Release 1.3.0

Major Features and Improvements

• Added locality-sensitive hashing (LSH) support to the graph builder tool.
  This allows the graph builder to scale up to larger input datasets. As part
  of this change, the new nsl.configs.GraphBuilderConfig class was
  introduced, as well as a new nsl.tools.build_graph_from_config function.
  The new parameters for controlling the LSH algorithm are named lsh_rounds
  and lsh_splits.

Bug Fixes and Other Changes

• Changed nsl.tools.add_edge to return a boolean result indicating if a new
  edge was added or not; previously, this function was not returning any
  value.
• Fixed a bug in nsl.tools.read_tsv_graph that was incrementing the reported
  edge count too often.
• Removed Python 2 unit tests.
• Fixed a bug in nsl.estimator.add_adversarial_regularization and
  nsl.estimator.add_graph_regularization so that the UPDATE_OPS can be
  triggered correctly.
• Updated graph-NSL tutorials not to parse neighbor features during
  evaluation.
• Added scaled graph and adversarial loss values as scalars to the summary in
  nsl.estimator.add_graph_regularization and
  nsl.estimator.add_adversarial_regularization respectively.
• Updated graph and adversarial regularization loss metrics in
  nsl.keras.GraphRegularization and nsl.keras.AdversarialRegularization
  respectively, to include scaled values for consistency with their respective
  loss term contributions.

Thanks to our Contributors

This release contains contributions from many people at Google.