Current Version (Still in Development)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecation

Version 1.17.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on protobuf>=4.25.2,<6.0.0 for Python 3.11 and on protobuf>=4.21.6,<6.0.0 for 3.9 and 3.10.
Updated Bazel build rules
Bumped the minimum bazel version required to build tfdv to 6.5.0.
Depends on tensorflow>=2.17,<2.18.
Updating ZetaSQL to v2024.11.1 and related dependencies.
Update GCC version to gcc-10.
macOS wheel publishing is temporarily paused due to missing ARM64 support.

Known Issues

N/A

Breaking Changes

N/A

Deprecation

N/A

Version 1.16.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Relax dependency on Protobuf to include version 6.5

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.16.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on tensorflow>=2.16,<2.17.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.15.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Fix bug in implementation of custom validations.
Depends on tensorflow>=2.15,<2.16.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.15.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

When computing cross feature statistics, skip configured crosses that include features of unsupported types (i.e., are not univalent numeric features).
Update the minimum Bazel version required to build TFDV to 6.1.0.
Modifies get_statistics_html() utility function to return a value indicating a dataset has no examples.
Outputs both a standard and a quantiles histogram for level N value list length statistics.
Add a macos_arm64 config setting to the TFDV build file. NOTE: At this time, any M1 support for TFDV is experimental and untested.
Bumps the pybind11 version to 2.11.1.
Depends on tensorflow~=2.15.0.
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.
For nested features with N nested levels (N > 1), the statistics counting the number of values in CommonStatistics and WeightedCommonStatistics will rely on the innermost level.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated python 3.8 support.
Deprecated Windows support.

Version 1.14.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Bumped the Ubuntu version on which TFX-BSL is tested to 20.04 (previously was 16.04).
Use @platforms instead of @bazel_tools//platforms to specify constraints in OSS build.
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.

Known Issues

N/A

Breaking Changes

Moves some non-public arrow_util functions to TFX-BSL.
Changes SkewPair proto to store tf.Examples in serialized format.

Deprecations

N/A

Version 1.13.0

Major Features and Improvements

Introduces a Schema option HistogramSelection to allow numeric drift/skew calculations to use QUANTILES histograms, which are more robust to outliers.

Bug Fixes and Other Changes

Rename statistics_io_impl and default_record_sink (not part of public API).
Update the minimum Bazel version required to build TFDV to 5.3.0.
Depends on numpy~=1.22.0.
Depends on pyfarmhash>=0.2.2,<0.4.
Depends on tensorflow>=2.12.0,<2.13.
Depends on protobuf>=3.20.3,<5.
Depends on tfx-bsl>=1.13.0,<1.14.0.
Depends on tensorflow-metadata>=1.13.1,<1.14.0.

Known Issues

N/A

Breaking Changes

Jensen-Shannon divergence now treats NaN values as always contributing to higher drift score.

Deprecations

Deprecated python 3.7 support.

Version 1.12.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

TFDV is now tested against macOS 12.5 (Monterey).

Known Issues

N/A

Breaking Changes

Depends on tensorflow>=2.11,<3
Depends on tfx-bsl>=1.12.0,<1.13.0.
Depends on tensorflow-metadata>=1.12.0,<1.13.0.

Deprecations

N/A

Version 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.
Add a custom_validate_statistics function to the validation API, and support passing custom validations to validate_statistics. Note that custom validation is not supported on Windows.

Bug Fixes and Other Changes

Fix bug in implementation of semantic_domain_stats_sample_rate.
Add beam metrics on string length
Determine whether to calculate string statistics based on the is_categorical field in the schema string domain.
Histograms counts should now be more accurate for distributions with few distinct values, or frequent individual values.
Nested list length histogram counts are no longer based on the number of values one up in the nested list hierarchy.
Support using jensen-shannon divergence to detect drift and skew for string and categorical features.
get_drift_skew_dataframe now includes a threshold column.
Adds support for NormalizedAbsoluteDifference comparator.
Depends on tensorflow>=1.15.5,<2 or tensorflow>=2.10,<3
Depends on joblib>=1.2.0.

Known Issues

N/A

Breaking Changes

Histogram semantics are slightly changed, so that buckets include their upper bound instead of their lower bound. STANDARD histograms will no longer generate buckets that contain infinite and finite endpoints together.
Introduces StatsOptions.use_sketch_based_topk_uniques replacing experimental_use_sketch_based_topk_uniques. The latter option can still be written, but not read.

