Tensorflow-similarity

This release focus on improving and extending data samplers.

Added

- `SingleShotMemorySampler()` is available
- Data samplers now support:
- The use of a data augmenter
- Having a warmup period before augmentation
- `single_lookup()` now returns the rank of each match.
- Memory Samplers now scale to millions of examples without slowdown.

Fixes

- Fixed the issue where model couldn't be reloaded due to a bug in load_model()

Changed

- Rewrote the sampler interface to allows better customization and subclassing.

[0.6.x] - Distance Metrics

This release add specialized distance metrics and fixes bugs

Added

- Distance metrics are now avialable under `tensorflow_similarity.distance_metrics` including:
- `dist_gap`: distance between the maximum positive distance and the min negative distance.
- `pos_max`: positive maximum distance
- `neg_min`: negative minimal distance.
- More generally minimal, average and maximal distance for positive and negative anchors are supported. You can either uses the short-hand alias (e.g `neg_min`, `pos_avg`....), or use the underlying `DistanceMetric()` class that allows to configure all aspect of the metric.

This release focus on a major refactor of the internal API to ensure clean decoupling and ensure the package offers streamlined API that makes it easy to extend the package to additional backend.

Added

- Ability to save and reload the model from disk via:
- save: `model.save()`, `index_save()`
- load: `model.load()`, `index_load()`
- Integration tests and many unit tests added to ensure that core packages features are robusts and wont' suffer major regression as revision are released. C/I added as well for automatic deployement.

Fixed

- `Matcher` and `Table` API streamlined to avoid unnecessary data manipulation that results in increased performance.

Changed

- Model API renamed to be more consistent with Keras naming convention to have model verbs. For example: `index_reset()` was renamed `reset_index()`.
- The indexer was refactored to decouple the matcher, index table and matching logic to be in different components that can be transparently replaced with alternatives that implements the abstract class methods.
- `Evaluator` API introduced with major refactoring to the `Model` and `Indexer` class to ensure all the embedding evaluation code is now self contained and have clean interface.

This release focuses on making the package installable via pip and providing a well documented hello world colab.

Initial fully functional alpha release based on the new core engine that leverages TF 2.x improvements over TF 1.x. This release focuses on showcasing the new core API to collect feedback and comments.