing analyzer cache.
• Update DatasetMetadata to be a wrapper around the
  tensorflow_metadata.proto.v0.schema_pb2.Schema proto. TensorFlow Metadata
  will be the schema used to define data parsing across TFX. The serialized
  DatasetMetadata is now the Schema proto in ascii format, but the previous
  format can still be read.
• Change ApplySavedModel implementation to use tf.Session.make_callable
  instead of tf.Session.run for improved performance.

Bug Fixes and Other Changes

• tft.vocabulary and tft.compute_and_apply_vocabulary now support
  filtering based on adjusted mutual information when
  use_adjusetd_mutual_info is set to True.
• tft.vocabulary and tft.compute_and_apply_vocabulary now takes
  regularization term 'min_diff_from_avg' that adjusts mutual information to
  zero whenever the difference between count of the feature with any label and
  its expected count is lower than the threshold.
• Added an option to tft.vocabulary and tft.compute_and_apply_vocabulary
  to compute a coverage vocabulary, using the new coverage_top_k,
  coverage_frequency_threshold and key_fn parameters.
• Added tft.ptransform_analyzer for advanced use cases.
• Modified QuantilesCombiner to use tf.Session.make_callable instead of
  tf.Session.run for improved performance.
• ExampleProtoCoder now also supports non-serialized Example representations.
• tft.tfidf now accepts a scalar Tensor as vocab_size.
• assertItemsEqual in unit tests are replaced by assertCountEqual.
• NumPyCombiner now outputs TF dtypes in output_tensor_infos instead of
  numpy dtypes.
• Adds function tft.apply_pyfunc that provides limited support for
  tf.pyfunc. Note that this is incompatible with serving. See documentation
  for more details.
• CombinePerKey now adds a dimension for the key.
• Depends on numpy>=1.14.5,<2.
• Depends on apache-beam[gcp]>=2.10,<3.
• Depends on protobuf==3.7.0rc2.
• ExampleProtoCoder.encode now converts a feature whose value is None to an
  empty value, where before it did not accept None as a valid value.
• AnalyzeDataset, AnalyzeAndTransformDataset and TransformDataset can now
  accept dictionaries which contain None, and which will be interpreted the
  same as an empty list. They will never produce an output containing None.

Breaking changes

• ColumnSchema and related classes (Domain, Axis and
  ColumnRepresentation and their subclasses) have been removed. In order to
  create a schema, use from_feature_spec. In order to inspect a schema
  use the as_feature_spec and domains methods of Schema. The
  constructors of these classes are replaced by functions that still work when
  creating a Schema but this usage is deprecated.
• Requires pre-installed TensorFlow >=1.12,<2.
• ExampleProtoCoder.decode now converts a feature with empty value (e.g.
  features { feature { key: "varlen" value { } } }) or missing key for a
  feature (e.g. features { }) to a None in the output dictionary. Before
  it would represent these with an empty list. This better reflects the
  original example proto and is consistent with TensorFlow Data Validation.
• Coders now returns a list instead of an ndarray for a VarLenFeature.

Deprecations