Merge branch 'master' into remove-bert

CHANGELOG.rst

@@ -17,6 +17,19 @@ This project adheres to `Semantic Versioning`_ starting with version 1.0.
 
 .. towncrier release notes start
 
+[1.9.6] - 2020-04-15
+^^^^^^^^^^^^^^^^^^^^
+
+Bugfixes
+--------
+- `#5426 <https://github.com/rasahq/rasa/issues/5426>`_: Fix `rasa test nlu` plotting when using multiple runs.
+- `#5489 <https://github.com/rasahq/rasa/issues/5489>`_: Fixed issue where ``max_number_of_predictions`` was not considered when running end-to-end testing.
+
+Miscellaneous internal changes
+------------------------------
+- #5626
+
+
 [1.9.5] - 2020-04-01
 ^^^^^^^^^^^^^^^^^^^^
 

Makefile

@@ -40,7 +40,7 @@ clean:
 	rm -rf docs/_build
 
 install:
-	poetry run python -m pip install -U 'pip<20'
+	poetry run python -m pip install -U pip
 	poetry install
 
 install-mitie:

README.md

@@ -95,33 +95,64 @@ also be asked to sign a
 
 ## Development Internals
 
-### Building from source
+### Installing Poetry
 
 Rasa uses Poetry for packaging and dependency management. If you want to build it from source,
 you have to install Poetry first. This is how it can be done:
 
-```
-curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python3
+```bash
+curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python
 ```
 
 There are several other ways to install Poetry. Please, follow 
 [the official guide](https://python-poetry.org/docs/#installation) to see all possible options.
 
-To install dependencies and `rasa` itself in editable mode execute
+### Managing environments
+
+The official [Poetry guide](https://python-poetry.org/docs/managing-environments/) suggests to use
+[pyenv](https://github.com/pyenv/pyenv) or any other similar tool to easily switch between Python versions. 
+This is how it can be done:
+
+```bash
+pyenv install 3.7.6
+pyenv local 3.7.6  # Activate Python 3.7.6 for the current project
+```
+
+By default, Poetry will try to use the currently activated Python version to create the virtual environment 
+for the current project automatically. You can also create and activate a virtual environment manually — in this
+case, Poetry should pick it up and use it to install the dependencies. For example:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+```
+
+You can make sure that the environment is picked up by executing 
+
+```bash
+poetry env info
 ```
+
+### Building from source
+
+To install dependencies and `rasa` itself in editable mode execute
+
+```bash
 make install
 ```
 
 ### Running and changing the documentation
 
 First of all, install all the required dependencies:
-```
+
+```bash
 make install
 ```
 