Deprecations

N/A

Version 1.10.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Skew pipeline supports counting pairs of feature values in base/test.
Depends on apache-beam[gcp]>=2.40,<3.
Depends on pyarrow>=6,<7.
Depends on tfx-bsl>=1.10.1,<1.11.0.
Depends on tensorflow-metadata>=1.10.0,<1.11.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.9.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on tensorflow>=1.15.5,<2 or tensorflow>=2.9,<3
Depends on tfx-bsl>=1.9.0,<1.10.0.
Depends on tensorflow-metadata>=1.9.0,<1.10.0.

Known Issues

N/A

Breaking Changes

Some fields in feature skew results proto changed names to be more generic.
Removes the unused skew_config.proto

Deprecations

N/A

Version 1.8.0

Major Features and Improvements

From this version we will be releasing python 3.9 wheels.

Bug Fixes and Other Changes

Adds get_statistics_html to the public API.
Fixes several incorrect type annotations.
Schema inference handles derived features.
StatsOptions.to_json now raises an error if it encounters unsupported options.
Depends on apache-beam[gcp]>=2.38,<3.
Depends on tensorflow-metadata>=1.8.0,<1.9.0.
Depends on tfx-bsl>=1.8.0,<1.9.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.7.0

Major Features and Improvements

Adds the DetectFeatureSkew PTransform to the public API, which can be used to detect feature skew between training and serving examples.
Uses sketch-based top-k/uniques in TFDV inmemory mode.

Bug Fixes and Other Changes

Fixes a bug in load_statistics that would cause failure when reading binary protos.
Depends on pyfarmhash>=0.2,<0.4.
Depends on tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3.
Depends on tensorflow-metadata>=1.7.0,<1.8.0.
Depends on tfx-bsl>=1.7.0,<1.8.0.
Depends on apache-beam[gcp]>=2.36,<3.
Updated the documentation for CombinerStatsGenerator to clarify that the first accumulator passed to merge_accumulators may be modified.
Added compression type detection when reading csv header.
Detection of invalid utf8 strings now works regardless of relative frequency.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.6.0

Major Features and Improvements

Introduces a convenience wrapper for handling indexed access to statistics protos.
String features are checked for UTF-8 validity, and the number of invalid strings is reported as invalid_utf8_count.

Bug Fixes and Other Changes

Depends on numpy>=1.16,<2.
Depends on absl-py>=0.9,<2.0.0.
Depends on tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3.
Depends on tensorflow-metadata>=1.6.0,<1.7.0.
Depends on tfx-bsl>=1.6.0,<1.7.0.
Depends on apache-beam[gcp]>=2.35,<3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.5.0

Major Features and Improvements

Bug Fixes and Other Changes

BasicStatsGenerator is now responsible for setting the global num_examples. This field will no longer be populated at the DatasetFeatureStatistics level if default generators are disabled.
Depends on apache-beam[gcp]>=2.34,<3.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3.
Depends on tensorflow-metadata>=1.5.0,<1.6.0.
Depends on tfx-bsl>=1.5.0,<1.6.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.4.0

Major Features and Improvements

Float features can now be analyzed as categorical for the purposes of top-k and unique count using experimental sketch based generators.
Support SQL based slicing in TFDV. This would enable slicing (using SQL) in TFX OSS and Dataflow environments. SQL based slicing is currently not supported on Windows.

Bug Fixes and Other Changes

Variance calculations have been updated to be more numerically stable for large datasets or large magnitude numeric data.
When running per-example validation against a schema, output of validate_examples_in_tfrecord and validate_examples_in_csv now optionally return samples of anomalous examples.
Changes to source code ensures that it can now work with pyarrow>=3.
Add load_anomalies_binary utility function.
Merge two accumulators at a time instead of batching.
BasicStatsGenerator is now responsible for setting FeatureNameStatistics.Type. Previously it was possible for a top-k generator and BasicStatsGenerator to set different types for categorical numeric features with physical type STRING.
Depends on pyarrow>=1,<6.
Depends on tensorflow-metadata>=1.4,<1.5.
Depends on tfx-bsl>=1.4,<1.5.
PartitionedStatsFn can optionally provide their own PTransform to control how inputs are partitioned.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated python 3.6 support.

Version 1.3.0

Major Features and Improvements

Bug Fixes and Other Changes

Fixed bug in JensenShannonDivergence calculation affecting comparisons of histograms that each contain a single value.
Fixed bug in dataset constraints validation that caused failures with very large numbers of examples.
Fixed a bug wherein slicing on a feature missing from some batches could produce slice keys derived from a different feature.
Depends on apache-beam[gcp]>=2.32,<3.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,<3.
Depends on tfx-bsl>=1.3,<1.4.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.2.0

Major Features and Improvements

Added statistics/generators/mutual_information.py. It estimates AMI using a knn estimation. It differs from sklearn_mutual_information.py in that this supports multivalent features/labels (by encoding) and multivariate features/labels. The plan is to deprecate sklearn_mutual_information.py in the future.
Fixed NonStreamingCustomStatsGenerator to respect max_batches_per_partition.

