mirror of
https://github.com/gsi-upm/senpy
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.. | ||
sklearn | ||
async_plugin.py | ||
basic_analyse_entry_plugin.py | ||
basic_box_plugin.py | ||
basic_plugin.py | ||
basic.py | ||
configurable_plugin.py | ||
dummy_plugin.py | ||
dummy_required_plugin.py | ||
emorand_plugin.py | ||
mynoop.py | ||
mynoop.senpy | ||
parameterized_plugin.py | ||
rand_plugin.py | ||
README.md | ||
sleep_plugin.py |
This is a collection of plugins that exemplify certain aspects of plugin development with senpy.
The first series of plugins are the basic
ones.
Their starting point is a classification function defined in basic.py
.
They all include testing and running them as a script will run all tests.
In ascending order of customization, the plugins are:
- Basic is the simplest plugin of all. It leverages the
SentimentBox
Plugin class to create a plugin out of a classification method, andMappingMixin
to convert the labels from (pos
,neg
) to (marl:Positive
,marl:Negative
- Basic_box is just like the previous one, but replaces the mixin with a custom function.
- Basic_configurable is a version of
basic
with a configurable map of emojis for each sentiment. - Basic_parameterized like
basic_info
, but users set the map in each query (viaextra_parameters
). - Basic_analyse_entry uses the more general
analyse_entry
method and adds the annotations individually.
In rest of the plugins show advanced topics:
- mynoop: shows how to add a definition file with external requirements for a plugin. Doing this with a python-only module would require moving all imports of the requirements to their functions, which is considered bad practice.
- Async: a barebones example of training a plugin and analyzing data in parallel.
All of the plugins in this folder include a set of test cases and they are periodically tested with the latest version of senpy.
Additioanlly, for an example of stand-alone plugin that can be tested and deployed with docker, take a look at: lab.gsi.upm.es/senpy/plugin-example bbm