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https://github.com/gsi-upm/senpy
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This is still not functional, because it involves a LOT of changes to the basic structure of the project. Some of the main changes can be seen in the CHANGELOG.md file, if you're interested, but it boils down to simplifying the logic of plugins (no more activation/deactivation shenanigans), more robust typing and use of schemas (pydantic) to avoid inconsistencies and user errors. |
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| .. | ||
| moral | ||
| sklearn | ||
| 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
SentimentBoxPlugin class to create a plugin out of a classification method, andMappingMixinto 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
basicwith 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_entrymethod 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