Bug Fixes and Other Changes

Switched from namedtuple to tfx_namedtuple in order to avoid pickling issues with PySpark.
Depends on 'scikit-learn>=0.23,<0.24' ("mutual-information" extra only)
Depends on 'scipy>=1.5,<2' ("mutual-information" extra only)
Depends on apache-beam[gcp]>=2.31,<3.
Depends on tensorflow-metadata>=1.2,<1.3.
Depends on tfx-bsl>=1.2,<1.3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.1.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on google-cloud-bigquery>=1.28.0,<2.21.
Depends on tfx-bsl>=1.1.1,<1.2.
Fixes error when using tfdv.experimental_get_feature_value_slicer with pandas==1.3.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.1.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Optimized certain stats generators that needs to materialize the input RecordBatches.
Depends on protobuf>=3.13,<4.
Depends on tensorflow-metadata>=1.1,<1.2.
Depends on tfx-bsl>=1.1,<1.2.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 1.0.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Increased the threshold beyond which a string feature value is considered "large" by the experimental sketch-based top-k/unique generator to 1024.
Added normalized AMI to sklearn mutual information generator.
Depends on apache-beam[gcp]>=2.29,<3.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3.
Depends on tensorflow-metadata>=1.0,<1.1.
Depends on tfx-bsl>=1.0,<1.1.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Removed the following deprecated symbols. Their deprecation was announced in 0.30.0.

tfdv.validate_instance
tfdv.lift_stats_generator
tfdv.partitioned_stats_generator
tfdv.get_feature_value_slicer

Removed parameter compression_type in tfdv.generate_statistics_from_tfrecord

Version 0.30.0

Major Features and Improvements

This version is the last version before TFDV 1.0. Once 1.0, all the TFDV public APIs (i.e. symbols in the root __init__.py) will be subject to semantic versioning. We are deprecating some public APIs in this version and they will be removed in 1.0.
Sketch-based top-k/unique stats generator now is able to detect invalid utf-8 sequences / large texts and replace them with a placeholder. It will not suffer from memory issue usually caused by image / large text features in the data. Note that this generator is not by default used yet.
Added StatsOptions.experimental_use_sketch_based_topk_uniques which enables the sketch-based top-k/unique stats generator.

Bug Fixes and Other Changes

Fixed bug in display_schema that caused domains not to be displayed.
Modified how get_schema_dataframe outputs numeric domains.
Anomalies previously (un)classified as UKNOWN_TYPE now trigger more specific anomaly types: INVALID_DOMAIN_SPECIFICATION and MULTIPLE_REASONS.
Depends on tensorflow-metadata>=0.30,<0.31.
Depends on tfx-bsl>=0.30,<0.31.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

tfdv.LiftStatsGenerator is going to be removed in the next version from the public API. To enable that generator, supply StatsOptions.label_feature
tfdv.NonStreamingCustomStatsGenerator is going to be removed in the next version from the public API. You may continue to import it from TFDV but it will not be subject to compatibility guarantees.
tfdv.validate_instance is going to be removed in the next version from the public API. You may continue to import it from TFDV but it will not be subject to compatibility guarantees.
Removed tfdv.DecodeCSV, tfdv.DecodeTFExample (deprecated in 0.27).
Removed feature_whitelist in tfdv.StatsOptions (deprecated in 0.28). Use feature_allowlist instead.
tfdv.get_feature_value_slicer is deprecated. tfdv.experimental_get_feature_value_slicer is introduced as a replacement. TFDV is likely to have a different slicing functionality post 1.0, which may not be compatible with the current slicers.
StatsOptions.slicing_functions is deprecated. StatsOptions.experimental_slicing_functions is introduced as a replacement.
tfdv.WriteStatisticsToText is removed (deprecated in 0.25.0).
Parameter compression_type in tfdv.generate_statistics_from_tfrecord is deprecated. The compression type is currently automatically determined.

Version 0.29.0

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Added check for invalid min and max values for values_counts for nested features.
Bumped the mininum bazel version required to build TFDV to 3.7.2.
Depends on absl-py>=0.9,<0.13.
Depends on tensorflow-metadata>=0.29,<0.30.
Depends on tfx-bsl>=0.29,<0.30.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 0.28.0

Major Features and Improvements

Add anomaly detection for max bytes size for images.