 After the installation has finished, you can run and view the documentation
 locally using:
-```
+
+```bash
 make livedocs
 ```
 
@@ -130,25 +161,46 @@ You can now change the docs locally and the web page will automatically reload
 and apply your changes.
 
 ### Running the Tests
+
 In order to run the tests, make sure that you have the development requirements installed:
+
 ```bash
 export PIP_USE_PEP517=false
 make prepare-tests-ubuntu # Only on Ubuntu and Debian based systems
 make prepare-tests-macos  # Only on macOS
 ```
 
 Then, run the tests:
+
 ```bash
 make test
 ```
 
 They can also be run at multiple jobs to save some time:
+
 ```bash
 JOBS=[n] make test
 ```
 
 Where `[n]` is the number of jobs desired. If omitted, `[n]` will be automatically chosen by pytest.
 
+### Resolving merge conflicts
+
+Poetry doesn't include any solution that can help to resolve merge conflicts in
+the lock file `poetry.lock` by default.
+However, there is a great tool called [poetry-merge-lock](https://poetry-merge-lock.readthedocs.io/en/latest/).
+Here is how use can install it:
+
+```bash
+pip install poetry-merge-lock
+```
+
+Just execute this command to resolve merge conflicts in `poetry.lock` automatically:
+
+```bash
+poetry-merge-lock
+```
+
 ### Steps to release a new version
 Releasing a new version is quite simple, as the packages are build and distributed by GitHub Actions.
 

changelog/5631.misc.rst

@@ -0,0 +1 @@
+Set regularization constant in ``EmbeddingIntentClassifier`` to 0.001.

changelog/5633.misc.rst

@@ -0,0 +1,2 @@
+Add more information about the usage of Poetry and the process of merge conflict resolution
+in the lockfile of Poetry to `README.md`.

changelog/5635.improvement.rst

@@ -0,0 +1 @@
+Update dependencies based on the ``dependabot`` check.

changelog/5636.improvement.rst

@@ -0,0 +1,2 @@
+Add dropout between ``FFNN`` and ``DenseForSparse`` layers in ``DIETClassifier``,
+``ResponseSelector`` and ``EmbeddingIntentClassifier`` controlled by ``use_dense_input_dropout`` config parameter.

data/test_evaluations/end_to_end_trips_circuit_breaker.md

@@ -0,0 +1,13 @@
+## story_trips_circuit_breaker
+* greet: /greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet

data/test_evaluations/stories_trip_circuit_breaker.md

@@ -0,0 +1,13 @@
+## story_trips_circuit_breaker
+* greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet
+  - utter_greet

docs/core/forms.rst

@@ -286,7 +286,7 @@ In the restaurant case, your stories would look something like this:
         ( ... all other slots the form set ... )
         - form{"name": null}
 
-Again, is is **strongly** recommended that you use interactive
+Again, it is **strongly** recommended that you use interactive
 learning to build these stories.
 Please read :ref:`section_interactive_learning_forms`
 on how to use interactive learning with forms.

docs/index.rst

@@ -29,6 +29,7 @@ Understand messages, hold conversations, and connect to messaging channels and A
    user-guide/architecture
    user-guide/messaging-and-voice-channels
    user-guide/testing-your-assistant
+   user-guide/setting-up-ci-cd
    user-guide/validate-files
    user-guide/configuring-http-api
    user-guide/how-to-deploy

docs/nlu/components.rst

@@ -922,7 +922,9 @@ EmbeddingIntentClassifier
          | drop_rate                       | 0.2              | Dropout rate for encoder. Value should be between 0 and 1.   |
          |                                 |                  | The higher the value the higher the regularization effect.   |
          +---------------------------------+------------------+--------------------------------------------------------------+
-         | use_sparse_input_dropout        | True             | If 'True' apply dropout to sparse tensors.                   |
+         | use_sparse_input_dropout        | False            | If 'True' apply dropout to sparse input tensors.             |
+         +---------------------------------+------------------+--------------------------------------------------------------+
+         | use_dense_input_dropout         | False            | If 'True' apply dropout to dense input tensors.              |
          +---------------------------------+------------------+--------------------------------------------------------------+
          | evaluate_every_number_of_epochs | 20               | How often to calculate validation accuracy.                  |
          |                                 |                  | Set to '-1' to evaluate just once at the end of training.    |
@@ -1486,7 +1488,9 @@ ResponseSelector
          | drop_rate_attention             | 0.0               | Dropout rate for attention. Value should be between 0 and 1. |
          |                                 |                   | The higher the value the higher the regularization effect.   |
          +---------------------------------+-------------------+--------------------------------------------------------------+
-         | use_sparse_input_dropout        | False             | If 'True' apply dropout to sparse tensors.                   |
+         | use_sparse_input_dropout        | False             | If 'True' apply dropout to sparse input tensors.             |
+         +---------------------------------+-------------------+--------------------------------------------------------------+
+         | use_dense_input_dropout         | False             | If 'True' apply dropout to dense input tensors.              |
          +---------------------------------+-------------------+--------------------------------------------------------------+
          | evaluate_every_number_of_epochs | 20                | How often to calculate validation accuracy.                  |
          |                                 |                   | Set to '-1' to evaluate just once at the end of training.    |
