rasa 2.8.0 on Python PyPI

#9045: The option model_confidence=linear_norm is deprecated and will be removed in Rasa Open Source 3.0.0.
Rasa Open Source 2.3.0 introduced linear_norm as a possible value for model_confidence
parameter in machine learning components such as DIETClassifier, ResponseSelector and TEDPolicy.
Based on user feedback, we have identified multiple problems with this option.
Therefore, model_confidence=linear_norm is now deprecated and
will be removed in Rasa Open Source 3.0.0. If you were using model_confidence=linear_norm for any of the mentioned components,
we recommend to revert it back to model_confidence=softmax and re-train the assistant. After re-training,
we also recommend to re-tune the thresholds for fallback components.
#9091: The fallback mechanism for spaCy models has now been removed in Rasa 3.0.0.
Rasa Open Source 2.5.0 introduced support for spaCy 3.0. This introduced a
breaking feature because models would no longer be manually linked. To make
the transition smooth Rasa would rely on the language parameter in the
config.yml to fallback to a medium spaCy model if no model was configured
for the SpacyNLP component. In Rasa Open Source 3.0.0 and onwards the
SpacyNLP component will require the model name (like "en_core_web_md")
to be passed explicitly.

Features

#8724: Added sasl_mechanism as an optional configurable parameters for the Kafka Producer.
#8913: Introduces a new policy called UnexpecTEDIntentPolicy.
UnexpecTEDIntentPolicy helps you review conversations
and also allows your bot to react to unexpected user turns in conversations.
It is an auxiliary policy that should only be used in conjunction with
at least one other policy, as the only action that it can trigger
is the special and newly introduced
action_unlikely_intent action.
The auto-configuration will include UnexpecTEDIntentPolicy in your
configuration automatically, but you can also include it yourself
in the policies section of the configuration:
```
policies:
  - name: UnexpecTEDIntentPolicy
    epochs: 200
    max_history: 5
```
As part of the feature, it also introduces:
- IntentMaxHistoryTrackerFeaturizer
  to featurize the trackers for UnexpecTEDIntentPolicy.
- MultiLabelDotProductLoss to support UnexpecTEDIntentPolicy's multi-label training objective.
- A new default action called action_unlikely_intent.
rasa test command has also been adapted to UnexpecTEDIntentPolicy:
- If a test story contains action_unlikely_intent and the policy ensemble does not trigger it, this leads to
  a test error (wrongly predicted action) and the corresponding story will be logged in failed_test_stories.yml.
- If the story does not contain action_unlikely_intent and Rasa Open Source does predict it then
  the prediction of action_unlikely_intent will be ignored for the evaluation (and hence not lead
  to a prediction error) but the story will be logged in a file called stories_with_warnings.yml.
The rasa data validate command will warn if action_unlikely_intent is
included in the training stories. Accordingly, YAMLStoryWriter and MarkdownStoryWriter have been updated to not dump action_unlikely_intent when writing stories to a file.
:::caution
The introduction of a new default action breaks backward compatibility of previously trained models.
It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train
your assistant before trying to use this version.
:::

Improvements

#8127: Added detailed json schema validation for UserUttered, SlotSet, ActionExecuted and EntitiesAdded events both sent and received from the action server, as well as covered at high-level the validation of the rest of the 20 events.
In case the events are invalid, a ValidationError will be raised.
#8232: Users don't need to specify an additional buffer size for sparse featurizers anymore during incremental training.
Space for new sparse features are created dynamically inside the downstream machine learning
models - DIETClassifier, ResponseSelector. In other words, no extra buffer is created in
advance for additional vocabulary items and space will be dynamically allocated for them inside the model.
This means there's no need to specify additional_vocabulary_size for CountVectorsFeaturizer or
number_additional_patterns for RegexFeaturizer. These parameters are now deprecated.
Before
```
pipeline:
  - name: "WhitespaceTokenizer"
  - name: "RegexFeaturizer"
    number_additional_patterns: 100
  - name: "CountVectorsFeaturizer"
    additional_vocabulary_size: {text: 100, response: 20}
```
Now
```
pipeline:
  - name: "WhitespaceTokenizer"
  - name: "RegexFeaturizer"
  - name: "CountVectorsFeaturizer"
```
Also, all custom layers specifically built for machine learning models - RasaSequenceLayer, RasaFeatureCombiningLayer
and ConcatenateSparseDenseFeatures now inherit from RasaCustomLayer so that they support flexible incremental training out of the box.
#8295: Speed up the contradiction check of the RulePolicy
by a factor of 3.
#8801: Change the confidence score assigned by FallbackClassifier to fallback intent to be the same as the fallback threshold.
#8926: Issue a UserWarning if a specified domain folder contains files that look like YML files but cannot be parsed successfully.
Only invoked if user specifies a folder path in --domain paramater. Previously those invalid files in the specified folder were silently ignored.
Does not apply to individually specified domain YAML files, e.g. --domain /some/path/domain.yml, those being invalid will still raise an exception.

Bugfixes

#8711: Fix for unnecessary retrain and duplication of folders in the model

Miscellaneous internal changes

#8241, #8525, #8694, #8704