Bug Fixes and Other Changes

Depends on numpy>=1.16,<1.20.
Fixed a bug that affected all CombinerFeatureStatsGenerators.
Allow for bytes type in get_feature_value_slicer in addition to Text and int.
Fixed a bug that caused TFDV to improperly infer a fixed shape when tfdv.infer_schema and tfdv.update_schema were called with infer_feature_shape=True.
Deprecated parameter infer_feature_shape of function tfdv.update_schema. If a schema feature has a pre-defined shape, tfdv.update_schema will always validate it. Otherwise, it will not try to add a shape.
Deprecated tfdv.StatsOptions.feature_whitelist and added feature_allowlist as a replacement. The former will be removed in the next release.
Added get_schema_dataframe and get_anomalies_dataframe utility functions.
Depends on apache-beam[gcp]>=2.28,<3.
Depends on tensorflow-metadata>=0.28,<0.29.
Depends on tfx-bsl>=0.28.1,<0.29.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 0.27.0

Major Features and Improvements

Performance improvement to BasicStatsGenerator.

Bug Fixes and Other Changes

Added a compact() and setup() interface to CombinerStatsGenerator, CombinerFeatureStatsWrapperGenerator, BasicStatsGenerator, CompositeStatsGenerator, and ConstituentStatsGenerator.
Stopped depending on tensorflow-transform.
Depends on apache-beam[gcp]>=2.27,<3.
Depends on pyarrow>=1,<3.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3.
Depends on tensorflow-metadata>=0.27,<0.28.
Depends on tfx-bsl>=0.27,<0.28.

Known Issues

N/A

Breaking changes

N/A

Deprecations

tfdv.DecodeCSV and tfdv.DecodeTFExample are deprecated. Use tfx_bsl.public.tfxio.CsvTFXIO and tfx_bsl.public.tfxio.TFExampleRecord instead.

Version 0.26.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.25,!=2.26.*,<2.29.

Known Issues

N/A

Breaking changes

N/A

Deprecations

N/A

Version 0.26.0

Major Features and Improvements

Added support for per-feature example weights which allows associating each column its specific weight column. See the per_feature_weight_override parameter in StatsOptions.__init__.

Bug Fixes and Other Changes

Newly added LifecycleStage.DISABLED is now exempt from validation (similar to LifecycleStage.DEPRECATED, etc).
Fixed a bug where TFDV blindly trusts the claim type in the provided schema. TFDV now computes the stats according to the actual type of the data, and only when the actual type matches the claim in the schema will it compute type-specific stats (e.g. categorical ints).
Added an option to control whether to add default stats generators when tfdv.GenerateStatistics().
Started using a new quantiles computation routine that does not depend on TF. This could potentially increase the performance of TFDV under certain workloads.
Extending schema_util to support sematic domains.
Moving natural_language_stats_generator to natural_language_domain_inferring_stats_generator and creating a new natural_language_stats_generator based on the fields of natural_language_domain.
Providing vocab_utils to assist in opening / loading vocabulary files.
A SchemaDiff will be reported upon J-S skew/drift.
Fixed a bug in FLOAT_TYPE_SMALL_FLOAT anomaly message.
Depends on apache-beam[gcp]>=2.25,!=2.26.*,<3.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.4.*,<3.
Depends on tensorflow-metadata>=0.26,<0.27.
Depends on tensorflow-transform>=0.26,<0.27.
Depends on tfx-bsl>=0.26,<0.27.

Known Issues

N/A

Breaking changes

N/A

Deprecations

N/A

Version 0.25.0

Major Features and Improvements

Add support for detecting drift and distribution skew in numeric features.
tfdv.validate_statistics now also reports the raw measurements of distribution skew/drift (if any is done), regardless whether skew/drift is detected. The report is in the drift_skew_info of the Anomalies proto (return value of validate_statistics).
From this release TFDV will also be hosting nightly packages on https://pypi-nightly.tensorflow.org. To install the nightly package use the following command:
```
pip install --extra-index-url https://pypi-nightly.tensorflow.org/simple tensorflow-data-validation
```
Note: These nightly packages are unstable and breakages are likely to happen. The fix could often take a week or more depending on the complexity involved for the wheels to be available on the PyPI cloud service. You can always use the stable version of TFDV available on PyPI by running the command pip install tensorflow-data-validation .

