RELEASE.md

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.

Release 0.8.0

Major Features and Improvements

• Add TFTransformOutput utility class that wraps the output of tf.Transform for use in training. This makes it easier to consume the output written by tf.Transform (see update examples for usage).
• Increase efficiency of quantiles (and therefore bucketize).

Bug Fixes and Other Changes

• Change tft.sum/tft.mean/tft.var to only support basic numeric types.
• Widen the output type of tft.sum for some input types to avoid overflow and/or to preserve precision.
• For int32 and int64 input types, change the output type of tft.mean/ tft.var/tft.scale_to_z_score from float64 to float32 .
• Change the output type of tft.size to be always int64.
• Context now accepts passthrough_keys which can be used when additional information should be attached to dataset instances in the pipeline which should not be part of the transformation graph, for example: instance keys.
• In addition to using TFTransformOutput, the examples demonstrate new workflows where a vocabulary is computed, but not applied, in the preprocessing_fn.
• Added dependency on the absl-py package.
• TransformTestCase test cases can now be parameterized.
• Add support for partitioned variables when loading a model.
• Export the coders subpackage so that users can access it as tft.coders, e.g. tft.coders.ExampleProtoCoder.
• Setting dtypes for numpy arrays in tft.coders.ExampleProtoCoder and tft.coders.CsvCoder.
• tft.mean, tft.max and tft.var now support tf.SparseTensor.
• Update examples to use "core" TensorFlow estimator API (tf.estimator).
• Depends on protobuf>=3.6.0<4.

Breaking changes

• apply_saved_transform is removed. See note on partially_apply_saved_transform in the Deprecations section.
• No longer set vocabulary_file in IntDomain when using tft.compute_and_apply_vocabulary or tft.apply_vocabulary.
• Requires pre-installed TensorFlow >=1.8,<2.

Deprecations

• The expected_asset_file_contents of TransformTestCase.assertAnalyzeAndTransformResults has been deprecated, use expected_vocab_file_contents instead.
• transform_fn_io.TRANSFORMED_METADATA_DIR and transform_fn_io.TRANSFORM_FN_DIR should not be used, they are now aliases for TFTransformOutput.TRANSFORMED_METADATA_DIR and TFTransformOutput.TRANSFORM_FN_DIR respectively.
• partially_apply_saved_transform is deprecated, users should use the transform_raw_features method of TFTransformOuptut instead. These differ in that partially_apply_saved_transform can also be used to return both the input placeholders and the outputs. But users do not need this functionality because they will typically create the input placeholders themselves based on the feature spec.
• Renamed tft.uniques to tft.vocabulary, tft.string_to_int to tft.compute_and_apply_vocabulary and tft.apply_vocab to tft.apply_vocabulary. The existing methods will remain for a few more minor releases but are now deprecated and should get migrated away from.

Release 0.6.0

Major Features and Improvements

Bug Fixes and Other Changes

• Depends on apache-beam[gcp]>=2.4,<3.
• Trim min/max value in tft.bucketize where the computed number of bucket boundaries is more than requested. Updated documentation to clearly indicate that the number of buckets is computed using approximate algorithms, and that computed number can be more or less than requested.
• Change the namespace used for Beam metrics from tensorflow_transform to tfx.Transform.
• Update Beam metrics to also log vocabulary sizes.
• CsvCoder updated to support unicode.
• Update examples to not use the coder argument for IO, and instead use a separate beam.Map to encode/decode data.

Breaking changes

• Requires pre-installed TensorFlow >=1.6,<2.

Deprecations

Release 0.5.0

Major Features and Improvements

• Batching of input instances is now done automatically and dynamically.
• Added analyzers to compute covariance matrices (tft.covariance) and principal components for PCA (tft.pca).
• CombinerSpec and combine_analyzer now accept multiple inputs/outputs.

Bug Fixes and Other Changes

• Depends on apache-beam[gcp]>=2.3,<3.
• Fixes a bug where TransformDataset would not return correct output if the output DatasetMetadata contained deferred values (such as vocabularies).
• Added checks that the prepreprocessing function's outputs all have the same size in the batch dimension.
• Added tft.apply_buckets which takes an input tensor and a list of bucket boundaries, and returns bucketized data.
• tft.bucketize and tft.apply_buckets now set metadata for the output tensor, which means the resulting tf.Metadata for the output of these functions will contain min and max values based on the number of buckets, and also be set to categorical.
• Testing helper function assertAnalyzeAndTransformResults can now also test the content of vocabulary files and other assets.
• Reduces the number of beam stages needed for certain analyzers, which can be a performance bottleneck when transforming many features.
• Performance improvements in tft.uniques.
• Fix a bug in tft.bucketize where the bucket boundary could be same as a min/max value, and was getting dropped.
• Allows scaling individual components of a tensor independently with tft.scale_by_min_max, tft.scale_to_0_1, and tft.scale_to_z_score.
• Fix a bug where apply_saved_transform could only be applied in the global name scope.
• Add warning when frequency_threshold that are <= 1. This is a no-op and generally reflects mistaking frequency_threshold for a relative frequency where in fact it is an absolute frequency.

Breaking changes

• The interfaces of CombinerSpec and combine_analyzer have changed to allow for multiple inputs/outputs.
• Requires pre-installed TensorFlow >=1.5,<2.

