Version 0.23.0

Major Features and Improvements

• Added tft.scale_to_gaussian to transform input to standard gaussian.
• Vocabulary related analyzers and mappers now accept a file_format argument
  allowing the vocabulary to be saved in TFRecord format. The default format
  remains text (TFRecord format requires tensorflow>=2.4).

Bug Fixes and Other Changes

• Enable SavedModelLoader to import and apply TF2 SavedModels.
• tft.min, tft.max, tft.sum, tft.covariance and tft.pca now have
  default output values to properly process empty analysis datasets.
• tft.scale_by_min_max, tft.scale_to_0_1 and the corresponding per-key
  versions now apply a sigmoid function to scale tensors if the analysis
  dataset is either empty or contains a single distinct value.
• Added best-effort tf.text op registration when loading transformation
  graphs.
• Vocabularies computed over numerical features will now assign values to
  entries with equal frequency in reverse lexicographical order as well,
  similarly to string features.
• Fixed an issue that causes the TABLE_INITIALIZERS graph collection to
  contain a tensor instead of an op when a TF2 SavedModel or a TF2 Hub Module
  containing a table is loaded inside the preprocessing_fn.
• Fixes an issue where the output tensors of tft.TransformFeaturesLayer
  would all have unknown shapes.
• Stopped depending on avro-python3.
• Depends on apache-beam[gcp]>=2.23,<3.
• Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,<2.4.
• Depends on tensorflow-metadata>=0.23,<0.24.
• Depends on tfx-bsl>=0.23,<0.24.

Breaking changes

• Existing caches (for all analyzers) are automatically invalidated.

Deprecations

• Deprecating Py2 support.
• Note: We plan to remove Python 3.5 support after this release.

Version 0.22.0

Major Features and Improvements

Bug Fixes and Other Changes

• tft.bucketize_per_key no longer assumes that the keys during
  transformation existed in the analysis dataset. If a key is missing then the
  assigned bucket will be -1.
• tft.estimated_probability_density, when categorical=True, no longer
  assumes that the values during transformation existed in the analysis dataset,
  and will assume 0 density in that case.
• Switched analyzer cache representation of dataset keys from using a primitive
  str to a DatasetKey class.
• tft_beam.analyzer_cache.ReadAnalysisCacheFromFS can now filter cache entry
  keys when given a cache_entry_keys parameter. cache_entry_keys can be
  produced by utilizing get_analysis_cache_entry_keys.
• Reduced number of shuffles via packing multiple combine merges into a
  single Beam combiner.
• Switch tft.TransformFeaturesLayer to use the TF 2 tf.saved_model.load API
  to load a previously exported SavedModel.
• Adds tft.sparse_tensor_left_align as a utility which aligns
  tf.SparseTensors to the left.
• Depends on avro-python3>=1.8.1,!=1.9.2.*,<2.0.0 for Python3.5 + MacOS.
• Depends on apache-beam[gcp]>=2.20.0,<3.
• Depends on tensorflow>=1.15,!=2.0.*,<2.3.
• Depends on tensorflow-metadata>=0.22.0,<0.23.0.
• Depends on tfx-bsl>=0.22.0,<0.23.0.

Breaking changes

• tft.AnalyzeDatasetWithCache no longer accepts a flat pcollection as an
  input. Instead it will flatten the datasets in the input_values_pcoll_dict
  input if needed.
• tft.TransformFeaturesLayer no longer takes a parameter
  drop_unused_features. Its default behavior is now equivalent to having set
  drop_unused_features to True.

Deprecations

Release 0.21.2

Release 0.21.2

Major Features and Improvements

• Expanded capability for per-key analyzers to analyze larger sets of keys that
  would not fit in memory, by storing the key-value pairs in vocabulary files.
  This is enabled by passing a per_key_filename to tft.count_per_key and
  tft.scale_to_z_score_per_key.
• Added tft.TransformFeaturesLayer and
  tft.TFTransformOutput.transform_features_layers to allow transforming
  features for a TensorFlow Keras model.

Bug Fixes and Other Changes

• tft.apply_buckets_with_interpolation now handles NaN values by imputing with
  the middle of the normalized range.
• Depends on tfx-bsl>=0.21.3,<0.22.