Bug Fixes and Other Changes

Added tfdv.load_stats_binary to load stats what were written using tfdv.WriteStatisticsToText (now tfdv.WriteStatisticsToBinaryFile).
Anomalies previously (un)classified as UKNOWN_TYPE now trigger more specific anomaly types: DOMAIN_INVALID_FOR_TYPE, UNEXPECTED_DATA_TYPE, FEATURE_MISSING_NAME, FEATURE_MISSING_TYPE, INVALID_SCHEMA_SPECIFICATION
Fixed a bug that import tensorflow_data_validation would fail if IPython is not installed. IPython is an optional dependency of TFDV.
Depends on apache-beam[gcp]>=2.25,<3.
Depends on tensorflow-metadata>=0.25,<0.26.
Depends on tensorflow-transform>=0.25,<0.26.
Depends on tfx-bsl>=0.25,<0.26.
Depends on scikit-learn>=1.0,<2 (mutual-information installation).

Known Issues

N/A

Breaking Changes

tfdv.WriteStatisticsToText is renamed as tfdv.WriteStatisticsToBinaryFile. The former is still available but will be removed in a future release.

Deprecations

N/A

Version 0.24.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.24,<3.
Depends on tensorflow-transform>=0.24.1,<0.25.
Depends on tfx-bsl>=0.24.1,<0.25.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Version 0.24.0

Major Features and Improvements

You can now build the TFDV wheel with python setup.py bdist_wheel. Note:
If you want to build a manylinux2010 wheel you'll still need to use Docker.
Bazel is still required.
You can now build manylinux2010 TFDV wheel for Python 3.8.

Bug Fixes and Other Changes

Support allowlist and denylist features in tfdv.visualize_statistics method.
Depends on absl-py>=0.9,<0.11.
Depends on pandas>=1.0,<2.
Depends on protobuf>=3.9.2,<4.
Depends on tensorflow-metadata>=0.24,<0.25.
Depends on tensorflow-transform>=0.24,<0.25.
Depends on tfx-bsl>=0.24,<0.25.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated Py3.5 support.
Deprecated sample_count option in tfdv.StatsOptions. Use sample_rate option instead.

Version 0.23.1

Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.24,<3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecating python 3.5 support.

Version 0.23.0

Major Features and Improvements

Data validation is now able to handle arbitrarily nested arrow List/LargeList types. Schema entries for features with multiple nest levels describe the value count at each level in the value_counts field.
Add combiner stats generator to estimate top-K and uniques using Misra-Gries and K-Minimum Values sketches.

Bug Fixes and Other Changes

Validate that enough supported images are present (if image_domain.minimum_supported_image_fraction is provided).
Stopped requiring avro-python3.
Depends on apache-beam[gcp]>=2.23,<3.
Depends on pyarrow>=0.17,<0.18.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,<3.
Depends on tensorflow-metadata>=0.23,<0.24.
Depends on tensorflow-transform>=0.23,<0.24.
Depends on tfx-bsl>=0.23,<0.24.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

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

Version 0.22.2

Major Features and Improvements

Bug Fixes and Other Changes

Fixed a bug that affected tfx 0.22.0 to work with TFDV 0.22.1.
Depends on 'avro-python3>=1.8.1,<1.9.2' on Python 3.5 + MacOS

Known Issues

Breaking Changes

Deprecations

Version 0.22.1

Major Features and Improvements

Statistics generation is now able to handle arbitrarily nested arrow List/LargeList types. Stats about the list elements' presence and valency are computed at each nest level, and stored in a newly added field, valency_and_presence_stats in CommonStatistics.

Bug Fixes and Other Changes

Trigger DATASET_HIGH_NUM_EXAMPLES when a dataset has more than the specified limit on number of examples.
Fix bug in display_anomalies that prevented dataset-level anomalies from being displayed.
Trigger anomalies when a feature has a number of unique values that does not conform to the specified minimum/maximum.
Trigger anomalies when a float feature has unexpected Inf / -Inf values.
Depends on apache-beam[gcp]>=2.22,<3.
Depends on pandas>=0.24,<2.
Depends on tensorflow-metadata>=0.22.2,<0.23.0.
Depends on tfx-bsl>=0.22.1,<0.23.0.