@@ -1715,7 +1719,9 @@ DIETClassifier
          | drop_rate_attention             | 0.0              | Dropout rate for attention. Value should be between 0 and 1. |
          |                                 |                  | The higher the value the higher the regularization effect.   |
          +---------------------------------+------------------+--------------------------------------------------------------+
-         | use_sparse_input_dropout        | True             | If 'True' apply dropout to sparse tensors.                   |
+         | use_sparse_input_dropout        | True             | If 'True' apply dropout to sparse input tensors.             |
+         +---------------------------------+------------------+--------------------------------------------------------------+
+         | use_dense_input_dropout         | True             | If 'True' apply dropout to dense input tensors.              |
          +---------------------------------+------------------+--------------------------------------------------------------+
          | evaluate_every_number_of_epochs | 20               | How often to calculate validation accuracy.                  |
          |                                 |                  | Set to '-1' to evaluate just once at the end of training.    |

docs/user-guide/building-assistants.rst

@@ -155,9 +155,10 @@ For example:
 
 
 While it's good to test the bot interactively, we should also add end to end test cases that
-can later be included as part of our CI/CD system. `End to end stories <https://rasa.com/docs/rasa/user-guide/testing-your-assistant/#end-to-end-testing>`_
-include NLU data, so that both components of Rasa can be tested.  Create a file called
-``test_stories.md`` in the root directory with some test cases:
+can later be included as part of a :ref:`CI/CD system <setting-up-ci-cd>`. End-to-end :ref:`test conversations <end-to-end-testing>`
+include NLU data, so that both components of Rasa can be tested. The file
+``tests/conversation_tests.md`` contains example test conversations. Delete all the test conversations and replace
+them with some test conversations for your assistant so far:
 
 .. code-block:: md
 
@@ -185,7 +186,7 @@ To test our model against the test file, run the command:
 
 .. code-block:: bash
 
-   rasa test --e2e --stories test_stories.md
+   rasa test --stories tests/conversation_tests.md
 
 The test command will produce a directory named ``results``. It should contain a file
 called ``failed_stories.md``, where any test cases that failed will be printed. It will
@@ -635,8 +636,7 @@ use the ``from_text`` method to extract the users whole message:
 
 .. code-block:: python
 
-    def slot_mappings(self) -> Dict[Text: Union[Dict, List[Dict]]]:
-        # type: () -> Dict[Text: Union[Dict, List[Dict]]]
+    def slot_mappings(self) -> Dict[Text, Union[Dict, List[Dict[Text, Any]]]]:
         """A dictionary to map required slots to
         - an extracted entity
         - intent: value pairs

docs/user-guide/command-line-interface.rst

@@ -234,7 +234,14 @@ You can specify the training data, the fraction, and the output directory using
 
 
 This command will attempt to keep the proportions of intents the same in train and test.
+If you have NLG data for retrieval actions, this will be saved to seperate files:
 
+.. code-block:: bash
+
+   ls train_test_split
+
+         nlg_test_data.md     test_data.json
+         nlg_training_data.md training_data.json
 
 Convert Data Between Markdown and JSON
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/user-guide/connectors/twilio.rst

@@ -1,4 +1,4 @@
-:desc: Build a Rasa Chat Bot on Twilio
+:desc: Deploy a Rasa Open Source assistant through text message or WhatsApp via the Twilio connector
 
 .. _twilio:
 
@@ -7,12 +7,17 @@ Twilio
 
 .. edit-link::
 
-You first have to create a Twilio app to get credentials.
-Once you have them you can add these to your ``credentials.yml``.
+You can use the Twilio connector to deploy an assistant that is available over text message.
+
+.. contents::
+   :local:
 
 Getting Credentials
 ^^^^^^^^^^^^^^^^^^^
 
+You first have to create a Twilio app to get credentials.
+Once you have them you can add these to your ``credentials.yml``.
+
 **How to get the Twilio credentials:**
 You need to set up a Twilio account.
 
@@ -25,24 +30,29 @@ You need to set up a Twilio account.
   3. Now you can use the ``Account SID``, ``Auth Token``, and the phone
      number you purchased in your ``credentials.yml``.
 
-For more information, see the `Twilio REST API
-<https://www.twilio.com/docs/iam/api>`_.
+For more information, see the `Twilio REST API <https://www.twilio.com/docs/iam/api>`_.
 
-Using run script
-^^^^^^^^^^^^^^^^
 
-If you want to connect to the Twilio input channel using the run
-script, e.g. using:
+Connecting to WhatsApp
+----------------------
 
-.. code-block:: bash
+You can deploy a Rasa Open Source assistant to WhatsApp through Twilio. However, to do so, you have
+to have a `WhatsApp Business <https://www.whatsapp.com/business/>`_ profile. Associate
+your Whatsapp Business profile with the phone number you purchased through Twilio to
+access the `Twilio API for WhatsApp <https://www.twilio.com/whatsapp>`_.
 
-  rasa run
 
-you need to supply a ``credentials.yml`` with the following content:
+Applying the Credentials
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Add the Twilio credentials to your  ``credentials.yml``:
 
 .. code-block:: yaml
 
    twilio:
      account_sid: "ACbc2dxxxxxxxxxxxx19d54bdcd6e41186"
      auth_token: "e231c197493a7122d475b4xxxxxxxxxx"
      twilio_number: "+440123456789"
+
+Make sure to restart your Rasa Open Source server or container to make changes to
+which connectors are available.