Deprecations

Release 0.4.0

Major Features and Improvements

• Added a combine_analyzer() that supports user provided combiner, conforming to beam.CombinFn(). This allows users to implement custom combiners (e.g. median), to complement analyzers (like min, max) that are prepackaged in TFT.
• Quantiles Analyzer (tft.quantiles), with a corresponding tft.bucketize mapper.

Bug Fixes and Other Changes

• Depends on apache-beam[gcp]>=2.2,<3.
• Fixes some KeyError issues that appeared in certain circumstances when one would call AnalyzeAndTransformDataset (due to a now-fixed Apache Beam [bug] (https://issues.apache.org/jira/projects/BEAM/issues/BEAM-2966)).
• Allow all functions that accept and return tensors, to accept an optional name scope, in line with TensorFlow coding conventions.
• Update examples to construct input functions by hand instead of using helper functions.
• Change scale_by_min_max/scale_to_0_1 to return the average(min, max) of the range in case all values are identical.
• Added export of serving model to examples.
• Use "core" version of feature columns (tf.feature_column instead of tf.contrib) in examples.
• A few bug fixes and improvements for coders regarding Python 3.

Breaking changes

• Requires pre-installed TensorFlow >= 1.4.
• No longer distributing a WHL file in PyPI. Only doing a source distribution which should however be compatible with all platforms (ie you are still able to pip install tensorflow-transform and use requirements.txt or setup.py files for environment setup).
• Some functions now introduce a new name scope when they did not before so the names of tensors may change. This will only affect you if you directly lookup tensors by name in the graph produced by tf.Transform.
• Various Analyzer Specs (_NumericCombineSpec, _UniquesSpec, _QuantilesSpec) are now private. Analyzers are accessible only via the top-level TFT functions (min, max, sum, size, mean, var, uniques, quantiles).

Deprecations

• The serving_input_fns on tensorflow_transform/saved/input_fn_maker.py will be removed on a future version and should not be used on new code, see the examples directory for details on how to migrate your code to define their own serving functions.

Release 0.3.1

Major Features and Improvements

• We now provide helper methods for creating serving_input_receiver_fn for use with tf.estimator. These mirror the existing functions targeting the legacy tf.contrib.learn.estimators-- i.e. for each *_serving_input_fn() in input_fn_maker there is now also a *_serving_input_receiver_fn().

Bug Fixes and Other Changes

• Introduced tft.apply_vocab this allows users to separately apply a single vocabulary (as generated by tft.uniques) to several different columns.
• Provide a source distribution tar tensorflow-transform-X.Y.Z.tar.gz.

Breaking Changes

• The default prefix for tft.string_to_int vocab_filename changed from vocab_string_to_int to vocab_string_to_int_uniques. To make your pipelines resilient to implementation details please set vocab_filename if you are using the generated vocab_filename on a downstream component.

Release 0.3.0

Major Features and Improvements

• Added hash_strings mapper.
• Write vocabularies as asset files instead of constants in the SavedModel.

Bug Fixes and Other Changes

• 'tft.tfidf' now adds 1 to idf values so that terms in every document in the corpus have a non-zero tfidf value.
• Performance and memory usage improvement when running with Beam runners that use multi-threaded workers.
• Performance optimizations in ExampleProtoCoder.
• Depends on apache-beam[gcp]>=2.1.1,<3.
• Depends on protobuf>=3.3<4.
• Depends on six>=1.9,<1.11.

Breaking Changes

• Requires pre-installed TensorFlow >= 1.3.
• Removed tft.map use tft.apply_function instead (as needed).
• Removed tft.tfidf_weights use tft.tfidf instead.
• beam_metadata_io.WriteMetadata now requires a second pipeline argument (see examples).
• A Beam bug will now affect users who call AnalyzeAndTransformDataset in certain circumstances. Roughly speaking, if you call beam.Pipeline() at some point (as all our examples do) you will not experience this bug. The bug is characterized by an error similar to KeyError: (u'AnalyzeAndTransformDataset/AnalyzeDataset/ComputeTensorValues/Extract[Maximum:0]', None) This bug will be fixed in Beam 2.2.

Release 0.1.10

Major Features and Improvements

• Add json-example serving input functions to TF.Transform.
• Add variance analyzer to tf.transform.

Bug Fixes and Other Changes

• Remove duplication in output of tft.tfidf.
• Ensure ngrams output dense_shape is greater than or equal to 0.
• Alters the behavior and interface of tensorflow_transform.mappers.ngrams.
• Depends on apache-beam[gcp]=>2,<3.
• Making TF Parallelism runner-dependent.
• Fixes issue with csv serving input function.
• Various performance and stability improvements.

Deprecations

• tft.map will be removed on version 0.2.0, see the examples directory for instructions on how to use tft.apply_function instead (as needed).
• tft.tfidf_weights will be removed on version 0.2.0, use tft.tfidf instead.

Release 0.1.9

Major Features and Improvements

• Refactor internals to remove Column and Statistic classes

Bug Fixes and Other Changes

• Remove collections from graph to avoid warnings
• Return float32 from tfidf_weights
• Update tensorflow_transform to use tf.saved_model APIs.
• Add default values on example proto coder.
• Various performance and stability improvements.