Known Issues

Breaking Changes

Deprecations

Version 0.22.0

Major Features and Improvements

Bug Fixes and Other Changes

Crop values in natural language stats generator.
Switch to using PyBind11 instead of SWIG for wrapping C++ libraries.
CSV decoder support for multivalent columns by using tfx_bsl's decoder.
When inferring a schema entry for a feature, do not add a shape with dim = 0 when min_num_values = 0.
Add utility methods tfdv.get_slice_stats to get statistics for a slice and tfdv.compare_slices to compare statistics of two slices using Facets.
Make tfdv.load_stats_text and tfdv.write_stats_text public.
Add PTransforms tfdv.WriteStatisticsToText and tfdv.WriteStatisticsToTFRecord to write statistics proto to text and tfrecord files respectively.
Modify tfdv.load_statistics to handle reading statistics from TFRecord and text files.
Added an extra requirement group mutual-information. As a result, barebone TFDV does not require scikit-learn any more.
Added an extra requirement group visualization. As a result, barebone TFDV does not require ipython any more.
Added an extra requirement group all that specifies all the extra dependencies TFDV needs. Use pip install tensorflow-data-validation[all] to pull in those dependencies.
Depends on pyarrow>=0.16,<0.17.
Depends on apache-beam[gcp]>=2.20,<3.
Depends on `ipython>=7,<8;python_version>="3"'.
Depends on `scikit-learn>=0.18,<0.24'.
Depends on tensorflow>=1.15,!=2.0.*,<3.
Depends on tensorflow-metadata>=0.22.0,<0.23.
Depends on tensorflow-transform>=0.22,<0.23.
Depends on tfx-bsl>=0.22,<0.23.

Known Issues

(Known issue resolution) It is no longer necessary to use Apache Beam 2.17 when running TFDV on Windows. The current release of Apache Beam will work.

Breaking Changes

tfdv.GenerateStatistics now accepts a PCollection of pa.RecordBatch instead of pa.Table.
All the TFDV coders now output a PCollection of pa.RecordBatch instead of a PCollection of pa.Table.
tfdv.validate_instances and tfdv.api.validation_api.IdentifyAnomalousExamples now takes pa.RecordBatch as input instead of pa.Table.
The StatsGenerator interface (and all its sub-classes) now takes pa.RecordBatch as the input data instead of pa.Table.
Custom slicing functions now accepts a pa.RecordBatch instead of pa.Table as input and should output a tuple (slice_key, record_batch).

Deprecations

Deprecating Py2 support.

Release 0.21.5

Major Features and Improvements

Add label_feature to StatsOptions and enable LiftStatsGenerator when label_feature and schema are provided.
Add JSON serialization support for StatsOptions.

Bug Fixes and Other Changes

Only requires avro-python3>=1.8.1,!=1.9.2.*,<2.0.0 on Python 3.5 + MacOS

Breaking Changes

Deprecations

Release 0.21.4

Major Features and Improvements

Support visualizing feature value lift in facets visualization.

Bug Fixes and Other Changes

Fix issue writing out string feature values in LiftStatsGenerator.
Requires 'apache-beam[gcp]>=2.17,<3'.
Requires 'tensorflow-transform>=0.21.1,<0.22'.
Requires 'tfx-bsl>=0.21.3,<0.22'.

Breaking Changes

Deprecations

Release 0.21.2

Major Features and Improvements

Bug Fixes and Other Changes

Fix facets visualization.
Optimize LiftStatsGenerator for string features.
Make _WeightedCounter serializable.
Add support computing for weighted examples in LiftStatsGenerator.

Breaking Changes

Deprecations

tfdv.TFExampleDecoder has been removed. This legacy decoder converts serialized tf.Example to a dict of numpy arrays, which is the legacy input format (prior to Apache Arrow). TFDV has stopped accepting that format since 0.14. Use tfdv.DecodeTFExample instead.

Release 0.21.1

Major Features and Improvements

Bug Fixes and Other Changes

Do validation on weighted feature stats.
During schema inference, skip features which are missing common stats. This makes schema inference work when the input stats are generated from some pre-existing, unknown schema.
Fix facets visualization in Chrome >=M80.

Known Issues

Running TFDV with Apache Beam 2.18 or 2.19 does not work on Windows. If you are using TFDV on Windows, use Apache Beam 2.17.

Breaking Changes

Deprecations

Release 0.21.0

Major Features and Improvements

Started depending on the CSV parsing / type inferring utilities provided by tfx-bsl (since tfx-bsl 0.15.2). This also brings performance improvements to the CSV decoder (~2x faster in decoding. Type inferring performance is not affected).
Compute bytes statistics for features of BYTES type. Avoid computing topk and uniques for such features.
Added LiftStatsGenerator which computes lift between one feature (typically a label) and all other categorical features.

Bug Fixes and Other Changes

Exclude examples in which the entire sparse feature is missing when calculating sparse feature statistics.
Validate min_examples_count dataset constraint.
Document the schema fields, statistics fields, and detection condition for each anomaly type that TFDV detects.
Handle null array in cross feature stats generator, top-k & uniques combiner stats generator, and sklearn mutual information generator.
Handle infinity in basic stats generator.
Set num_missing and num_examples correctly in the presence of sparse features.
Compute weighted feature stats for all weighted features declared in schema.
Enforce that mutual information is non-negative.
Depends on tensorflow-metadata>=0.21.0,<0.22.
Depends on pyarrow>=0.15 (removed the upper bound as it is determined by tfx-bsl).
Depends on tfx-bsl>=0.21.0,<0.22
Depends on apache-beam>=2.17,<3
Validate that float feature does not contain NaNs (if disallow_nan is True).

