Ktrain

New:
- support for `tabular` data including explainable AI for tabular predictions
- `learner.validate` and `learner.evaluate` now support regression models
- added `restore_weights_only` flag to `lr_find`. When True, only the model weights will be restored after
simulating training, not the optimizer weights. In at least a few observed cases, this "warm up" seems to improve performance
when actual training begins. Further investigation is needed, so it is False by default.

Changed
- N/A

Fixed:
- added `save_path` argument to `Learner.validate` and `Learner.evaluate`. If `print_report=False`, classification
report will be saved as CSV to `save_path`.
- Use `torch.no_grad` with `ZeroShotClassifier.predict` to [prevent OOM](https://github.com/amaiya/ktrain/issues/215)
- Added `max_length` parameter to `ZeroShotClassifier.predict` to [prevent errors on long documnets](https://github.com/amaiya/ktrain/issues/215)
- Added type check to `TransformersPreprocessor.preprocess_train`

New:
- N/A

Changed
- N/A

Fixed:
- Changed `qa` module to use use 'Auto' when loading `QuestionAnswering` models and tokenizer
- try `from_pt=True` for `qa` module if initial model-loading fails
- use `get_hf_model_name` in `qa` module

New:
- N/A

Changed
- N/A

Fixed:
- return gracefully if no documents match question in `qa` module
- tokenize question in `qa` module to ensure all candidate documents are returned
- Added error in `text.preprocessor` when training set has incomplete integer labels

New:
- added `batch_size` argument to `ZeroShotClassifier.predict` that can be increased to speed up predictions.
This is especially useful if `len(topic_strings)` is large.

Changed
- N/A

Fixed:
- fixed typo in `load_predictor` error message

New:
- N/A

Changed
- updated doc comments in core module
- removed unused `nosave` parameter from `reset_weights`
- added warning about obsolete `show_wd` parameter in `print_layers` method
- pin to `scipy==1.4.1` due to TensorFlow requirement

Fixed:
- N/A

New:
- N/A

Changed
- Use `tensorflow==2.1.0` if Python 3.6/3.7 and use `tensorflow==2.2.0` only if on Python 3.8 due to TensorFlow v2.2.0 issues

Fixed:
- N/A