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 | ||
| mynoop.py | ||
| mynoop.senpy | ||
| parameterized_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 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.cluster.gsi.dit.upm.es/senpy/plugin-example bbm