Breaking changes

Deprecations

Release 0.21.0

Release 0.21.0

Major Features and Improvements

• Added a new version of the census example to demonstrate usage in TF 2.0.
• New mapper estimated_probability_density to compute either exact
  probabilities (for discrete categorical variable) or approximate density over
  fixed intervals (continuous variables).
• New analyzers count_per_key and histogram to return counts of unique
  elements or values within predefined ranges. Calling tft.histogram on
  non-categorical value will assign each data point to the appropriate fixed
  bucket and then count for each bucket.
• Provided capability for per-key analyzers to analyze larger sets of keys that
  would not fit in memory, by storing the key-value pairs in vocabulary files.
  This is enabled by passing a per_key_filename to
  tft.scale_by_min_max_per_key and tft.scale_to_0_1_per_key.

Bug Fixes and Other Changes

• Added beam counters to log analyzer and mapper usage.
• Cleanup deprecated APIs used in census and sentiment examples.
• Support windows style paths in analyzer_cache.
• tft_beam.WriteTransformFn and tft_beam.WriteMetadata have been made
  idempotent to allow retrying them in case of a failure.
• tft_beam.WriteMetadata takes an optional argument write_to_unique_subdir
  and returns the path to which metadata was written. If
  write_to_unique_subdir is True, metadata is written to a unique subdirectory
  under path, otherwise it is written to path.
• Support non utf-8 characters when reading vocabularies in
  tft.TFTransformOutput
• tft.TFTransformOutput.vocabulary_by_name now returns bytes instead of str
  with python 3.

Breaking changes

Deprecations

Release 0.15.0

Release 0.15.0

Major Features and Improvements

• This release introduces initial beta support for TF 2.0. TF 2.0 programs
  running in "safety" mode (i.e. using TF 1.X APIs through the
  tensorflow.compat.v1 compatibility module are expected to work. Newly
  written TF 2.0 programs may not work if they exercise functionality that is
  not yet supported. If you do encounter an issue when using
  tensorflow-transform with TF 2.0, please create an issue
  https://github.com/tensorflow/transform/issues with instructions on how to
  reproduce it.
• Performance improvements for preprocessing_fns with many Quantiles
  analyzers.
• tft.quantiles and tft.bucketize are now using new TF core quantiles ops
  instead of contrib ops.
• Performance improvements due to packing multiple combine analyzers into a
  single Beam Combiner.

Bug Fixes and Other Changes

• Existing analyzer cache is invalidated.
• Saved transforms now support composite tensors (such as tf.RaggedTensor).
• Vocabulary's cache coder now supports non utf-8 encodable tokens.
• Fixes encoding of the tft.covariance accumulator cache.
• Fixes encoding per-key analyzers accumulator cache.
• Make various utility methods in tft.inspect_preprocessing_fn support
  tf.RaggedTensor.
• Moved beam/shared lib to tfx-bsl. If running with latest master, tfx-bsl
  must also be latest master.
• preprocessing_fns now have beta support of calls to tf.functions, as long
  as they don't contain calls to tf.Transform analyzers/mappers or table
  initializers.
• tft.quantiles and tft.bucketize are now using core TF ops.
• Depends on tfx-bsl>=0.15,<0.16.
• Depends on tensorflow-metadata>=0.15,<0.16.
• Depends on apache-beam[gcp]>=2.16,<3.
• Depends on tensorflow>=0.15,<2.2.
  • Starting from 1.15, package
    tensorflow comes with GPU support. Users won't need to choose between
    tensorflow and tensorflow-gpu.
  • Caveat: tensorflow 2.0.0 is an exception and does not have GPU
    support. If tensorflow-gpu 2.0.0 is installed before installing
    tensorflow-transform, it will be replaced with tensorflow 2.0.0.
    Re-install tensorflow-gpu 2.0.0 if needed.

Breaking changes

• always_return_num_quantiles changed to default to True in tft.quantiles
  and tft.bucketize, resulting in exact bucket count returned.
• Removes the input_fn_maker module which has been deprecated since TFT 0.11.
  For idiomatic construction of input_fn, see tensorflow_transform examples.

Deprecations

Release 0.14.0

Major Features and Improvements

• New tft.word_count mapper to identify the number of tokens for each row
  (for pre-tokenized strings).
• All tft.scale_to_* mappers now have per-key variants, along with analyzers
  for mean_and_var_per_key and min_and_max_per_key.
• New tft_beam.AnalyzeDatasetWithCache allows analyzing ranges of data while
  producing and utilizing cache. tft.analyzer_cache can help read and write
  such cache to a filesystem between runs. This caching feature is worth using
  when analyzing a rolling range in a continuous pipeline manner. This is an
  experimental feature.
• Added reduce_instance_dims support to tft.quantiles and elementwise to
  tft.bucketize, while avoiding separate beam calls for each feature.

