Update RELEASE.md

Author: dhruvesh09

RELEASE.md

@@ -57,392 +57,3 @@
 ## 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.
-* Make`GenerateStatistics` 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.