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.

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.

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).

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.