Breaking Changes

Changed the behavior regarding to statistics over CSV data:
- Previously, if a CSV column was mixed with integers and empty strings, FLOAT statistics will be collected for that column. A change was made so INT statistics would be collected instead.
Removed csv_decoder.DecodeCSVToDict as Dict[str, np.ndarray] had no longer been the internal data representation any more since 0.14.

Deprecations

Release 0.15.0

Major Features and Improvements

Generate statistics for sparse features.
Directly convert a batch of tf.Examples to Arrow tables. Avoids conversion of tf.Example to intermediate Dict representation.

Bug Fixes and Other Changes

Generate statistics for the weight feature.
Support validation and schema inference from sliced statistics that include the default slice (validation/inference will be done using the default slice statistics).
Avoid flattening null arrays.
Set weighted_num_examples field in the statistics proto if a weight feature is specified.
Replace DecodedExamplesToTable with a Python implementation.
Building TFDV from source does not need pyarrow anymore.
Depends on apache-beam[gcp]>=2.16,<3.
Depends on six>=1.12,<2.
Depends on scikit-learn>=0.18,<0.22.
Depends on tfx-bsl>=0.15,<0.16.
Depends on tensorflow-metadata>=0.15,<0.16.
Depends on tensorflow-transform>=0.15,<0.16.
Depends on tensorflow>=1.15,<3.
- 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-data-validation, it will be replaced with tensorflow 2.0.0. Re-install tensorflow-gpu 2.0.0 if needed.

Breaking Changes

Deprecations

Release 0.14.1

Major Features and Improvements

Add support for custom schema transformations when inferring schema.

Bug Fixes and Other Changes

Fix incorrect file hashes in the TFDV wheel.
Fix DOMException when embedding visualization in iframe.

Breaking Changes

Deprecations

Release 0.14.0

Major Features and Improvements

Performance improvement due to optimizing inner loops.
Add support for time semantic domain related statistics.
Performance improvement due to batching accumulators before merging.
Add utility method validate_examples_in_tfrecord, which identifies anomalous examples in TFRecord files containing TFExamples and generates statistics for those anomalous examples.
Add utility method validate_examples_in_csv, which identifies anomalous examples in CSV files and generates statistics for those anomalous examples.
Add fast TF example decoder written in C++.
Make BasicStatsGenerator to take arrow table as input. Example batches are converted to Apache Arrow tables internally and we are able to make use of vectorized numpy functions. Improved performance of BasicStatsGenerator by ~40x.
Make TopKUniquesStatsGenerator and TopKUniquesCombinerStatsGenerator to take arrow table as input.
Add update_schema API which updates the schema to conform to statistics.
Add support for validating changes in the number of examples between the current and previous spans of data (using the existing validate_statistics function).
Support building a manylinux2010 compliant wheel in docker.
Add support for cross feature statistics.

Bug Fixes and Other Changes

Expand unit test coverage.
Update natural language stats generator to generate stats if actual ratio equals match_ratio.
Use __slots__ in accumulators.
Fix overflow warning when generating numeric stats for large integers.
Set max value count in schema when the feature has same valency, thereby inferring shape for multivalent required features.
Fix divide by zero error in natural language stats generator.
Add load_anomalies_text and write_anomalies_text utility functions.
Define ReasonFeatureNeeded proto.
Add support for Windows OS.
Make semantic domain stats generators to take arrow column as input.
Fix error in number of missing examples and total number of examples computation.
Make FeaturesNeeded serializable.
Fix memory leak in fast example decoder.
Add semantic_domain_stats_sample_rate option to compute semantic domain statistics over a sample.
Increment refcount of None in fast example decoder.
Add compression_type option to generate_statistics_from_* methods.
Add link to SysML paper describing some technical details behind TFDV.
Add Python types to the source code.
MakeGenerateStatistics generate a DatasetFeatureStatisticsList containing a dataset with num_examples == 0 instead of an empty proto if there are no examples in the input.
Depends on absl-py>=0.7,<1
Depends on apache-beam[gcp]>=2.14,<3
Depends on numpy>=1.16,<2.
Depends on pandas>=0.24,<1.
Depends on pyarrow>=0.14.0,<0.15.0.
Depends on scikit-learn>=0.18,<0.21.
Depends on tensorflow-metadata>=0.14,<0.15.
Depends on tensorflow-transform>=0.14,<0.15.

