Infinite Training 2.1.0

Backward compatible with 2.0.0: existing code keeps working and warns where an
API has been renamed.

Fixed

• Target(target_value=0) is now honoured. The default was selected with
  target_value or <bound>, so a falsy 0/0.0 was replaced by an unreachable
  bound and the session could never stop on that target.
• last_value no longer raises AttributeError when read before training, or
  after an interrupt during the first round. It is derived from the persisted
  value history, so it also survives a restart.
• Target() is no longer used as a default argument value, which meant every
  trainer built without an explicit target shared one mutable instance.
• Calling train() or an inference method before compile() now raises a clear
  RuntimeError instead of AttributeError: 'InfiniteTrainer' object has no attribute 'best_model'.
• A target naming a metric that is absent from the training history now raises a
  RuntimeError listing the available keys, instead of a bare KeyError. When
  the missing key starts with val_, the message points at validation_data.
• Checkpoint parent directories are created automatically; previously a path
  such as runs/exp1/best.npy failed unless the directory already existed.
• Restored checkpoint values are converted back to Python floats and plain
  weight lists rather than being left as zero-dimensional object arrays.
• KeyboardInterrupt is logged instead of silently swallowed.
• requires-python corrected from >=3.9 to >=3.10. TensorFlow has required
  3.10 or newer since 2.16, so the old floor advertised an uninstallable
  combination.

Added

• InfiniteTrainer, the new name for InfinityTraining.
• rounds_completed property.
• save() as a public method for checkpointing on demand.
• Target.is_improvement(), Target.is_reached() and
  Target.worst_possible_value, so the comparison rules live with the target.
• __version__ and __all__ exports.
• A CI workflow running lint, tests on Python 3.10-3.12, and a packaging check
  on every push and pull request. Previously tests ran only when a release was
  published.
• The publish workflow verifies that the release tag matches the packaged
  version.
• CONTRIBUTING.md, CHANGELOG.md, and a rewritten README.md with an API
  reference, a resume guide and a security note.
• Test suite covering targets, stop conditions, persistence and resume,
  error handling, and the deprecation shims (55 tests, up from 3).

Changed

• The package is split into target.py, trainer.py and _storage.py. Public
  imports are unchanged: from infinite_training import InfiniteTrainer, Target.
• Type hints use float rather than np.double for scalar values.
• Progress and stop reasons are reported through the logging module.
• run.py moved to examples/mnist.py and no longer ships inside the wheel.
• Packaging declares its packages explicitly, so tests is no longer picked up
  by auto-discovery.

Removed

• The unused polars runtime dependency.

Deprecated

The following still work and will be removed in 3.0.0:

Deprecated	Replacement
InfinityTraining	InfiniteTrainer
optimize_weight	best_weights
last_weight	last_weights
optimize_value	best_value
list_value	value_history
optimize_model	best_model
predict_optimize()	predict_best()
optimize_weight_path=	best_weights_path=
last_weight_path=	last_weights_path=
optimize_value_path=	best_value_path=
list_value_path=	value_history_path=

Full changelog: https://github.com/vyncint/infinite-training/blob/main/CHANGELOG.md
Compare: v2.0.0...v2.1.0