pychatl 1.2.5

Tiny DSL to generate training dataset for NLU engines

pychatl |travis| |coveralls| |pypi| |license|
=================================

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:target: https://travis-ci.org/atlassistant/pychatl

.. |coveralls| image:: https://coveralls.io/repos/github/atlassistant/pychatl/badge.svg?branch=master
:target: https://coveralls.io/github/atlassistant/pychatl?branch=master

.. |pypi| image:: https://badge.fury.io/py/pychatl.svg
:target: https://badge.fury.io/py/pychatl

.. |license| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
:target: https://www.gnu.org/licenses/gpl-3.0

Tiny DSL to generate training dataset for NLU engines. Based on the javascript implementation of `chatl <https://github.com/atlassistant/chatl>`_.

Installation
------------

pip
~~~

.. code-block:: bash

$ pip install pychatl

source
~~~~~~

.. code-block:: bash

$ git clone https://github.com/atlassistant/pychatl.git
$ cd pychatl
$ python setup.py install

or

.. code-block:: bash

$ pip install -e .

Usage
-----

From the terminal
~~~~~~~~~~~~~~~~~

.. code-block:: bash

$ pychatl .\example\forecast.dsl .\example\lights.dsl -a snips -o '{ \"language\": \"en\" }'

From the code
~~~~~~~~~~~~~

.. code-block:: python

from pychatl import parse

result = parse("""
# pychatl is really easy to understand.
#
# You can defines:
# - Intents
# - Entities (with or without variants)
# - Synonyms
# - Comments (only at the top level)

# Inside an intent, you got training data.
# Training data can refer to one or more entities and/or synonyms, they will be used
# by generators to generate all possible permutations and training samples.

%[my_intent]
~[greet] some training data @[date]
another training data that uses an @[entity] at @[date#with_variant]

~[greet]
hi
hello

# Entities contains available samples and could refer to a synonym.

@[entity]
some value
other value
~[a synonym]

# Synonyms contains only raw values

~[a synonym]
possible synonym
another one

# Entities and intents can define arbitrary properties that will be made available
# to generators.
# For snips, `snips:type`, `extensible` and `strictness` are used for example.

@[date](snips:type=snips/datetime)
tomorrow
today

# Variants is used only to generate training sample with specific values that should
# maps to the same entity name, here `date`. Props will be merged with the root entity.

@[date#with_variant]
the end of the day
nine o clock
twenty past five
""")

# Now you got a parsed dataset so you may want to process it for a specific NLU engines

from pychatl.postprocess import snips

snips_dataset = snips(result) # Or give options with `snips(result, language='en')`

# And now you got your dataset ready to be fitted within snips-nlu!

Adapters
--------

For now, only the `snips adapter <https://github.com/snipsco/snips-nlu>`_ has been done. Here is a list of adapters and their respective properties:

+-----------------+----------------------+
| adapter | snips |
+=================+======================+
| type (1) | ✔️ with `snips:type` |
+-----------------+----------------------+
| extensible (2) | ✔️ |
+-----------------+----------------------+
| strictness (3) | ✔️ |
+-----------------+----------------------+

1. Specific type of the entity to use (such as datetime, temperature and so on)
2. Are values outside of training samples allowed?
3. Parser threshold

Testing
-------

.. code-block:: bash

$ pip install -e .[test]
$ python -m nose --with-doctest -v --with-coverage --cover-package=pychatl