Bug Fixes and Other Changes

• sparse_tensor_to_dense_with_shape now accepts an optional default_value
  parameter.
• tft.vocabulary and tft.compute_and_apply_vocabulary now support
  fingerprint_shuffle to sort the vocabularies by fingerprint instead of
  counts. This is useful for load balancing the training parameter servers.
  This is an experimental feature.
• Fix numerical instability in tft.vocabulary mutual information calculations.
• tft.vocabulary and tft.compute_and_apply_vocabulary now support computing
  vocabularies over integer categoricals and multivalent input features, and
  computing mutual information for non-binary labels.
• New numeric normalization method available:
  tft.apply_buckets_with_interpolation.
• Changes to make this library more compatible with TensorFlow 2.0.
• Fix sanitizing of vocabulary filenames.
• Emit a friendly error message when context isn't set.
• Analyzer output dtypes are enforced to be TensorFlow dtypes, and by extension
  ptransform_analyzer's output_dtypes is enforced to be a list of TensorFlow
  dtypes.
• Make tft.apply_buckets_with_interpolation support SparseTensors.
• Adds an experimental api for analyzers to annotate the post-transform schema.
• TFTransformOutput.transform_raw_features now accepts an optional
  drop_unused_features parameter to exclude unused features in output.
• If not specified, the min_diff_from_avg parameter of tft.vocabulary now
  defaults to a reasonable value based on the size of the dataset (relevant
  only if computing vocabularies using mutual information).
• Convert some tf.contrib functions to be compatible with TF2.0.
• New tft.bag_of_words mapper to compute the unique set of ngrams for each row
  (for pre-tokenized strings).
• Fixed a bug in tf_utils.reduce_batch_count_mean_and_var, and as a result
  mean_and_var analyzer, was miscalculating variance for the sparse
  elementwise=True case.
• At test utility tft_unit.cross_named_parameters for creating parameterized
  tests that involve the cartesian product of various parameters.
• Depends on tensorflow-metadata>=0.14,<0.15.
• Depends on apache-beam[gcp]>=2.14,<3.
• Depends on numpy>=1.16,<2.
• Depends on absl-py>=0.7,<2.
• Allow preprocessing_fn to emit a tf.RaggedTensor. In this case, the
  output Schema proto will not be able to be converted to a feature spec,
  and so the output data will not be able to be materialized with tft.coders.
• Ability to directly set exact num_buckets with new parameter
  always_return_num_quantiles for analyzers.quantiles and
  mappers.bucketize, defaulting to False in general but True when
  reduce_instance_dims is False.

Breaking changes

• tf_utils.reduce_batch_count_mean_and_var, which feeds into
  tft.mean_and_var, now returns 0 instead of inf for empty columns of a
  sparse tensor.
• tensorflow_transform.tf_metadata.dataset_schema.Schema class is removed.
  Wherever a dataset_schema.Schema was used, users should now provide a
  tensorflow_metadata.proto.v0.schema_pb2.Schema proto. For backwards
  compatibility, dataset_schema.Schema is now a factory method that produces
  a Schema proto. Updating code should be straightforward because the
  dataset_schema.Schema class was already a wrapper around the Schema proto.
• Only explicitly public analyzers are exported to the tft module, e.g.
  combiners are no longer exported and have to be accessed directly through
  tft.analyzers.
• Requires pre-installed TensorFlow >=1.14,<2.

Deprecations

• DatasetSchema is now a deprecated factory method (see above).
• tft.tf_metadata.dataset_schema.from_feature_spec is now deprecated.
  Equivalent functionality is provided by
  tft.tf_metadata.schema_utils.schema_from_feature_spec.

Release 0.13.0

Major Features and Improvements

• Now AnalyzeDataset, TransformDataset and AnalyzeAndTransformDataset can
  accept input data that only contains columns needed for that operation as
  opposed to all columns defined in schema. Utility methods to infer the list of
  needed columns are added to tft.inspect_preprocessing_fn. This makes it
  easier to take advantage of columnar projection when data is stored in
  columnar storage formats.
• Python 3.5 is supported.

Bug Fixes and Other Changes

• Version is now accessible as tensorflow_transform.__version__.
• Depends on apache-beam[gcp]>=2.11,<3.
• Depends on protobuf>=3.7,<4.

Breaking changes

• Coders now return index and value features rather than a combined feature for
  SparseFeature.
• Requires pre-installed TensorFlow >=1.13,<2.