Breaking Changes

Change examples_threshold to values_threshold and update documentation to clarify that counts are of values in semantic domain stats generators.
Refactor IdentifyAnomalousExamples to remove sampling and output (anomaly reason, example) tuples.
Rename anomaly_proto parameter in anomalies utilities to anomalies to make it more consistent with proto and schema utilities.
FeatureNameStatistics produced by GenerateStatistics is now identified by its .path field instead of the .name field. For example:
```
feature {
  name: "my_feature"
}
```
becomes:
```
feature {
  path {
    step: "my_feature"
  }
}
```
Change validate_instance API to accept an Arrow table instead of a Dict.
Change GenerateStatistics API to accept Arrow tables as input.

Deprecations

Release 0.13.1

Major Features and Improvements

Bug Fixes and Other Changes

Modify validation logic to raise SCHEMA_MISSING_COLUMN anomaly when observing a feature with no stats (was still broken, now fixed).

Breaking Changes

Deprecations

Release 0.13.0

Major Features and Improvements

Use joblib to exploit multiprocessing when computing statistics over a pandas dataframe.
Add support for semantic domain related statistics (natural language, image), enabled by StatsOptions.enable_semantic_domain_stats.
Python 3.5 is supported.

Bug Fixes and Other Changes

Expand unit test coverage.
Modify validation logic to raise SCHEMA_MISSING_COLUMN anomaly when observing a feature with no stats.
Add utility functions write_stats_text and load_stats_text to write and load DatasetFeatureStatisticsList protos.
Avoid using multiprocessing by default when generating statistics over a dataframe.
Depends on joblib>=0.12,<1.
Depends on tensorflow-transform>=0.13,<0.14.
Depends on tensorflow-metadata>=0.12.1,<0.14.
Requires pre-installed tensorflow>=1.13.1,<2.
Depends on apache-beam[gcp]>=2.11,<3.
Depends on absl>=0.1.6,<1.

Breaking Changes

Deprecations

Release 0.12.0

Major Features and Improvements

Add support for computing statistics over slices of data.
Performance improvement due to optimizing inner loops.
Add support for generating statistics from a pandas dataframe.
Performance improvement due to pre-allocating tf.Example in TFExampleDecoder.
Performance improvement due to merging common stats generator, numeric stats generator and string stats generator as a single basic stats generator.
Performance improvement due to merging top-k and uniques generators.
Add a validate_instance function, which checks a single example for anomalies.
Add a utility method get_statistics_html, which returns HTML that can be used for Facets visualization outside of a notebook.
Add support for schema inference of semantic domains.
Performance improvement on statistics computation over a pandas dataframe.

Bug Fixes and Other Changes

Use constant 'BYTES_VALUE' in the statistics proto to represent a bytes value which cannot be decoded as a utf-8 string.
Introduced CombinerFeatureStatsGenerator, a specialized interface for combiners that do not require cross-feature computations.
Expand unit test coverage.
Add optional frequency threshold that allows keeping only the most frequent values that are present in a minimum number of examples.
Add optional desired batch size that allows specification of the number of examples to include in each batch.
Depends on numpy>=1.14.5,<2.
Depends on protobuf>=3.6.1,<4.
Depends on apache-beam[gcp]>=2.10,<3.
Depends on tensorflow-metadata>=0.12.1,<0.13.
Depends on scikit-learn>=0.18,<1.
Depends on IPython>=5.0.
Requires pre-installed tensorflow>=1.12,<2.
Revise example notebook and update it to be able to run in Colab and Jupyter.

Breaking changes

Represent batch as a list of ndarrays instead of ndarrays of ndarrays.
Modify decoders to return ndarrays of type numpy.float32 for FLOAT features.

Deprecations

Release 0.11.0

Major Features and Improvements

Add option to infer feature types from schema when generating statistics over CSV data.
Add utility method set_domain to set the domain of a feature in the schema.
Add option to compute weighted statistics by providing a weight feature.
Add a PTransform for decoding TF examples.
Add utility methods write_schema_text and load_schema_text to write and load the schema protocol buffer.
Add option to compute statistics over a sample.
Optimize performance of statistics computation (~2x improvement on benchmark datasets).

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.8,<3.
Depends on tensorflow-transform>=0.11,<0.12.
Depends on tensorflow-metadata>=0.9,<0.10.
Fix bug in clearing oneof domain_info field in Feature proto.
Fix overflow error for large integers by casting them to STRING type.
Added API docs.

Breaking changes

Requires pre-installed tensorflow>=1.11,<2.
Make tf.Example decoder to represent a feature with no value list as a missing value (None).
Make StatsOptions as a class.

Deprecations

Release 0.9.0

Initial release of TensorFlow Data Validation.

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