Deprecations

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

Release 0.11.0

Major Features and Improvements

Bug Fixes and Other Changes

• 'tft.vocabulary' and 'tft.compute_and_apply_vocabulary' now support filtering
  based on mutual information when labels is provided.
• Export all package level exports of tensorflow_transform, from the
  tensorflow_transform.beam subpackage. This allows users to just import the
  tensorflow_transform.beam subpackage for all functionality.
• Adding API docs.
• Fix bug where Transform returned a different dtype for a VarLenFeature with
  0 elements.
• Depends on apache-beam[gcp]>=2.8,<3.

Breaking changes

• Requires pre-installed TensorFlow >=1.11,<2.

Deprecations

• All functions in tensorflow_transform.saved.input_fn_maker are deprecated.
  See the examples for how to construct the input_fn for training and serving.
  Note that the examples demonstrate the use of the tf.estimator API. The
  functions named *_serving_input_fn were for use with the
  tf.contrib.estimator API which is now deprecated. We do not provide
  examples of usage of the tf.contrib.estimator API, instead users should
  upgrade to the tf.estimator API.

Release 0.9.0

Major Features and Improvements

• Performance improvements for vocabulary generation when using top_k.
• Utility to deep-copy Beam PCollections was added to avoid unnecessary
  materialization.
• Utilize deep_copy to avoid unnecessary materialization of pcollections when
  the input data is immutable. This feature is currently off by default and can
  be enabled by setting tft.Context.use_deep_copy_optimization=True.
• Add bucketize_per_key which computes separate quantiles for each key and then
  bucketizes each value according to the quantiles computed for its key.
• tft.scale_to_z_score is now implemented with a single pass over the data.
• Export schema_utils package to convert from the tensorflow-metadata package
  to the (soon to be deprecated) tf_metadata subpackage of
  tensorflow-transform.

Bug Fixes and Other Changes

• Memory reduction during vocabulary generation.
• Clarify documentation on return values from tft.compute_and_apply_vocabulary
  and tft.string_to_int.
• tft.unit now explicitly creates Beam PCollections and validates the
  transformed dataset by writing and then reading it from disk.
• tft.min, tft.size, tft.sum, tft.scale_to_z_score and tft.bucketize
  now support tf.SparseTensor.
• Fix to tft.scale_to_z_score so it no longer attempts to divide by 0 when the
  variance is 0.
• Fix bug where internal graph analysis didn't handle the case where an
  operation has control inputs that are operations (as opposed to tensors).
• tft.sparse_tensor_to_dense_with_shape added which allows densifying a
  SparseTensor while specifying the resulting Tensor's shape.
• Add load_transform_graph method to TFTransformOutput to load the transform
  graph without applying it. This has the effect of adding variables to the
  checkpoint when calling it from the training input_fn when using
  tf.Estimator.
• 'tft.vocabulary' and 'tft.compute_and_apply_vocabulary' now accept an
  optional weights argument. When weights is provided, weighted frequencies
  are used instead of frequencies based on counts.
• 'tft.quantiles' and 'tft.bucketize' now accept an optoinal weights argument.
  When weights is provided, weighted count is used for quantiles instead of
  the counts themselves.
• Updated examples to construct the schema using
  dataset_schema.from_feature_spec.
• Updated the census example to allow the 'education-num' feature to be missing
  and fill in a default value when it is.
• Depends on tensorflow-metadata>=0.9,<1.
• Depends on apache-beam[gcp]>=2.6,<3.

Breaking changes

• We now validate a Schema in its constructor to make sure that it can be
  converted to a feature spec. In particular only tf.int64, tf.string and
  tf.float32 types are allowed.
• We now disallow default values for FixedColumnRepresentation.
• It is no longer possible to set a default value in the Schema, and validation
  of shape parameters will occur earlier.
• Removed Schema.as_batched_placeholders() method.
• Removed all components of DatasetMetadata except the schema, and removed all
  related classes and code.
• Removed the merge method for DatasetMetadata and related classes.
• read_metadata can now only read from a single metadata directory and
  read_metadata and write_metadata no longer accept the versions parameter.
  They now only read/write the JSON format.
• Requires pre-installed TensorFlow >=1.9,<2.

Deprecations

• apply_function is no longer needed and is deprecated.
  apply_function(fn, *args) is now equivalent to fn(*args). tf.Transform
  is able to handle while loops and tables without the user wrapping the
  function call in apply_function.